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China D, MacLean LJ, Wei J, Theodore N, Johnson N, Crawford N, Ding K, Uneri A. Automated labeling using tracked ultrasound imaging: Application in tracking vertebrae during spine surgery. Med Image Anal 2025; 105:103672. [PMID: 40516484 DOI: 10.1016/j.media.2025.103672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2024] [Revised: 05/22/2025] [Accepted: 05/30/2025] [Indexed: 06/16/2025]
Abstract
PURPOSE Recent advancements in machine learning (ML) allow for rapid analysis of complex image data, which supports the use of ultrasound (US)-based solutions in interventional procedures. These solutions often require large, labeled datasets that can be time-consuming to curate and subject to inter- and intra-labeler variability. This work presents a practical method for automated labeling of US images by transferring labels from 3D diagnostic images (e.g., CT or MR) using tracked US imaging to support supervised training. The approach was applied to segmenting spinal vertebrae, and the quality of the generated labels was evaluated by registering individual vertebrae from US to CT images to account for potential spinal deformation during surgery. METHODS The proposed approach uses tracked US imaging to map target structures from CT volumes onto individual US frames. A dataset of spine images was created by scanning cadaveric torso specimens. Automated data cleaning methods were used to discard invalid frames, and data augmentations were applied to account for variability in image appearance. A simple U-Net model, called TernausNet, was trained for segmenting vertebrae using three labeling strategies: full vertebra (FV), posterior surface (PS), and weighted posterior surface (PSw). The labels were evaluated through vertebrae segmentation and registration of the resulting segmentations to corresponding CT structures, considering the impact of labeling strategy, calibration errors, and data cleaning. RESULTS The proposed labeling strategies yielded improved segmentation accuracy over the direct mapping of CT labels (viz. FV), yielding a median of 5.18 [4.24, 6.66] mm RMSD for PS and 3.86 [2.87, 5.60] mm for PSw labeling. The PSw approach was particularly effective in reducing hallucination artifacts in the acoustic shadow regions below the vertebral cortex. Using the resulting segmentations, registrations were solved with 1.56 [1.30, 1.62] mm TRE for PS and 1.52 [1.32, 2.38] mm for PSw labeling. Automated data cleaning and augmentation were found to significantly enhance the accuracy of bone feature segmentation and vertebra registration. CONCLUSIONS The study presents an automated labeling method for US imaging that supports the training of ML models by mapping 3D structures onto 2D US frames. The results highlight the importance of proper probe calibration, data cleaning, and specific labeling strategies in mitigating segmentation and registration errors. The work demonstrates the potential of real-time US imaging as a tool for precise anatomical tracking in surgery.
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Affiliation(s)
- Debarghya China
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Luke J MacLean
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jinchi Wei
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Nicholas Theodore
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States
| | | | | | - Kai Ding
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, MD, United States
| | - Ali Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
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Li F, Tao S, Ji M, Liu L, Qin Z, Yang X, Wu R, Zhan J. Dynamic AI Ultrasound-Assisted Diagnosis System to Reduce Unnecessary Fine Needle Aspiration of Thyroid Nodules. JOURNAL OF CLINICAL ULTRASOUND : JCU 2025. [PMID: 40348606 DOI: 10.1002/jcu.24035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2024] [Revised: 02/23/2025] [Accepted: 03/31/2025] [Indexed: 05/14/2025]
Abstract
OBJECTIVES This study aims to compare the diagnostic efficiency of the American College of Radiology-Thyroid Imaging, Reporting, and Data System (ACR-TIRADS), fine-needle aspiration (FNA) cytopathology alone, and the dynamic artificial intelligence (AI) diagnostic system. MATERIALS AND METHODS A total of 1035 patients from three hospitals were included in the study. Of these, 590 were from the retrospective dataset and 445 cases were from the prospective dataset. The diagnostic accuracy of the dynamic AI system in the thyroid nodules was evaluated in comparison to the gold standard of postoperative pathology. The sensitivity, specificity, ROC, and diagnostic differences in the κ-factor relative to the gold standard were analyzed for the AI system and the FNA. RESULTS The dynamic AI diagnostic system showed good diagnostic stability in different ages and sexes and nodules of different sizes. The diagnostic AUC of the dynamic AI system showed a significant improvement from 0.89 to 0.93 compared to ACR TI-RADS. Compared to that of FNA cytopathology, the diagnostic efficacy of the dynamic AI system was found to be no statistical difference in both the retrospective cohort and the prospective cohort. CONCLUSION The dynamic AI diagnostic system enhances the accuracy of ACR TI-RADS-based diagnoses and has the potential to replace biopsies, thus reducing the necessity for invasive procedures in patients.
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Affiliation(s)
- Fang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuang Tao
- Department of Breast Surgery, Wujin Hospital Affiliated With Jiangsu University, Wujin, China
| | - Ma Ji
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Long Liu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ziwei Qin
- Department of Ultrasound, Xuzhou Centeral Hospital of Bengbu Medical College, Xuzhou, China
| | - Xueting Yang
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Zhan
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Imaizumi M, Sobue T, Taniguchi N, Miyagawa M, Momose T, Yoshinaga S, Yamamoto S, Ohishi W, Kitamura H, Zha L, Okubo T. Protocol and Progress of a Thyroid Study in the Epidemiological Study of Health Effects in Fukushima Emergency Workers. Disaster Med Public Health Prep 2025; 19:e108. [PMID: 40276923 DOI: 10.1017/dmp.2025.103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2025]
Abstract
OBJECTIVE To describe the protocol and progress of a thyroid study using thyroid ultrasonography in emergency workers who responded to the Fukushima nuclear accident. METHODS Thyroid ultrasonography was performed on Fukushima emergency workers at over 60 health examination institutions. The accuracy of ultrasonography is controlled by standard procedural protocols, examiner training, and a central review system. Thyroid findings are classified into 4 categories: Category A1 (no nodule or cyst), Category A2 (nodules ≤ 5.0 mm and/or cysts ≤ 20.0 mm), Category B (nodules ≥ 5.1 mm and/or cysts ≥ 20.1 mm), and Category C (requires immediate further examination). Participants classified as Categories B or C are recommended for secondary examination. RESULTS Among 3398 participants with available ultrasound images obtained at the first health examination between January 2016 and October 2023, 45.2 % were classified as Category A1, 39.2 % as Category A2, 15.5 % as Category B, and 0 % as Category C. Of the 207 participants for whom secondary examination results were available, seven were diagnosed with cancer or suspected cancer. CONCLUSIONS An accuracy control system of thyroid ultrasonography has been established which will continue to carefully investigate the thyroids of Fukushima emergency workers.
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Affiliation(s)
- Misa Imaizumi
- Department of Clinical Studies, Radiation Effects Research Foundation, Nagasaki and Hiroshima, Japan
| | | | - Nobuyuki Taniguchi
- Department of Clinical Laboratory Medicine, Jichi Medical University, Tochigi, Japan
| | - Megumi Miyagawa
- Department of Endocrinology and Metabolism, Toranomon Hospital, Tokyo, Japan
- Internal Medicine, Miyagawa Hospital, Kanagawa, Japan
| | - Takumaro Momose
- Fukushima Research and Engineering Institute, Japan Atomic Energy Agency, Fukushima, Japan
| | - Shinji Yoshinaga
- Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan
| | - Sayaka Yamamoto
- Department of Clinical Laboratory Medicine, Jichi Medical University, Tochigi, Japan
| | - Waka Ohishi
- Department of Clinical Studies, Radiation Effects Research Foundation, Nagasaki and Hiroshima, Japan
| | - Hiroko Kitamura
- Occupational Health Training Center, University of Occupational and Environmental Health, Japan, Fukuoka, Japan
- Department of Radiation Regulatory Science Research, National Institute of Radiological Sciences, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Ling Zha
- Division of Environmental Medicine and Population Sciences, Department of Social Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Toshiteru Okubo
- Research Center for Prevention from Radiation Hazards of Workers, National Institute of Occupational Safety and Health, Kanagawa, Japan
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Lee JT, Siu AKY, Ma EPM. Interpreting Pediatric Laryngeal Ultrasonography: A Training Protocol for Novice Examiners. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2025; 68:935-948. [PMID: 39913264 DOI: 10.1044/2024_jslhr-24-00367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
OBJECTIVE Laryngeal ultrasonography (LUS) is a noninvasive alternative to nasal endoscopy for diagnosing vocal fold pathologies in the pediatric population. Inducing less discomfort and physiological impact, LUS is more well tolerated by young patients. Despite its advantages, interpreting ultrasound images is highly subjective, potentially undermining diagnostic accuracy. To address the limitation, this research aims to evaluate the effect of training on novice examiners' LUS interpretation proficiency and, secondly, whether examiners' interpretation confidence increases after receiving the training. METHOD Thirty-eight novice examiners were randomly assigned to the experimental and control group where the former received training. A stimulus-response-feedback-stimulus paradigm was employed in the training. Qualitatively, the presence of vocal fold lesions and vocal fold motion impairment was examined. Quantitatively, the left and right vocal fold-arytenoid angles were measured. RESULTS Results showed that training significantly improved diagnostic accuracy in qualitative measurements. Quantitatively, statistically significant effects were found posttraining with enhanced intrarater agreement and reduced interrater variability. A substantial increase in interpretation confidence was observed following training. CONCLUSIONS In conclusion, there is an overall significant training effect on novice examiners' proficiency in LUS image interpretation. For future directions, it is recommended to investigate the training effect on the proficiency from ultrasound image acquisition to interpretation.
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Affiliation(s)
- Julianne T Lee
- Voice Research Laboratory, Unit of Human Communication, Learning, and Development, Faculty of Education, The University of Hong Kong, Pok Fu Lam
| | - Alice K-Y Siu
- Faculty of Medicine, The Chinese University of Hong Kong, Shenzhen, China
| | - Estella P-M Ma
- Voice Research Laboratory, Unit of Human Communication, Learning, and Development, Faculty of Education, The University of Hong Kong, Pok Fu Lam
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Bell C, White SL, Tylee T, Dighe M, Greca AL, Goldner W, Mayson S, Haugen BR, Pozdeyev N. Thyroid Nodule Sphericity Metrics Discriminate Benign and Malignant Follicular and Oncocytic Neoplasms. Thyroid 2025; 35:291-297. [PMID: 39992747 DOI: 10.1089/thy.2024.0670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/26/2025]
Abstract
Background: We investigated if thyroid nodule taller-than-wide (TTW) feature and sphericity metrics are helpful in separating benign neoplastic thyroid nodules (follicular and oncocytic adenomas) from follicular thyroid carcinomas (FTC) and oncocytic thyroid carcinomas (OCA). Methods: This is a retrospective study of TTW sonographic feature as reported by radiologists and nodule sphericity metrics at two academic health systems. Surgical pathology reports for benign and malignant follicular and oncocytic neoplasms, non-neoplastic nodules (hyperplastic and adenomatoid), and classic papillary thyroid cancers (PTC) were extracted from enterprise data warehouses. We independently reviewed each ultrasound and recorded nodule dimensions to identify nodules that were TTW and determine if the proportion of TTW nodules is different in benign and malignant thyroid nodules of various histologies. We also evaluated the sphericity index and sphericity ratio, two quantitative measures of how close the 3D shape of the nodule is to a sphere. Results: In total, 1110 nodules were analyzed: 209 non-neoplastic nodules (hyperplastic and adenomatoid), 398 benign neoplasms (follicular and oncocytic adenomas), and 503 malignant neoplasms (FTC, OCA, PTC, and follicular variant-PTC [FV-PTC]) and noninvasive follicular thyroid neoplasm with papillary-like features (NIFTP). There was no statistical difference for the TTW feature when follicular and oncocytic adenomas were compared with FV-PTC, OCA, FTC, and NIFTP (χ2, p = 0.08, sensitivity of 28% [confidence interval 23-24%]), when follicular adenoma was compared with FTC (χ2, p = 0.64) or when oncocytic adenoma was compared with OCA (χ2, p = 0.08). Benign and malignant neoplasms were more likely than non-neoplastic nodules to be TTW (χ2, p = 0.02). In contrast, the sphericity index and sphericity ratio were significantly different in most comparisons of benign and malignant nodules (Wilcoxon, p < 0.03) except for oncocytic tumors. Conclusions: TTW shape has limited utility in distinguishing benign follicular and oncocytic neoplasms from malignancy. This sonographic feature was more common among all benign and malignant neoplasms when they were compared as a group to non-neoplastic nodules, which suggests this sonographic feature is an indicator of neoplastic growth (benign or malignant) but not cancer. Alternative methods, such as measures of sphericity, are needed to distinguish benign and malignant oncocytic and follicular neoplasms.
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Affiliation(s)
- Caitlin Bell
- Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Samantha L White
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Tracy Tylee
- Division of Endocrinology, Diabetes & Metabolism, University of Washington, Seattle, Washington, USA
| | - Manjiri Dighe
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Amanda La Greca
- Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Whitney Goldner
- Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Sarah Mayson
- Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Bryan R Haugen
- Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Nikita Pozdeyev
- Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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Cozzi JL, Li H, Fuhrman JD, Lan L, Williams J, Finnerty B, Fahey TJ, Tumati A, Genender J, Keutgen XM, Giger ML. Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound. Int J Comput Assist Radiol Surg 2025; 20:259-267. [PMID: 39751996 DOI: 10.1007/s11548-024-03294-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 11/12/2024] [Indexed: 01/04/2025]
Abstract
PURPOSE Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and can also be used in the development of machine learning nodule diagnosis systems. This paper presents the development, validation, and multi-institutional independent testing of a machine learning system for the automatic segmentation of thyroid nodules on ultrasound. METHODS The datasets, containing a total of 1595 thyroid ultrasound images from 520 patients with thyroid nodules, were retrospectively collected under IRB approval from University of Chicago Medicine (UCM) and Weill Cornell Medical Center (WCMC). Nodules were manually contoured by a team of UCM and WCMC physicians for ground truth. An AttU-Net, a U-Net architecture with additional attention weighting functions, was trained for the segmentations. The algorithm was validated through fivefold cross-validation by nodule and was tested on two independent test sets: one from UCM and one from WCMC. Dice similarity coefficient (DSC) and percent Hausdorff distance (%HD), Hausdorff distance reported as a percent of the nodule's effective diameter, served as the performance metrics. RESULTS On multi-institutional independent testing, the AttU-Net yielded average DSCs (std. deviation) of 0.915 (0.04) and 0.922 (0.03) and %HDs (std. deviation) of 12.9% (4.6) and 13.4% (6.3) on the UCM and WCMC test sets, respectively. Similarity testing showed the algorithm's performance on the two institutional test sets was equivalent up to margins of Δ DSC ≤ 0.013 and Δ %HD ≤ 1.73%. CONCLUSIONS This work presents a robust automatic thyroid nodule segmentation algorithm that could be implemented for risk stratification systems. Future work is merited to incorporate this segmentation method within an automatic thyroid classification system.
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Affiliation(s)
- Joseph L Cozzi
- Department of Radiology, University of Chicago, Chicago, IL, USA.
| | - Hui Li
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Jordan D Fuhrman
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Li Lan
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Jelani Williams
- Division of General Surgery and Surgical Oncology, Department of Surgery, University of Chicago Medicine, Endocrine Surgery Research Program, Chicago, IL, USA
| | - Brendan Finnerty
- Endocrine Oncology Research Program, Division of Endocrine Surgery, Department of Surgery, New York Presbyterian Hospital-Weill Cornell Medicine, New York, USA
| | - Thomas J Fahey
- Endocrine Oncology Research Program, Division of Endocrine Surgery, Department of Surgery, New York Presbyterian Hospital-Weill Cornell Medicine, New York, USA
| | - Abhinay Tumati
- Endocrine Oncology Research Program, Division of Endocrine Surgery, Department of Surgery, New York Presbyterian Hospital-Weill Cornell Medicine, New York, USA
| | - Joshua Genender
- Department of Radiology, University of Chicago, Chicago, IL, USA
- David Geffen School of Medicine, University of California - Los Angeles, Los Angeles, CA, USA
| | - Xavier M Keutgen
- Division of General Surgery and Surgical Oncology, Department of Surgery, University of Chicago Medicine, Endocrine Surgery Research Program, Chicago, IL, USA
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Sayiner ZA, Munis HS, Köroğlu İ, Akyol EMB, Zengin O, Akarsu E. Tumor necrosis factor-alpha inhibitors may increase the need for fine-needle aspiration cytology in axial spondyloarthritis patients. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2024; 70:e20240871. [PMID: 39630724 PMCID: PMC11639510 DOI: 10.1590/1806-9282.20240871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 09/04/2024] [Indexed: 12/07/2024]
Abstract
OBJECTIVE Tumor necrosis factor alpha inhibitors are frequently used in the treatment of axial spondyloarthritis. While tumor necrosis factor alpha is associated with some malignancies, studies on its effects on thyroid functions and thyroid nodules are limited. This study aimed to assess the effect of tumor necrosis factor alpha inhibitors treatments on the evaluation of thyroid nodules and thyroid function tests in axial spondyloarthritis patients. METHODS A total of 106 patients with axial spondyloarthritis, 48 receiving nonsteroidal antiinflammatory drugs and 58 receiving tumor necrosis factor alpha inhibitors, were included in the study. All cases were screened by ultrasound for the presence of nodules, European Thyroid Association - Thyroid Imaging Reporting and Data System classification category, thyroid volume, and thyroid function tests. RESULTS The prevalence of patients with multiple nodules in the tumor necrosis factor alpha inhibitors treatment group was significantly higher compared to the nonsteroidal antiinflammatory drug group (68.2 vs. 38.1%, p=0.033). Furthermore, the prevalence of nodules measuring ≥1 cm in the tumor necrosis factor alpha inhibitors treatment group was higher compared to the only nonsteroidal antiinflammatory drug treatment group (56 vs. 19.05%, p=0.011). In the tumor necrosis factor alpha inhibitors treatment group, the proportion of patients with nodules requiring fine-needle aspiration cytology was higher compared to the nonsteroidal antiinflammatory drug group (34.5 vs. 16.7%, p=0.038). No significant difference was found between thyroid-stimulating hormone and free thyroxine values before and during treatment in both groups (p>0.005). CONCLUSION The number of nodules requiring fine-needle aspiration cytology according to the European Thyroid Association - Thyroid Imaging Reporting and Data System classification seems higher in nonsteroidal antiinflammatory drugs users than Tumor necrosis factor-alpha inhibitor users. Therefore, it may be rational to examine the thyroid gland while administering Tumor necrosis factor-alpha inhibitor treatment in axial spondyloarthritis patients.
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Affiliation(s)
- Zeynel Abidin Sayiner
- Gaziantep University, School of Medicine, Department of Endocrinology and Metabolism – Gaziantep, Turkey
| | - Hayriye Sultan Munis
- Gaziantep University, School of Medicine, Department of Internal Medicine – Gaziantep, Turkey
| | - İpek Köroğlu
- Gaziantep University, School of Medicine, Department of Endocrinology and Metabolism – Gaziantep, Turkey
| | - Elif Melis Baloğlu Akyol
- Gaziantep University, School of Medicine, Department of Endocrinology and Metabolism – Gaziantep, Turkey
| | - Orhan Zengin
- Gaziantep University, School of Medicine, Department of Rheumatology – Gaziantep, Turkey
| | - Ersin Akarsu
- Gaziantep University, School of Medicine, Department of Endocrinology and Metabolism – Gaziantep, Turkey
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Lee JY, Lee MK, Lim HK, Lee CY, Sung JY, Yoon JH, Hahn SY, Shin JH, Kim JH, Jung SL, Chung SR, Baek JH, Na DG, Korean Society of Thyroid Radiology (KSThR), Korean Society of Radiology. Standardized Ultrasound Evaluation for Active Surveillance of Low-Risk Thyroid Microcarcinoma in Adults: 2024 Korean Society of Thyroid Radiology Consensus Statement. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2024; 85:1060-1082. [PMID: 39660311 PMCID: PMC11625847 DOI: 10.3348/jksr.2024.0132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Revised: 09/09/2024] [Accepted: 09/11/2024] [Indexed: 12/12/2024]
Abstract
Active surveillance (AS) has been widely adopted as an alternative to immediate surgery owing to the indolent nature and favorable outcomes of papillary thyroid microcarcinoma (PTMC). AS is generally recommended for tumors measuring ≤1 cm without aggressive cytological subtypes, risk of gross extrathyroidal extension (ETE), lymph node metastasis (LNM), or distant metastasis. AS requires careful patient selection based on various patient and tumor characteristics, and ultrasound (US) findings. Moreover, during AS, regular US is performed to monitor any signs of tumor progression, including tumor growth, new US features of potential gross ETE, and LNM. Therefore, appropriate imaging-based assessment plays a crucial role in determining whether AS or surgery should be pursued. However, detailed recommendations concerning US evaluation are currently insufficient, necessitating the formulation of this guideline. The Korean Society of Thyroid Radiology has developed a consensus statement for low-risk PTMC, covering US assessment methods when considering AS as a management option and conducting follow-up imaging tests during AS. This guideline aims to provide optimal scientific evidence and expert opinion consensus regarding a standardized US-based assessment protocol for low-risk PTMC.
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Lee JY, Lee MK, Lim HK, Lee CY, Sung JY, Yoon JH, Han SY, Shin JH, Kim JH, Jung SL, Chung SR, Baek JH, Na DG. Standardized Ultrasound Evaluation for Active Surveillance of Low-Risk Thyroid Microcarcinoma in Adults: 2024 Korean Society of Thyroid Radiology Consensus Statement. Korean J Radiol 2024; 25:942-958. [PMID: 39473087 PMCID: PMC11524690 DOI: 10.3348/kjr.2024.0871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Revised: 09/09/2024] [Accepted: 09/11/2024] [Indexed: 11/02/2024] Open
Abstract
Active surveillance (AS) has been widely adopted as an alternative to immediate surgery owing to the indolent nature and favorable outcomes of papillary thyroid microcarcinoma (PTMC). AS is generally recommended for tumors measuring ≤1 cm without aggressive cytological subtypes, risk of gross extrathyroidal extension (ETE), lymph node metastasis (LNM), or distant metastasis. AS requires careful patient selection based on various patient and tumor characteristics, and ultrasound (US) findings. Moreover, during AS, regular US is performed to monitor any signs of tumor progression, including tumor growth, new US features of potential gross ETE, and LNM. Therefore, appropriate imaging-based assessment plays a crucial role in determining whether AS or surgery should be pursued. However, detailed recommendations concerning US evaluation are currently insufficient, necessitating the formulation of this guideline. The Korean Society of Thyroid Radiology has developed a consensus statement for low-risk PTMC, covering US assessment methods when considering AS as a management option and conducting follow-up imaging tests during AS. This guideline aims to provide optimal scientific evidence and expert opinion consensus regarding a standardized US-based assessment protocol for low-risk PTMC.
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Affiliation(s)
- Ji Ye Lee
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Min Kyoung Lee
- Department of Radiology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyun Kyung Lim
- Department of Radiology, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Chang Yoon Lee
- Department of Radiology, Research Institute and Hospital, National Cancer Center, Goyang, Republic of Korea
| | - Jin Yong Sung
- Department of Radiology and Thyroid Center, Daerim St. Mary's Hospital, Seoul, Republic of Korea
| | - Jung Hyun Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Soo Yeon Han
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jung Hee Shin
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ji-Hoon Kim
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - So Lyung Jung
- Department of Radiology, St. Vincent's Hospital, The Catholic University of Korea, Suwon, Republic of Korea
| | - Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Dong Gyu Na
- Department of Radiology, Gangneung Asan Hospital, Ulsan University College of Medicine, Gangneung, Republic of Korea.
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Ghai S, Goldstein DP, Sawka AM. Ultrasound Imaging in Active Surveillance of Small, Low-Risk Papillary Thyroid Cancer. Korean J Radiol 2024; 25:749-755. [PMID: 39028013 PMCID: PMC11306002 DOI: 10.3348/kjr.2024.0148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 05/09/2024] [Accepted: 05/11/2024] [Indexed: 07/20/2024] Open
Abstract
The recent surge in the incidence of small papillary thyroid cancers (PTCs) has been linked to the widespread use of ultrasonography, thereby prompting concerns regarding overdiagnosis. Active surveillance (AS) has emerged as a less invasive alternative management strategy for low-risk PTCs, especially for PTCs measuring ≤1 cm in maximal diameter. Recent studies report low disease progression rates of low-risk PTCs ≤1 cm under AS. Ongoing research is currently exploring the feasibility of AS for larger PTCs (<20 mm). AS protocols include meticulous ultrasound assessment, emphasis on standardized techniques, and a multidisciplinary approach; they involve monitoring the nodules for size, growth, potential extrathyroidal extension, proximity to the trachea and recurrent laryngeal nerve, and potential cervical nodal metastases. The criteria for progression, often defined as an increase in the maximum diameter of the PTC, warrant a review of precision and ongoing examinations. Challenges exist regarding the reliability of volume measurements for defining PTC disease progression. Although ultrasonography plays a pivotal role, challenges in assessing progression and minor extrathyroidal extension underscore the importance of a multidisciplinary approach in disease management. This comprehensive overview highlights the evolving landscape of AS for PTCs, emphasizing the need for standardized protocols, meticulous assessments, and ongoing research to inform decision-making.
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Affiliation(s)
- Sangeet Ghai
- Joint Department of Medical Imaging, University Health Network-Mount Sinai Hospital-Women's College Hospital, University of Toronto, Toronto, Canada.
| | - David P Goldstein
- Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Anna M Sawka
- Division of Endocrinology, Department of Medicine, University Health Network and University of Toronto, Toronto, Canada
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11
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Zhang M, Yang Y, Dong R, Wang L, Sun Y, Li Y, Wang Z, Xu R, Yang W, Jin L, Huang J, Yu N, Long X. Deciphering Depressor Anguli Oris for Lower Face Rejuvenation: A Prospective Ultrasound-based Investigation. Aesthet Surg J 2024; 44:880-888. [PMID: 38377399 DOI: 10.1093/asj/sjae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND The depressor anguli oris muscle (DAO) is a pivotal treatment target when creating a harmonic jawline. However, evidence of its live morphology remains scarce. OBJECTIVES In this study we aimed to reevaluate the DAO with a facile ultrasound analysis and thereby guide safer and more effective botulinum toxin type A (BTX-A) injection. METHODS A prospective ultrasound assessment was conducted in 41 patients. Morphology of the DAO and its relative position to neighboring structures were appraised at the ubiquitous facial landmark, the labiomandibular fold (LMF). Three-dimensional images were captured before and after the patient received the BTX-A injection based on sonographic evidence. RESULTS The skin-to-muscle depths of the DAO on average (measured from the medial to lateral border) were 5.26, 5.61, and 8.42 mm. The DAO becomes thinner and wider from zone 1 to zone 3 (P < .001). Overlapping lengths of the DAO and the depressor labii inferioris increased from zone 1 to zone 3: 4.74, 9.68, 14.54 mm (P < .001). The medial border of the DAO was located at 4.33, 6.12, 8.90 mm medial to the LMF (zone 1-3), and no muscle fibers of the DAO were observed in zone 1 or zone 2 in nearly one-third of patients. Improvement of the mouth corner downturn angle upon receiving BTX-A injection at zones 2 and 3 were 88.3%, 32.3%, and 14.7% for the neutral, maximum smile, and down-turning mouth corner expressions. CONCLUSIONS This work established an informative ultrasound portrait of the DAO and structures in the perioral region, which suggested the LMF as a convenient landmark for locating the DAO. Injection at the middle and lower thirds of the LMF at a 4- to 5-mm depth is recommended.
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12
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Lee KC, Kim JK, Kim DK. Comparison of the Size Measurement of Gallbladder Polyps by Three Different Radiologists in Abdominal Ultrasonography. Tomography 2024; 10:1031-1041. [PMID: 39058049 PMCID: PMC11281002 DOI: 10.3390/tomography10070077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND There is little information regarding the size measurement differences in gallbladder (GB) polyps performed by different radiologists on abdominal ultrasonography (US). AIM To reveal the differences in GB polyp size measurements performed by different radiologists on abdominal US. METHODS From June to September 2022, the maximum diameter of 228 GB polyps was measured twice on abdominal US by one of three radiologists (a third-year radiology resident [reader A], a radiologist with 7 years of experience in abdominal US [reader B], and an abdominal radiologist with 8 years of experience in abdominal US [reader C]). Intra-reader agreements for polyp size measurements were assessed by intraclass correlation coefficient (ICC). A Bland-Altman plot was used to visualize the differences between the first and second size measurements in each reader. RESULTS Reader A, reader B, and reader C evaluated 65, 77, and 86 polyps, respectively. The mean size of measured 228 GB polyps was 5.0 ± 1.9 mm. Except for the case where reader A showed moderate intra-reader agreement (0.726) for polyps with size ≤ 5 mm, all readers showed an overall high intra-reader reliability (reader A, ICC = 0.859; reader B, ICC = 0.947, reader C, ICC = 0.948), indicative of good and excellent intra-reader agreements. The 95% limit of agreement of reader A, B, and C was 1.9 mm of the mean in all three readers. CONCLUSIONS GB polyp size measurement on abdominal US showed good or excellent intra-reader agreements. However, size changes of approximately less than 1.9 mm should be interpreted carefully because these may be within the measurement error.
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Affiliation(s)
- Kyu-Chong Lee
- Department of Radiology, Armed Forces Capital Hospital, Seongnam 13574, Republic of Korea; (K.-C.L.); (J.-K.K.)
- Department of Radiology, Korea University Anam Hospital, 73 Geryeodae-ro, Seongbuk-Gu, Seoul 02841, Republic of Korea
| | - Jin-Kyem Kim
- Department of Radiology, Armed Forces Capital Hospital, Seongnam 13574, Republic of Korea; (K.-C.L.); (J.-K.K.)
| | - Dong-Kyu Kim
- Department of Radiology, Armed Forces Capital Hospital, Seongnam 13574, Republic of Korea; (K.-C.L.); (J.-K.K.)
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul 03722, Republic of Korea
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13
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Zhao HN, Yin H, Liu JY, Song LL, Peng YL, Ma BY. Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients. Front Oncol 2024; 14:1204987. [PMID: 38390270 PMCID: PMC10881794 DOI: 10.3389/fonc.2024.1204987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 01/09/2024] [Indexed: 02/24/2024] Open
Abstract
Objective This study aimed to develop a deep learning system to identify and differentiate the metastatic cervical lymph nodes (CLNs) of thyroid cancer. Methods From January 2014 to December 2020, 3059 consecutive patients with suspected with metastatic CLNs of thyroid cancer were retrospectively enrolled in this study. All CLNs were confirmed by fine needle aspiration. The patients were randomly divided into the training (1228 benign and 1284 metastatic CLNs) and test (307 benign and 240 metastatic CLNs) groups. Grayscale ultrasonic images were used to develop and test the performance of the Y-Net deep learning model. We used the Y-Net network model to segment and differentiate the lymph nodes. The Dice coefficient was used to evaluate the segmentation efficiency. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the classification efficiency. Results In the test set, the median Dice coefficient was 0.832. The sensitivity, specificity, accuracy, PPV, and NPV were 57.25%, 87.08%, 72.03%, 81.87%, and 66.67%, respectively. We also used the Y-Net classified branch to evaluate the classification efficiency of the LNs ultrasonic images. The classification branch model had sensitivity, specificity, accuracy, PPV, and NPV of 84.78%, 80.23%, 82.45%, 79.35%, and 85.61%, respectively. For the original ultrasonic reports, the sensitivity, specificity, accuracy, PPV, and NPV were 95.14%, 34.3%, 64.66%, 59.02%, 87.71%, respectively. The Y-Net model yielded better accuracy than the original ultrasonic reports. Conclusion The Y-Net model can be useful in assisting sonographers to improve the accuracy of the classification of ultrasound images of metastatic CLNs.
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Affiliation(s)
- Hai Na Zhao
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hao Yin
- Computer science of Sichuan University, Chengdu, Sichuan, China
| | - Jing Yan Liu
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
| | - Lin Lin Song
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yu Lan Peng
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
| | - Bu Yun Ma
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
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14
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Yan L, Ren L, Li Y, Luo Y. Inter-observer variation in two-dimensional and three-dimensional ultrasound measurement of papillary thyroid microcarcinoma. Cancer Imaging 2023; 23:94. [PMID: 37798807 PMCID: PMC10557328 DOI: 10.1186/s40644-023-00613-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/17/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUNDS The reliable ultrasound (US) measurements of papillary thyroid microcarcinoma (PTMC) are very important during active surveillance. This prospective study was design to investigate the inter-observer reliability and agreement of two- dimensional ultrasound(2DUS) and three-dimensional ultrasound(3DUS) in the measurement of maximum diameter and volume for PTMC. METHODS This prospective study included 51 consecutive patients with solitary PTMC confirmed by biopsy. Two independent observers performed measurements of each tumor using a standardized measurement protocol. The maximum diameter was the largest one of the three diameters measured on the largest transverse and longitudinal 2DUS images. 2DUS volume was calculated using ellipsoid formula method. The virtual organ computer aided analysis(VOCAL) was used to determine 3DUS volume. The inter-observer reliability was assessed using intraclass correlation coefficient(ICC) with 95% confidence intervals(CIs). Bland-Altman analysis was used to evaluate agreement, and expressed as a bias with 95% limits of agreement(LOA). RESULTS The maximum diameter was 0.78 ± 0.14 cm. Volume measured by 3DUS was significantly smaller than that by 2DUS(0.163 ± 0.074 cm3 vs. 0.175 ± 0.078 cm3, P = 0.005). The ICCs of inter-observer reliability of maximum diameter, 2DUS volume and 3DUS volume was 0.922(0.864-0.955), 0.928(0.874-0.959), and 0.974(0.955-0.985), respectively. The ICCs of 2DUS and 3DUS volume was 0.955(0.909-0.976). The inter-observer agreement of maximum diameter, 2DUS volume and 3DUS volume was 1.096(0.7322 to 1.459), 1.008(0.5802-1.435), and 1.011(0.7576-1.265), respectively. The inter-observer agreement of 2DUS and 3DUS volume was 1.096(0.7322 to 1.459). CONCLUSION Maximum diameter had the lowest degree of observer variation among all the measurements. Volume measured by 3DUS had lower variability and higher repeatability than that by 2DUS, which might be helpful to provide more reliable estimates of tumor size for PTMC.
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Affiliation(s)
- Lin Yan
- Department of Ultrasound, the First Medical Centre, Chinese PLA General Hospital, No.28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Ling Ren
- Department of Ultrasound, the First Medical Centre, Chinese PLA General Hospital, No.28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Yingying Li
- Department of Ultrasound, the First Medical Centre, Chinese PLA General Hospital, No.28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Yukun Luo
- Department of Ultrasound, the First Medical Centre, Chinese PLA General Hospital, No.28 Fuxing Road, Haidian District, Beijing, 100853, China.
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15
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Nagendra L, Pappachan JM, Fernandez CJ. Artificial intelligence in the diagnosis of thyroid cancer: Recent advances and future directions. Artif Intell Cancer 2023; 4:1-10. [DOI: 10.35713/aic.v4.i1.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/24/2023] [Accepted: 08/07/2023] [Indexed: 09/07/2023] Open
Abstract
The diagnosis and management of thyroid cancer is fraught with challenges despite the advent of innovative diagnostic, surgical, and chemotherapeutic modalities. Challenges like inaccuracy in prognostication, uncertainty in cytopathological diagnosis, trouble in differentiating follicular neoplasms, intra-observer and inter-observer variability on ultrasound imaging preclude personalised treatment in thyroid cancer. Artificial intelligence (AI) is bringing a paradigm shift to the healthcare, powered by quick advancement of the analytic techniques. Several recent studies have shown remarkable progress in thyroid cancer diagnostics based on AI-assisted algorithms. Application of AI techniques in thyroid ultrasonography and cytopathology have shown remarkable impro-vement in sensitivity and specificity over the traditional diagnostic modalities. AI has also been explored in the development of treatment algorithms for indeterminate nodules and for prognostication in the patients with thyroid cancer. The benefits of high repeatability and straightforward implementation of AI in the management of thyroid cancer suggest that it holds promise for clinical application. Limited clinical experience and lack of prospective validation studies remain the biggest drawbacks. Developing verified and trustworthy algorithms after extensive testing and validation using prospective, multi-centre trials is crucial for the future use of AI in the pipeline of precision medicine in the management of thyroid cancer.
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Affiliation(s)
- Lakshmi Nagendra
- Department of Endocrinology, JSS Medical College & JSS Academy of Higher Education and Research Center, Mysore 570015, India
| | - Joseph M Pappachan
- Department of Endocrinology & Metabolism, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, United Kingdom
- Faculty of Science, Manchester Metropolitan University, Manchester M15 6BH, M15 6BH, United Kingdom
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Cornelius James Fernandez
- Department of Endocrinology & Metabolism, Pilgrim Hospital, United Lincolnshire Hospitals NHS Trust, PE21 9QS PE21 9QS, United Kingdom
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16
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Gomes Ataide EJ, Jabaraj MS, Schenke S, Petersen M, Haghghi S, Wuestemann J, Illanes A, Friebe M, Kreissl MC. Thyroid Nodule Detection and Region Estimation in Ultrasound Images: A Comparison between Physicians and an Automated Decision Support System Approach. Diagnostics (Basel) 2023; 13:2873. [PMID: 37761240 PMCID: PMC10529523 DOI: 10.3390/diagnostics13182873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/27/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Thyroid nodules are very common. In most cases, they are benign, but they can be malignant in a low percentage of cases. The accurate assessment of these nodules is critical to choosing the next diagnostic steps and potential treatment. Ultrasound (US) imaging, the primary modality for assessing these nodules, can lack objectivity due to varying expertise among physicians. This leads to observer variability, potentially affecting patient outcomes. PURPOSE This study aims to assess the potential of a Decision Support System (DSS) in reducing these variabilities for thyroid nodule detection and region estimation using US images, particularly in lesser experienced physicians. METHODS Three physicians with varying levels of experience evaluated thyroid nodules on US images, focusing on nodule detection and estimating cystic and solid regions. The outcomes were compared to those obtained from a DSS for comparison. Metrics such as classification match percentage and variance percentage were used to quantify differences. RESULTS Notable disparities exist between physician evaluations and the DSS assessments: the overall classification match percentage was just 19.2%. Individually, Physicians 1, 2, and 3 had match percentages of 57.6%, 42.3%, and 46.1% with the DSS, respectively. Variances in assessments highlight the subjectivity and observer variability based on physician experience levels. CONCLUSIONS The evident variability among physician evaluations underscores the need for supplementary decision-making tools. Given its consistency, the CAD offers potential as a reliable "second opinion" tool, minimizing human-induced variabilities in the critical diagnostic process of thyroid nodules using US images. Future integration of such systems could bolster diagnostic precision and improve patient outcomes.
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Affiliation(s)
- Elmer Jeto Gomes Ataide
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, 39120 Magdeburg, Germany; (S.S.); (M.C.K.)
| | | | - Simone Schenke
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, 39120 Magdeburg, Germany; (S.S.); (M.C.K.)
- Department of Nuclear Medicine, Klinikum Bayreuth, 95445 Bayreuth, Germany
| | - Manuela Petersen
- Department of General, Visceral, Vascular and Transplant Surgery, University Hospital Magdeburg, 39120 Magdeburg, Germany
| | - Sarvar Haghghi
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, 39120 Magdeburg, Germany; (S.S.); (M.C.K.)
- Department of Nuclear Medicine, University Hospital Frankfurt, 60590 Frankfurt, Germany
| | - Jan Wuestemann
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, 39120 Magdeburg, Germany; (S.S.); (M.C.K.)
| | | | - Michael Friebe
- Surag Medical GmbH, 39118 Magdeburg, Germany
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
- Center for Innovation, Business Development and Entrepreneurship (CIBE), FOM University of Applied Science, 45127 Essen, Germany
| | - Michael C. Kreissl
- Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, 39120 Magdeburg, Germany; (S.S.); (M.C.K.)
- STIMULATE Research Campus, 39106 Magdeburg, Germany
- Center for Advanced Medical Engineering (CAME), Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany
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17
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Cheng A, Lee JWK, Ngiam KY. Use of 3D ultrasound to characterise temporal changes in thyroid nodules: an in vitro study. J Ultrasound 2023; 26:643-651. [PMID: 36053484 PMCID: PMC10468465 DOI: 10.1007/s40477-022-00698-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 06/13/2022] [Indexed: 10/14/2022] Open
Abstract
OBJECTIVE Thyroid nodules are extremely common, with prevalence rate up to 68%, yet only 7-15% of these are malignant. Many nodules require surveillance and 2-dimensional ultrasound (2D US) is used. Issues include the huge workload of obtaining and labeling images and difficulty comparing sizes of nodules over time due to large inter-operator variability. Inaccuracies may result in unnecessary FNAC or missed diagnosis of malignant nodules. METHODS We compared two techniques: freehand plain 2D US against freehand 2D US with gyroscopic guidance, both followed by 3D reconstruction using software. We measured the volume of nodules and a normal thyroid gland. RESULTS We found 2D US with gyroscopic guidance to be superior to plain 2D US as 3D reconstructions of greater accuracy are produced. The volume of the thyroid lobe measured 8.42 cm3 ± 0.94 was reasonably close to the normal average volume. However, the measured volume of the ellipsoidal nodule by the software is 8.69 cm3 ± 0.97 while the measured volume of the spherical nodule is 7.09 cm3 ± 0.79. As the expected volume of the nodules were 4.24cm3 and 4.19 cm3 respectively, the measured volume of the nodule was not accurate. The time taken to characterise nodules was reduced greatly from over 30 min in usual procedure to less than 10 min. CONCLUSION We find 3D US promising for evaluating size of thyroid nodules, with potential to study other TIRAD characteristics. Freehand 2D US with gyroscopic guidance shows the most promise for producing reliable, accurate and faster 3D reconstructions of thyroid nodules.
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Affiliation(s)
- Aldred Cheng
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - James Wai Kit Lee
- Division of Endocrine Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Kee Yuan Ngiam
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Division of Endocrine Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
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18
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Grani G, Del Gatto V, Cantisani V, Mandel SJ, Durante C. A Reappraisal of Suspicious Sonographic Features of Thyroid Nodules: Shape Is Not an Independent Predictor of Malignancy. J Clin Endocrinol Metab 2023; 108:e816-e822. [PMID: 36810804 DOI: 10.1210/clinem/dgad092] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 02/02/2023] [Accepted: 02/14/2023] [Indexed: 02/24/2023]
Abstract
CONTEXT For the correct clinical application of the sonographic risk-stratification systems, the definition of independent risk features that are foundational to each system is crucial. OBJECTIVE The aim of this study was to identify the gray-scale sonographic features independently associated with malignancy, and to compare different definitions. METHODS This prospective, diagnostic accuracy study took place in a single thyroid nodule referral center. All patients consecutively referred to our center for fine-needle aspiration cytology of a thyroid nodule between November 1, 2015 and March 30, 2020, were enrolled before cytology. Each nodule was examined by 2 experienced clinicians to record the sonographic features on a rating form. Histologic (when available) or cytologic diagnosis was used as the reference standard. For each single sonographic feature and definition, the sensitivity, specificity, positive and negative predictive values, and diagnostic odds ratios (DOR) were calculated. The significant predictors were then included in a multivariable regression model. RESULTS The final study cohort consisted of 903 nodules in 852 patients. A total of 76 nodules (8.4%) were malignant. Six features were independent predictors of malignancy: suspicious lymph node (DOR 16.23), extrathyroidal extension (DOR 6.60), irregular or infiltrative margins (DOR 7.13), marked hypoechogenicity (DOR 3.16), solid composition (DOR 3.61), and punctate hyperechoic foci (including microcalcifications and indeterminate foci; DOI 2.69). Taller-than-wide shape was not confirmed as an independent predictor. CONCLUSION We identified the key suspicious features of thyroid nodules and provided a simplified definition of some debated ones. Malignancy rate increases with number of features.
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Affiliation(s)
- Giorgio Grani
- Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome 00161, Italy
| | - Valeria Del Gatto
- Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome 00161, Italy
| | - Vito Cantisani
- Department of Radiological, Anatomo-Pathological, and Oncological Sciences, "Sapienza" University of Rome, Rome 00161, Italy
| | - Susan J Mandel
- Division of Endocrinology, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cosimo Durante
- Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome 00161, Italy
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19
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Xiang P, Ahmadi S, Coleman A, West W, Lobon I, Bikas A, Landa I, Marqusee E, Kim M, Alexander EK, Pappa T. Identifying and Predicting Diverse Patterns of Benign Nodule Growth. J Clin Endocrinol Metab 2023; 108:e458-e463. [PMID: 36625198 DOI: 10.1210/clinem/dgad007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/05/2022] [Accepted: 01/05/2023] [Indexed: 01/11/2023]
Abstract
CONTEXT The natural history of benign thyroid nodules is typically characterized by slow growth and minimal risk of malignant transformation. Available data have, to date, been unable to elucidate the diversity of benign nodule growth patterns over time nor predictive of which patients follow which pattern. OBJECTIVE We aimed to better define the diverse patterns of benign nodule behavior and their predictors. METHODS We prospectively studied 389 consecutive patients with solitary, solid, cytologically benign thyroid nodules ≥1 cm and follow-up ultrasound for at least 4 years. Demographic, sonographic, biochemical data were collected at initial evaluation, and subsequent growth patterns were identified over the follow-up. Predictors of growth at initial evaluation and 3 years of follow-up were defined. RESULTS The mean (±SD) follow-up was 7.7 (±2.7) years. Three distinct growth patterns were identified: A) stagnant nodules with average growth rate < 0.2 mm/year; B) slow-growing nodules with a rate 0.2 to 1.0 mm/year; and C) fast-growing nodules increasing > 1.0 mm/year. Fast-growing nodules represented 17.2% of the cohort, and were more frequent in patients younger than 50 years (OR 2.2 [1.2-4.1], P = 0.016), and in larger nodules (2.0-2.9 cm, OR 3.5 [1.7-7.1], P = 0.001; >3.0 cm, OR 4.4 [1.8-10.4], P = 0.001 vs reference 1-1.9 cm). In a multiple regression model, nodule growth at 3 years at an average growth rate over 0.2 mm/year over 3 years since initial evaluation was an independent predictor of longer-term fast nodule growth, even after adjusting for age, biological sex, TSH level, and nodule size (P < 0.001). CONCLUSION The natural history of benign nodule growth is diverse, with over 80% of nodules demonstrating minimal to no growth long-term. Nearly 20% of cytologically benign nodules may exhibit a fast, continued growth pattern, which can be predicted by the 3-year growth rate pattern. These findings can help inform decision making for tailored benign nodule follow-up and monitoring.
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Affiliation(s)
- PingPing Xiang
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
- Department of Endocrinology, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210029, China
| | - Sara Ahmadi
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Alexandra Coleman
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - William West
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Isabel Lobon
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Athanasios Bikas
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Iñigo Landa
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ellen Marqusee
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Matthew Kim
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Erik K Alexander
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Theodora Pappa
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, MA 02115, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
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20
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Cao CL, Li QL, Tong J, Shi LN, Li WX, Xu Y, Cheng J, Du TT, Li J, Cui XW. Artificial intelligence in thyroid ultrasound. Front Oncol 2023; 13:1060702. [PMID: 37251934 PMCID: PMC10213248 DOI: 10.3389/fonc.2023.1060702] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 04/07/2023] [Indexed: 05/31/2023] Open
Abstract
Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field.
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Affiliation(s)
- Chun-Li Cao
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Qiao-Li Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Jin Tong
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Li-Nan Shi
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Wen-Xiao Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Ya Xu
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Jing Cheng
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Ting-Ting Du
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital of Shihezi University, Shihezi, China
- NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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21
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Staibano P, Ham J, Chen J, Zhang H, Gupta MK. Inter-Rater Reliability of Thyroid Ultrasound Risk Criteria: A Systematic Review and Meta-Analysis. Laryngoscope 2023; 133:485-493. [PMID: 36039947 DOI: 10.1002/lary.30347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 07/05/2022] [Accepted: 07/29/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The most commonly employed diagnostic criteria for identifying thyroid nodules include Thyroid Imaging and Reporting Data System (TI-RADS) and American Thyroid Association (ATA) guidelines. The purpose of this systematic review and meta-analysis is to determine the inter-rater reliability of thyroid ultrasound criteria. METHODS We performed a library search of MEDLINE (Ovid), EMBASE (Ovid), and Web of Science for full-text articles published from January 2005 to June 2022. We included full-text primary research articles that used TI-RADS and/or ATA guidelines to evaluate thyroid nodules in adults. These included studies must have calculated inter-rater reliability using any validated metric. The Quality Appraisal for Reliability Studies (QAREL) was used to assess study quality. We planned for a random-effects meta-analysis, in addition to covariate and publication bias analyses. This study was performed in accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines and registered prior to conduction (International prospective register of systematic reviews-PROSPERO: CRD42021275072). RESULTS Of the 951 articles identified via the database search, 35 met eligibility criteria. All studies were observational. The most commonly utilized criteria were ACR Thyroid Imaging and Reporting Data System (TI-RADS) and/or ATA criteria, while the majority of studies employed Κ statistics. For ACR TI-RADS, the pooled Κ was 0.51 (95% confidence interval [CI]: 0.42, 0.57; n = 7) while for ATA, the pooled Κ was 0.52 (95% CI: 0.37, 0.67; n = 3). Due to the small number of studies, covariate or publication bias analyses were not performed. CONCLUSION Ultrasound criteria demonstrate moderate inter-rater reliability, but these findings are impacted by poor study quality and a lack of standardization. Laryngoscope, 133:485-493, 2023.
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Affiliation(s)
- Phillip Staibano
- Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Jennifer Ham
- Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Jennifer Chen
- Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Han Zhang
- Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Michael K Gupta
- Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
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22
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Duman E, Aslan A, Buz A, Sancak S, Aslan M, Kabaalioglu A, Fersahoglu AT, Okuroglu N, Onur E. Interobserver and Intraobserver Reliability in Sonoelastographic Assessment of Thyroid Nodules. Ultrasound Q 2023; 39:53-60. [PMID: 35943395 DOI: 10.1097/ruq.0000000000000616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Thyroid ultrasonography is the first and perhaps most fundamental step for the radiological distinction of benign and malignant nodules. In this study, 2 radiologists reviewed the sonoelastographic and Doppler images of thyroid nodules and evaluated for the intraobserver and interobserver reliability. PURPOSE We aimed to determine confusing nodule identifiers and sonographic features differently defined by observers. METHODS A total of 157 nodules in 91 patients (male/female, 72:19) with ages ranging from 18 to 72 years old were included in the study. Ultrasonographic images and video clips of the nodules were obtained and presented to 2 reviewers unaware of the cytopathology results. Two observers defined the characteristics of the nodules based on previously determined criteria. Then, intraobserver and interobserver correlation coefficients were calculated for each subcategory. RESULTS In the grayscale ultrasonographic examination, varying degrees from low to high interobserver correlation coefficients were obtained for different subcategories (between κ = 0.359 and κ = 0.821). In color Doppler examination, we obtained medium correlation coefficients ( κ = 0.493 and κ = 0.553). On the other hand, there was a high correlation coefficient in tissue compression elastography ( κ = 0.617 and κ = 0.638).According to our study results, elastographic pattern, shape of the nodule, presence of echogenic foci, and pathological lymph nodes are better predictors to determine the malignant potential of thyroid nodule with higher interobserver correlation. Therefore, these criteria may be used primarily for the evaluation of thyroid nodules. The intraobserver correlation coefficient was higher in the practitioner with longer experience, suggesting the importance of professional practice period on the decision-making process.
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Affiliation(s)
- Emrah Duman
- Department of Radiology, Göztepe Training and Research Hospital, Faculty of Medicine, Istanbul Medeniyet University Medical School, İstanbul, Turkey
| | | | - Aysenur Buz
- Department of Radiology, Vezirkopru State Hospital, Samsun
| | - Seda Sancak
- Department of Internal Medicine, Endocrinology and Metabolism Disorders, University of Health Sciences, Fatih Sultan Mehmet Education and Research Hospital, Istanbul, Turkey
| | | | | | - Ayse Tuba Fersahoglu
- Department of General Surgery, University of Health Sciences, Fatih Sultan Mehmet Education and Research Hospital
| | - Nalan Okuroglu
- Department of Internal Medicine, University of Health Sciences, Fatih Sultan Mehmet Education and Research Hospital, Istanbul, Turkey
| | - Ender Onur
- Department of General Surgery, University of Health Sciences, Fatih Sultan Mehmet Education and Research Hospital
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Gao Z, Chen Y, Sun P, Liu H, Lu Y. Clinical knowledge embedded method based on multi-task learning for thyroid nodule classification with ultrasound images. Phys Med Biol 2023; 68:045018. [PMID: 36652723 DOI: 10.1088/1361-6560/acb481] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 01/18/2023] [Indexed: 01/20/2023]
Abstract
Objective. Thyroid nodules are common glandular abnormality that need to be diagnosed as benign or malignant to determine further treatments. Clinically, ultrasonography is the main diagnostic method, but it is highly subjective with severe variability. Recently, many deep-learning-based methods have been proposed to alleviate subjectivity and achieve good results yet, these methods often neglect important guidance from clinical knowledge. Our objective is to utilize such guidance for accurate and reliable thyroid nodule classification.Approach. In this study, a multi-task learning model embedded with clinical knowledge of ACR Thyroid Imaging, Reporting and Data System guideline is proposed. The clinical features defined in the guideline have strong correlations with malignancy and they were modeled as tasks alongside the pathological type. Multi-task learning was utilized to exploit the correlations to improve diagnostic performance. To alleviate the impact of noisy labels on clinical features, a loss-weighting strategy was proposed. Five-fold cross-validation was applied to an internal training set of size 4989, and an external test set of size 243 was used for evaluation.Main results. The proposed multi-task learning model achieved an average AUC of 0.901 and an ensemble AUC of 0.917 on the test set, which significantly outperformed the single-task baseline models.Significance. The results indicated that multi-task learning of clinical features can effectively classify thyroid nodules and reveal the possibility of using clinical indicators as auxiliary tasks to improve performance when diagnosing other diseases.
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Affiliation(s)
- Zixiong Gao
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China
- Guangdong Province Key Laboratory Computational Science, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Yufan Chen
- Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, People's Republic of China
| | - Pengtao Sun
- The Department of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People's Republic of China
| | - Hongmei Liu
- Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, People's Republic of China
| | - Yao Lu
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China
- Guangdong Province Key Laboratory Computational Science, Sun Yat-Sen University, Guangzhou, People's Republic of China
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24
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Wang T, Yan D, Liu Z, Xiao L, Liang C, Xin H, Feng M, Zhao Z, Wang Y. Diagnosis of cervical lymph node metastasis with thyroid carcinoma by deep learning application to CT images. Front Oncol 2023; 13:1099104. [PMID: 36776294 PMCID: PMC9909181 DOI: 10.3389/fonc.2023.1099104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The incidence of thyroid diseases has increased in recent years, and cervical lymph node metastasis (LNM) is considered an important risk factor for locoregional recurrence. This study aims to develop a deep learning-based computer-aided diagnosis (CAD) method to diagnose cervical LNM with thyroid carcinoma on computed tomography (CT) images. Methods A new deep learning framework guided by the analysis of CT data for automated detection and classification of LNs on CT images is proposed. The presented CAD system consists of two stages. First, an improved region-based detection network is designed to learn pyramidal features for detecting small nodes at different feature scales. The region proposals are constrained by the prior knowledge of the size and shape distributions of real nodes. Then, a residual network with an attention module is proposed to perform the classification of LNs. The attention module helps to classify LNs in the fine-grained domain, improving the whole classification network performance. Results A total of 574 axial CT images (including 676 lymph nodes: 103 benign and 573 malignant lymph nodes) were retrieved from 196 patients who underwent CT for surgical planning. For detection, the data set was randomly subdivided into a training set (70%) and a testing set (30%), where each CT image was expanded to 20 images by rotation, mirror image, changing brightness, and Gaussian noise. The extended data set included 11,480 CT images. The proposed detection method outperformed three other detection architectures (average precision of 80.3%). For classification, ROI of lymph node metastasis labeled by radiologists were used to train the classification network. The 676 lymph nodes were randomly divided into 70% of the training set (73 benign and 401 malignant lymph nodes) and 30% of the test set (30 benign and 172 malignant lymph nodes). The classification method showed superior performance over other state-of-the-art methods with an accuracy of 96%, true positive and negative rates of 98.8 and 80%, respectively. It outperformed radiologists with an area under the curve of 0.894. Discussion The extensive experiments verify the high efficiency of the proposed method. It is considered instrumental in a clinical setting to diagnose cervical LNM with thyroid carcinoma using preoperative CT images. The future research can consider adding radiologists' experience and domain knowledge into the deep-learning based CAD method to make it more clinically significant. Conclusion The extensive experiments verify the high efficiency of the proposed method. It is considered instrumental in a clinical setting to diagnose cervical LNM with thyroid carcinoma using preoperative CT images.
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Affiliation(s)
- Tiantian Wang
- Department of Thyroid Surgery, the Second Affiliated Hospital of Zhejiang University College of Medicine, Hangzhou, China
| | - Ding Yan
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Zhaodi Liu
- School of Medicine, Zhejiang University, Hangzhou, China
| | - Lianxiang Xiao
- Shandong Provincial Maternal and Child Health Care Hospital, Shandong University, Jinan, China
| | - Changhu Liang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Haotian Xin
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Mengmeng Feng
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Zijian Zhao
- School of Control Science and Engineering, Shandong University, Jinan, China,*Correspondence: Zijian Zhao,
| | - Yong Wang
- Department of Thyroid Surgery, the Second Affiliated Hospital of Zhejiang University College of Medicine, Hangzhou, China
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Clinical value of artificial intelligence in thyroid ultrasound: a prospective study from the real world. Eur Radiol 2023:10.1007/s00330-022-09378-y. [PMID: 36622410 DOI: 10.1007/s00330-022-09378-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 12/07/2022] [Accepted: 12/13/2022] [Indexed: 01/10/2023]
Abstract
OBJECTIVE To evaluate the diagnostic performance of a commercial artificial intelligence (AI)-assisted ultrasonography (US) for thyroid nodules and to validate its value in real-world medical practice. MATERIALS AND METHODS From March 2021 to July 2021, 236 consecutive patients with 312 suspicious thyroid nodules were prospectively enrolled in this study. One experienced radiologist performed US examinations with a real-time AI system (S-Detect). US images and AI reports of the nodules were recorded. Nine residents and three senior radiologists were invited to make a "benign" or "malignant" diagnosis based on recorded US images without knowing the AI reports. After referring to AI reports, the diagnosis was made again. The diagnostic performance of AI, residents, and senior radiologists with and without AI reports were analyzed. RESULTS The sensitivity, accuracy, and AUC of the AI system were 0.95, 0.84, and 0.753, respectively, and were not statistically different from those of the experienced radiologists, but were superior to those of the residents (all p < 0.01). The AI-assisted resident strategy significantly improved the accuracy and sensitivity for nodules ≤ 1.5 cm (all p < 0.01), while reducing the unnecessary biopsy rate by up to 27.7% for nodules > 1.5 cm (p = 0.034). CONCLUSIONS The AI system achieved performance, for cancer diagnosis, comparable to that of an average senior thyroid radiologist. The AI-assisted strategy can significantly improve the overall diagnostic performance for less-experienced radiologists, while increasing the discovery of thyroid cancer ≤ 1.5 cm and reducing unnecessary biopsies for nodules > 1.5 cm in real-world medical practice. KEY POINTS • The AI system reached a senior radiologist-like level in the evaluation of thyroid cancer and could significantly improve the overall diagnostic performance of residents. • The AI-assisted strategy significantly improved ≤ 1.5 cm thyroid cancer screening AUC, accuracy, and sensitivity of the residents, leading to an increased detection of thyroid cancer while maintaining a comparable specificity to that of radiologists alone. • The AI-assisted strategy significantly reduced the unnecessary biopsy rate for thyroid nodules > 1.5 cm by the residents, while maintaining a comparable sensitivity to that of radiologists alone.
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Hong MJ, Lee YH, Kim JH, Na DG, You SH, Shin JE, Kim SK, Yang KS, Korean Society of Thyroid Radiology. Orientation of the ultrasound probe to identify the taller-than-wide sign of thyroid malignancy: a registry-based study with the Thyroid Imaging Network of Korea. Ultrasonography 2023; 42:111-120. [PMID: 36458371 PMCID: PMC9816703 DOI: 10.14366/usg.22082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/19/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE Although the taller-than-wide (TTW) sign has been regarded as one of the most specific ultrasound (US) features of thyroid malignancy, uncertainty still exists regarding the US probe's orientation when evaluating it. This study investigated which US plane would be optimal to identify the TTW sign based on malignancy risk stratification using a registry-based imaging dataset. METHODS A previous study by 17 academic radiologists retrospectively analyzed the US images of 5,601 thyroid nodules (≥1 cm, 1,089 malignant and 4,512 benign) collected in the webbased registry of Thyroid Imaging Network of Korea through the collaboration of 26 centers. The present study assessed the diagnostic performance of the TTW sign itself and fine needle aspiration (FNA) indications via a comparison of four international guidelines, depending on the orientation of the US probe (criterion 1, transverse plane; criterion 2, either transverse or longitudinal plane). RESULTS Overall, the TTW sign was more frequent in malignant than in benign thyroid nodules (25.3% vs. 4.6%). However, the statistical differences between criteria 1 and 2 were negligible for sensitivity, specificity, and area under the curve (AUC) based on the size effect (all P<0.05, Cohen's d=0.19, 0.10, and 0.07, respectively). Moreover, the sensitivity, specificity, and AUC of the four FNA guidelines were similar between criteria 1 and 2 (all P>0.05, respectively). CONCLUSION A longitudinal US probe orientation provided little additional diagnostic value over the transverse orientation in detecting the TTW sign of thyroid nodules.
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Affiliation(s)
- Min Ji Hong
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
| | - Young Hen Lee
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Korea,Correspondence to: Young Hen Lee, MD, Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwongu, Ansan 15355, Korea Tel. +82-31-412- 5228 Fax. +82-31-412-5224 E-mail:
| | - Ji-hoon Kim
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Dong Gyu Na
- Department of Radiology, GangNeung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea
| | - Sung-Hye You
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Ji Eun Shin
- Health Screening and Promotion Center, Asan Medical Center, Seoul, Korea
| | - Seul Kee Kim
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Kyung-Sook Yang
- Department of Biostatistics, Korea University College of Medicine, Seoul, Korea
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Tao Y, Yu Y, Wu T, Xu X, Dai Q, Kong H, Zhang L, Yu W, Leng X, Qiu W, Tian J. Deep learning for the diagnosis of suspicious thyroid nodules based on multimodal ultrasound images. Front Oncol 2022; 12:1012724. [PMID: 36425556 PMCID: PMC9680169 DOI: 10.3389/fonc.2022.1012724] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/18/2022] [Indexed: 09/07/2023] Open
Abstract
OBJECTIVES This study aimed to differentially diagnose thyroid nodules (TNs) of Thyroid Imaging Reporting and Data System (TI-RADS) 3-5 categories using a deep learning (DL) model based on multimodal ultrasound (US) images and explore its auxiliary role for radiologists with varying degrees of experience. METHODS Preoperative multimodal US images of 1,138 TNs of TI-RADS 3-5 categories were randomly divided into a training set (n = 728), a validation set (n = 182), and a test set (n = 228) in a 4:1:1.25 ratio. Grayscale US (GSU), color Doppler flow imaging (CDFI), strain elastography (SE), and region of interest mask (Mask) images were acquired in both transverse and longitudinal sections, all of which were confirmed by pathology. In this study, fivefold cross-validation was used to evaluate the performance of the proposed DL model. The diagnostic performance of the mature DL model and radiologists in the test set was compared, and whether DL could assist radiologists in improving diagnostic performance was verified. Specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristics curves (AUC) were obtained. RESULTS The AUCs of DL in the differentiation of TNs were 0.858 based on (GSU + SE), 0.909 based on (GSU + CDFI), 0.906 based on (GSU + CDFI + SE), and 0.881 based (GSU + Mask), which were superior to that of 0.825-based single GSU (p = 0.014, p< 0.001, p< 0.001, and p = 0.002, respectively). The highest AUC of 0.928 was achieved by DL based on (G + C + E + M)US, the highest specificity of 89.5% was achieved by (G + C + E)US, and the highest accuracy of 86.2% and sensitivity of 86.9% were achieved by DL based on (G + C + M)US. With DL assistance, the AUC of junior radiologists increased from 0.720 to 0.796 (p< 0.001), which was slightly higher than that of senior radiologists without DL assistance (0.796 vs. 0.794, p > 0.05). Senior radiologists with DL assistance exhibited higher accuracy and comparable AUC than that of DL based on GSU (83.4% vs. 78.9%, p = 0.041; 0.822 vs. 0.825, p = 0.512). However, the AUC of DL based on multimodal US images was significantly higher than that based on visual diagnosis by radiologists (p< 0.05). CONCLUSION The DL models based on multimodal US images showed exceptional performance in the differential diagnosis of suspicious TNs, effectively increased the diagnostic efficacy of TN evaluations by junior radiologists, and provided an objective assessment for the clinical and surgical management phases that follow.
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Affiliation(s)
- Yi Tao
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yanyan Yu
- The National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Tong Wu
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiangli Xu
- Department of Ultrasound, The Second Hospital of Harbin, Harbin, China
| | - Quan Dai
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hanqing Kong
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lei Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Weidong Yu
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaoping Leng
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Weibao Qiu
- Shenzhen Key Laboratory of Ultrasound Imaging and Therapy, Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jiawei Tian
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
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Krönke M, Eilers C, Dimova D, Köhler M, Buschner G, Schweiger L, Konstantinidou L, Makowski M, Nagarajah J, Navab N, Weber W, Wendler T. Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry. PLoS One 2022; 17:e0268550. [PMID: 35905038 PMCID: PMC9337648 DOI: 10.1371/journal.pone.0268550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/02/2022] [Indexed: 11/29/2022] Open
Abstract
Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time, and accuracy. Volume reference was MRI. 28 healthy volunteers (24—50 a) were scanned with 2D and 3D ultrasound (and by MRI) by three physicians (MD 1, 2, 3) with different experience levels (6, 4, and 1 a). In the 2D scans, the thyroid lobe volumes were calculated with the ellipsoid formula. A convolutional deep neural network (CNN) automatically segmented the 3D thyroid lobes. 26, 6, and 6 random lobe scans were used for training, validation, and testing, respectively. On MRI (T1 VIBE sequence) the thyroid was manually segmented by an experienced MD. MRI thyroid volumes ranged from 2.8 to 16.7ml (mean 7.4, SD 3.05). The CNN was trained to obtain an average Dice score of 0.94. The interobserver variability comparing two MDs showed mean differences for 2D and 3D respectively of 0.58 to 0.52ml (MD1 vs. 2), −1.33 to −0.17ml (MD1 vs. 3) and −1.89 to −0.70ml (MD2 vs. 3). Paired samples t-tests showed significant differences for 2D (p = .140, p = .002 and p = .002) and none for 3D (p = .176, p = .722 and p = .057). Intraobsever variability was similar for 2D and 3D ultrasound. Comparison of ultrasound volumes and MRI volumes showed a significant difference for the 2D volumetry of all MDs (p = .002, p = .009, p <.001), and no significant difference for 3D ultrasound (p = .292, p = .686, p = 0.091). Acquisition time was significantly shorter for 3D ultrasound. Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times.
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Affiliation(s)
- Markus Krönke
- Department of Radiology and Nuclear Medicine, German Heart Center, Technical University of Munich, Munich, Germany
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christine Eilers
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Computer Science, Technical University of Munich, Garching Near Munich, Germany
- * E-mail:
| | - Desislava Dimova
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Computer Science, Technical University of Munich, Garching Near Munich, Germany
| | - Melanie Köhler
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Computer Science, Technical University of Munich, Garching Near Munich, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Gabriel Buschner
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Lilit Schweiger
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Lemonia Konstantinidou
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Computer Science, Technical University of Munich, Garching Near Munich, Germany
| | - Marcus Makowski
- Department of Radiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - James Nagarajah
- Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nassir Navab
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Computer Science, Technical University of Munich, Garching Near Munich, Germany
- Chair for Computer Aided Medical Procedures, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Wolfgang Weber
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Thomas Wendler
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Computer Science, Technical University of Munich, Garching Near Munich, Germany
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Davey MG, O'Donnell JPM, Boland MR, Ryan ÉJ, Walsh SR, Kerin MJ, Lowery AJ. Optimal localization strategies for non-palpable breast cancers -A network meta-analysis of randomized controlled trials. Breast 2022; 62:103-113. [PMID: 35151049 PMCID: PMC8844725 DOI: 10.1016/j.breast.2022.02.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 01/31/2022] [Accepted: 02/06/2022] [Indexed: 12/04/2022] Open
Abstract
PURPOSE Mammographic screening programmes have increased detection rates of non-palpable breast cancers. In these cases, wire-guided localization (WGL) is the most common approach used to guide breast conserving surgery (BCS). Several RCTs have compared WGL to a range of novel localization techniques. We aimed to perform a network meta-analysis (NMA) of randomized controlled trials (RCTs) comparing methods of non-palpable breast cancer localization. METHODS A NMA was performed according to PRISMA-NMA guidelines. Analysis was performed using R packages and Shiny. RESULTS 24 RCTs assessing 9 tumour localization methods in 4236 breasts were included. Margin positivity and reoperation rates were 16.9% (714/4236) and 14.3% (409/2870) respectively. Cryo-assisted localization had the highest margin positivity (28.2%, 58/206) and reoperation (18.9%, 39/206) rates. Compared to WGL (n = 2045 from 24 RCTs) only ultrasound guided localization (USGL) (n = 316 from 3 RCTs) significantly lowered margin positivity (odds ratio (OR): 0.192, 95% confidence interval (CI): 0.079-0.450) and reoperation rates (OR: 0.182, 95%CI: 0.069-0.434). Anchor-guided localization (n = 52, 1 RCT) significantly lowered margin positivity (OR: 0.229, 95%CI: 0.050-0.938) and magnetic-marker localization improved patient satisfaction (OR: 0.021, 95%CI: 0.001-0.548). There was no difference in operation duration, overall complications, haematoma, seroma, surgical site infection rates, or specimen size/vol/wt between methods. CONCLUSION USGL and AGL are non-inferior to WGL for the localization of non-palpable breast cancers. The reported data suggests that these techniques confer reduced margin positivity rates and requirement for re-operation. However, caution when interpreting results relating to RCTs with small sample sizes and further validation is required in larger prospective, randomized studies.
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Affiliation(s)
- Matthew G Davey
- Department of Surgery, The Lambe Institute for Translational Research, National University of Ireland, Galway, Galway, H91 YR71, Ireland.
| | - John P M O'Donnell
- Department of Surgery, The Lambe Institute for Translational Research, National University of Ireland, Galway, Galway, H91 YR71, Ireland
| | - Michael R Boland
- Department of Surgery, The Lambe Institute for Translational Research, National University of Ireland, Galway, Galway, H91 YR71, Ireland
| | - Éanna J Ryan
- Department of Surgery, The Lambe Institute for Translational Research, National University of Ireland, Galway, Galway, H91 YR71, Ireland
| | - Stewart R Walsh
- Department of Surgery, The Lambe Institute for Translational Research, National University of Ireland, Galway, Galway, H91 YR71, Ireland
| | - Michael J Kerin
- Department of Surgery, The Lambe Institute for Translational Research, National University of Ireland, Galway, Galway, H91 YR71, Ireland
| | - Aoife J Lowery
- Department of Surgery, The Lambe Institute for Translational Research, National University of Ireland, Galway, Galway, H91 YR71, Ireland
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Chen Y, Gao Z, He Y, Mai W, Li J, Zhou M, Li S, Yi W, Wu S, Bai T, Zhang N, Zeng W, Lu Y, Liu H. An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules. Radiology 2022; 303:613-619. [PMID: 35315719 DOI: 10.1148/radiol.211455] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background US-based diagnosis of thyroid nodules is subjective and influenced by radiologists' experience levels. Purpose To develop an artificial intelligence model based on American College of Radiology Thyroid Imaging Reporting and Data System characteristics for diagnosing thyroid nodules and identifying nodule characteristics (hereafter, MTI-RADS) and to compare the performance of MTI-RADS, radiologists, and a model trained on benign and malignant status based on surgical histopathologic analysis (hereafter, MDiag). Materials and Methods In this retrospective study, 1588 surgically proven nodules from 636 consecutive patients (mean age, 49 years ± 14 [SD]; 485 women) were included. MTI-RADS and MDiag were trained on US images of 1345 nodules (January 2018 to December 2019). The performance of MTI-RADS was compared with that of MDiag and radiologists with different experience levels on the test data set (243 nodules, January 2019 to December 2019) with the DeLong method and McNemar test. Results The area under the receiver operating characteristic curve (AUC) and sensitivity of MTI-RADS were 0.91 and 83% (55 of 66 nodules), respectively, which were not significantly different from those of experienced radiologists (0.93 [P = .45] and 92% [61 of 66 nodules; P = .07]) and exceeded those of junior radiologists (0.78 [P < .001] and 70% [46 of 66 nodules; P = .04]). The specificity of MTI-RADS (87% [154 of 177 nodules]) was higher than that of both experienced and junior radiologists (80% [141 of 177 nodules; P = .02] and 75% [133 of 177 nodules; P = .001], respectively). The AUC of MTI-RADS was higher than that of MDiag (0.91 vs 0.84, respectively; P = .001). In the test set of 243 nodules, the consistency rates between MTI-RADS and the experienced group were higher than those between MTI-RADS and the junior group for composition (79% [n = 193] vs 73% [n = 178], respectively; P = .02), echogenicity (75% [n = 183] vs 68% [n = 166]; P = .04), shape (93% [n = 227] vs 88% [n = 215]; P = .04), and smooth or ill-defined margin (72% [n = 174] vs 63% [n = 152]; P = .002). Conclusion The area under the receiver operating characteristic curve (AUC) of an artificial intelligence model based on the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) was higher than that of a model trained on benign and malignant status based on surgical histopathologic analysis. The AUC and sensitivity of the model based on TI-RADS exceeded those of junior radiologists; the specificity of the model was higher than that of both experienced and junior radiologists. © RSNA, 2022.
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Affiliation(s)
- Yufan Chen
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Zixiong Gao
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Yanni He
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Wuping Mai
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Jinhua Li
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Meijun Zhou
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Sushu Li
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Wenhong Yi
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Shuyu Wu
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Tong Bai
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Ning Zhang
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Weibo Zeng
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Yao Lu
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Hongmei Liu
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
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Yan L, Li X, Xiao J, Li Y, Zhu Y, He H, Luo Y. Contrast-enhanced ultrasound is a reliable and reproducible assessment of necrotic ablated volume after radiofrequency ablation for benign thyroid nodules: a retrospective study. Int J Hyperthermia 2021; 39:40-47. [PMID: 34936850 DOI: 10.1080/02656736.2021.1991009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
PURPOSE To investigate the intra- and inter-observer reliability and agreement of contrast-enhanced ultrasound (CEUS) in measuring ablated volume (Va) after radiofrequency ablation (RFA) for benign thyroid nodules. MATERIALS This retrospective study evaluated 65 patients with 74 benign thyroid nodules who underwent RFA. Patients were followed up at 1, 3, 6, and 12 months and every 12 months thereafter. Two independent observers measured the Va using CEUS during the same follow-up visit. The intra- and inter-observer reliability was assessed using intraclass correlation coefficient (ICC) with 95% confidence interval. The Bland-Altman analysis was used to evaluate the inter-observer agreement, which was expressed as a mean difference with 95% limit of agreement (LOA). RESULTS No significant difference was found in Va measurements by the two observers with a mean follow-up time of 41.17 ± 16.80 months (all p > 0.05). The intra- and inter-observer reliability were both excellent (ICC >0.90) at each follow-up period. The 95% LOA became wider over the follow-up period. The smallest 95% LOA was found at 1 month with a LOA from 0.8117 to 1.122, and the largest 95% LOA was from 0.5694 to 1.343 at 36 months. CONCLUSIONS CEUS could provide a reliable and reproducible assessment of Va after RFA for benign thyroid nodules. In clinical post-ablation follow-up, the irregular morphology of ablated area and the variation by different observers could not affect the assessment of Va by CEUS.
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Affiliation(s)
- Lin Yan
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - XinYang Li
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China.,School of Medicine, Nankai University, Tianjin, China
| | - Jing Xiao
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - YingYing Li
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yaqiong Zhu
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hongying He
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China.,School of Medicine, Nankai University, Tianjin, China
| | - Yukun Luo
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
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Zhao Z, Yang C, Wang Q, Zhang H, Shi L, Zhang Z. A deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules. Med Phys 2021; 48:7959-7970. [PMID: 34719057 DOI: 10.1002/mp.15319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 09/30/2021] [Accepted: 10/18/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE The incidence of thyroid cancer has significantly increased in the last few decades. However, diagnosis of the thyroid nodules is labor and time intensive for radiologists and strongly depends on the personal experience of the radiologists. In this pursuit, the present study envisaged to develop a deep learning-based computer-aided diagnosis (CAD) method that enabled the automatic detection and classification of suspicious thyroid nodules in order to reduce the unnecessary fine-needle aspiration biopsy. METHODS The CAD method consisted of two main parts: detecting the location of thyroid nodules using a multiscale detection network and classifying the detected thyroid nodules by an attention-based classification network. RESULTS The performance of the proposed method was evaluated and compared with that of other state-of-the-art deep learning methods and experienced radiologists. The proposed detection method outperformed three other detection architectures (average precision, 82.1% vs. 78.3%, 77.2%, and 74.8%). Moreover, the classification method showed a superior performance compared with four other state-of-the-art classification networks (accuracy, 94.8% vs. 91.2%, 85.0%, 80.8%, and 72.1%) and that by experienced radiologists (mean value of area under the curve, 0.941 vs. 0.833). CONCLUSIONS Our study verified the high efficiency of the proposed detection method. The findings can help improve the diagnostic performance of radiologists. However, the developed CAD system requires more training and evaluation in a large-population study.
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Affiliation(s)
- Zijian Zhao
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Congmin Yang
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qian Wang
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Huawei Zhang
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Linlin Shi
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Zhiwen Zhang
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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Ghai S, O'Brien C, Goldstein DP, Sawka AM. Ultrasound in active surveillance for low-risk papillary thyroid cancer: imaging considerations in case selection and disease surveillance. Insights Imaging 2021; 12:130. [PMID: 34529219 PMCID: PMC8446145 DOI: 10.1186/s13244-021-01072-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 08/07/2021] [Indexed: 01/25/2023] Open
Abstract
Active surveillance (AS) of small, low-risk papillary thyroid cancers (PTCs) is increasingly studied in prospective observational studies. Ultrasound is the primary imaging modality for case selection. While researchers have put forward selection criteria for PTCs based on size, absence of suspicious lymph nodes and tumor location, there are limited reported data highlighting inherent ultrasound limitations and guidelines for case selection and follow-up. We report our experience including imaging limitations encountered in the ongoing AS prospective observational study for PTCs measuring < 2 cm at our institute. We define disease progression as an increase in size of > 3 mm in the largest dimension of nodule or evidence of metastatic disease or extrathyroidal extension. Accurate, consistent and reproducible measurements of PTCs are essential in risk stratifying patients for the option of AS or disease progression. Interobserver discrepancy, shadowing from coarse calcification and background parenchyma heterogeneity or thyroiditis can limit accurate PTC size assessment and therefore hinder patient eligibility evaluation or AS follow-up. Following the ACR Thyroid Imaging, Reporting and Data System (TI-RADS) protocol of three-axes technique to measure a thyroid nodule enables reproducibility of measurements. In patients with multi-nodular goiter, accurate identification and labeling of the PTC is important to avoid mistaking with adjacent benign nodules at follow-up. Ultrasound assessment for extrathyroid extension of PTC, and relationship of PTC to trachea and the anatomic course of the recurrent laryngeal nerve are important considerations in evaluation for AS eligibility.
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Affiliation(s)
- Sangeet Ghai
- Joint Department of Medical Imaging, University Health Network - Mount Sinai Hospital - Women's College Hospital, University of Toronto, Toronto, ON, Canada. .,1PMB-283, Toronto General Hospital, 585 University Avenue, Toronto, ON, M5G 2N2, Canada.
| | - Ciara O'Brien
- Joint Department of Medical Imaging, University Health Network - Mount Sinai Hospital - Women's College Hospital, University of Toronto, Toronto, ON, Canada
| | - David P Goldstein
- Princess Margaret Cancer Centre, Department of Otolaryngology-Head and Neck Surgery/Surgical Oncology, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Anna M Sawka
- Division of Endocrinology, Department of Medicine, University Health Network and University of Toronto, Toronto, ON, Canada
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Yan L, Luo Y. Response to letter to the editor from Dr. Bernardi regarding suitability of residual vital ratio for prediction of local regrowth following radiofrequency ablation for benign thyroid nodules. Int J Hyperthermia 2021; 38:189-190. [PMID: 33576298 DOI: 10.1080/02656736.2021.1883128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Affiliation(s)
- Lin Yan
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yukun Luo
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
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Ghazizadeh S, Kelly TL, Khajanchee YS, Fleser J, Rozenfeld Y, Neuman M, Hammill CW, Orr L, Aliabadi-Wahle S. Standardization of thyroid ultrasound reporting in the community setting decreases biopsy rates. Clin Endocrinol (Oxf) 2021; 94:1035-1042. [PMID: 33529386 DOI: 10.1111/cen.14431] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVE With the rising incidence of thyroid cancer, a standardized approach to the evaluation of thyroid nodules is essential. Despite the presence of multiple national guidelines detailing evaluation and management of these nodules, significant variability exists in the information that is collected and reported to clinicians from diagnostic imaging. The aim of this study was to evaluate the impact of thyroid ultrasound standardization on thyroid cancer detection in a community practice setting. DESIGN As part of a physician-driven quality improvement project, a multidisciplinary team created an electronic worksheet to be utilized by sonographers to capture suspicious findings based on societal guidelines and agreed on institutional criteria for recommending fine needle aspiration (FNA) of thyroid nodules. PATIENTS For a one-year period prior to and after the intervention, all ultrasounds performed for suspected thyroid pathology, excluding patients undergoing follow-up imaging, were reviewed at two affiliated community hospitals served by a single radiology and pathology group. MEASUREMENTS The number of fine needle biopsies recommended and performed, as well as the percentage of FNAs positive for malignancy were evaluated. RESULTS A total of 608 and 675 ultrasounds were reviewed in pre- and post-standardization periods, respectively. Following standardization, there was a similar percentage of FNAs recommended (35% vs. 37%, p = .68), fewer FNAs per total ultrasounds performed (36% vs. 31%, p = .03), fewer FNAs performed when FNA was not explicitly recommended (9.9% vs. 2.8%, p = .000046) and an increased detection of cytology consistent with, or suspicious for, malignancy (5% vs. 11.5%, p = .0028). CONCLUSIONS Standardization of thyroid imaging protocol and management recommendations can reduce the number of FNAs performed and increase the percentage of positive tests in a community setting.
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Anterior neck soft tissue measurements on computed tomography to predict difficult laryngoscopy: a retrospective study. Sci Rep 2021; 11:8438. [PMID: 33875761 PMCID: PMC8055648 DOI: 10.1038/s41598-021-88076-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 04/08/2021] [Indexed: 11/20/2022] Open
Abstract
Predicting difficult laryngoscopy is an essential component of the airway management. We aimed to evaluate the use of anterior neck soft tissue measurements on computed tomography for predicting difficult laryngoscopy and to present a clear measurement protocol. In this retrospective study, 281 adult patients whose tracheas were intubated using a direct laryngoscope for thyroidectomy were enrolled. On computed tomography, the distances from the midpoint of the thyrohyoid membrane to the closest concave point of the vallecular (membrane-to-vallecula distance; dMV), and to the most distant point of the epiglottis (membrane-to-epiglottis distance; dME) were measured, respectively. The extended distances straight to the skin anterior from the dMV and dME were called the skin-to-vallecula distance (dSV) and skin-to-epiglottis distance (dSE), respectively. Difficult laryngoscopy was defined by a Cormack-Lehane grade of > 2. Difficult laryngoscopy occurred in 40 (14%) cases. Among four indices, the dMV showed the highest prediction ability for difficult laryngoscopy with an area under the receiver operating characteristic curve of 0.884 (95% confidence interval 0.841–0.919, P < 0.001). The optimal dMV cut-off value for predicting difficult laryngoscopy was 2.33 cm (sensitivity 75.0%; specificity 93.8%). The current study provides novel evidence that increased dMV is a potential predictive indicator of difficult laryngoscopy.
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Zhu J, Zhang S, Yu R, Liu Z, Gao H, Yue B, Liu X, Zheng X, Gao M, Wei X. An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images. Quant Imaging Med Surg 2021; 11:1368-1380. [PMID: 33816175 DOI: 10.21037/qims-20-538] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Background The aim of this study was to construct a deep convolutional neural network (CNN) model for localization and diagnosis of thyroid nodules on ultrasound and evaluate its diagnostic performance. Methods We developed and trained a deep CNN model called the Brief Efficient Thyroid Network (BETNET) using 16,401 ultrasound images. According to the parameters of the model, we developed a computer-aided diagnosis (CAD) system to localize and differentiate thyroid nodules. The validation dataset (1,000 images) was used to compare the diagnostic performance of the model using three state-of-the-art algorithms. We used an internal test set (300 images) to evaluate the BETNET model by comparing it with diagnoses from five radiologists with varying degrees of experience in thyroid nodule diagnosis. Lastly, we demonstrated the general applicability of our artificial intelligence (AI) system for diagnosing thyroid cancer in an external test set (1,032 images). Results The BETNET model accurately detected thyroid nodules in visualization experiments. The model demonstrated higher values for area under the receiver operating characteristic (AUC-ROC) curve [0.983, 95% confidence interval (CI): 0.973-0.990], sensitivity (99.19%), accuracy (98.30%), and Youden index (0.9663) than the three state-of-the-art algorithms (P<0.05). In the internal test dataset, the diagnostic accuracy of the BETNET model was 91.33%, which was markedly higher than the accuracy of one experienced (85.67%) and two less experienced radiologists (77.67% and 69.33%). The area under the ROC curve of the BETNET model (0.951) was similar to that of the two highly skilled radiologists (0.940 and 0.953) and significantly higher than that of one experienced and two less experienced radiologists (P<0.01). The kappa coefficient of the BETNET model and the pathology results showed good agreement (0.769). In addition, the BETNET model achieved an excellent diagnostic performance (AUC =0.970, 95% CI: 0.958-0.980) when applied to ultrasound images from another independent hospital. Conclusions We developed a deep learning model which could accurately locate and automatically diagnose thyroid nodules on ultrasound images. The BETNET model exhibited better diagnostic performance than three state-of-the-art algorithms, which in turn performed similarly in diagnosis as the experienced radiologists. The BETNET model has the potential to be applied to ultrasound images from other hospitals.
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Affiliation(s)
- Jialin Zhu
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Sheng Zhang
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Ruiguo Yu
- College of Intelligence and Computing, Tianjin University, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin Key Laboratory of Advanced Networking, Tianjin, China
| | - Zhiqiang Liu
- College of Intelligence and Computing, Tianjin University, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin Key Laboratory of Advanced Networking, Tianjin, China
| | - Hongyan Gao
- Tianjin Xiqing District Women and Children's Health and Family Planning Service Center, Tianjin, China
| | - Bing Yue
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xun Liu
- Department of Ultrasonography, the Fifth Central Hospital of Tianjin, Tianjin, China
| | - Xiangqian Zheng
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Ming Gao
- Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xi Wei
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Chung SR, Choi YJ, Lee SS, Kim SO, Lee SA, Jeon MJ, Kim WG, Lee JH, Baek JH. Interobserver Reproducibility in Sonographic Measurement of Diameter and Volume of Papillary Thyroid Microcarcinoma. Thyroid 2021; 31:452-458. [PMID: 33287640 DOI: 10.1089/thy.2020.0317] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Active surveillance is recommended as an alternative to immediate surgery for low-risk papillary thyroid microcarcinoma (PTMC), and determining meaningful changes in diameter and volume on ultrasonography (US) is critical. However, interobserver reproducibility of the sonographic measurement of maximum diameter and volume of PTMC has not been well established. We aimed to determine the reproducibility in the measurement of maximum diameter and volume of PTMC on US. Methods: Consecutive patients who underwent US for pathologically proven PTMC between December 2018 and December 2019 were retrospectively reviewed. Two observers independently performed sonographic measurement of each nodule using standardized measurement methods. Each observer measured maximum transverse, anteroposterior, and longitudinal nodule diameters, and using these, nodule volume was calculated using the ellipsoid formula. Interobserver reproducibility in the measurement of the maximum diameter and volume was assessed using percentage reproducibility coefficient (RC). Z-tests of the intraclass correlation coefficients (ICCs) were used to compare the interobserver reproducibility in subgroups defined according to sonographic characteristics, such as the presence of microcalcification, nodule size, and parenchymal heterogeneity. Results: A total of 197 thyroid nodules from 188 patients were included in the study series. The percentage RCs were 71.8% [95% confidence interval, CI 65.4-79.7%] and 23.7% [CI 21.6-26.3%] for volume and maximum diameter measurements, respectively. There were no significant differences noted in the ICC values according to nodule orientation, presence of calcifications, size, or parenchymal heterogeneity. Conclusion: For PTMC, a difference of up to 24% in the maximum diameter and 72% in the volume may be considered to be within measurement error on US. This value may be used to determine the cutoff for defining meaningful change in the maximum diameter and volume for PTMC during active surveillance.
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Affiliation(s)
- Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Jun Choi
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seon-Ok Kim
- Department of Clinical Epidemiology and Biostatistics, and Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sun-Ah Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Min Ji Jeon
- Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Won Gu Kim
- Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jeong Hyun Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Ultrasound Cine Loop Standard Operating Procedure for Benign Thyroid Diseases-Evaluation of Non-Physician Application. Diagnostics (Basel) 2021; 11:diagnostics11010067. [PMID: 33406645 PMCID: PMC7824138 DOI: 10.3390/diagnostics11010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/18/2020] [Accepted: 12/31/2020] [Indexed: 11/16/2022] Open
Abstract
Conventional ultrasound (US) is time-consuming, and results are subjected to high interobserver variability. In this study, the reliability of a novel thyroid US cine loop standard operating procedure (SOP) applied by non-physicians (Medical Technical Assistant, MTA) is investigated. Thirty-three consecutive patients (22 females, 11 males) were enrolled. Patients underwent conventional thyroid US performed by a nuclear medicine physician and additional MTA US cine loop according to a local SOP that includes transversal and sagittal cine loops covering the entire thyroid. The video sequences were transferred to the Picture Archiving and Communication System (PACS) for second reading purposes. MTA US data were not considered for medical reports but for blinded second reading review of the PACS images. The results of conventional physician US reports and reviewed MTA US cine loops were compared regarding size determinations of the thyroid and its nodules, as well as Thyroid Imaging Reporting and Data Systems (TIRADS) classification of all identified lesions. The results revealed very high concordance between conventional physician US and MTA US cine loop review for both size measurements and TIRADS classifications (r(s) = 0.84-0.99, p < 0.0001 each). Minor technical impairments were identified. The evaluated thyroid US cine loop SOP enables reliable second reading results and can be applied by non-physicians.
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Goundan PN, Mamou J, Rohrbach D, Smith J, Patel H, Wallace KD, Feleppa EJ, Lee SL. A Preliminary Study of Quantitative Ultrasound for Cancer-Risk Assessment of Thyroid Nodules. Front Endocrinol (Lausanne) 2021; 12:627698. [PMID: 34093429 PMCID: PMC8170470 DOI: 10.3389/fendo.2021.627698] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 04/26/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Gray-scale, B-mode ultrasound (US) imaging is part of the standard clinical procedure for evaluating thyroid nodules (TNs). It is limited by its instrument- and operator-dependence and inter-observer variability. In addition, the accepted high-risk B-mode US TN features are more specific for detecting classic papillary thyroid cancer rather than the follicular variant of papillary thyroid cancer or follicular thyroid cancer. Quantitative ultrasound (QUS) is a technique that can non-invasively assess properties of tissue microarchitecture by exploiting information contained in raw ultrasonic radiofrequency (RF) echo signals that is discarded in conventional B-mode imaging. QUS provides quantitative parameter-value estimates that are a function of the properties of US scatterers and microarchitecture of the tissue. The purpose of this preliminary study was to assess the performance of QUS parameters in evaluating benign and malignant thyroid nodules. METHODS Patients from the Thyroid Health Center at the Boston Medical Center were recruited to participate. B-mode and RF data were acquired and analyzed in 225 TNs (24 malignant and 201 benign) from 208 patients. These data were acquired either before (167 nodules) or after (58 nodules) subjects underwent fine-needle biopsy (FNB). The performance of a combination of QUS parameters (CQP) was assessed and compared with the performance of B-mode risk-stratification systems. RESULTS CQP produced an ROC AUC value of 0.857 ± 0.033 compared to a value of 0.887 ± 0.033 (p=0.327) for the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and 0.880 ± 0.041 (p=0.367) for the American Thyroid Association (ATA) risk-stratification system. Furthermore, using a CQP threshold of 0.263 would further reduce the number of unnecessary FNBs in 44% of TNs without missing any malignant TNs. When CQP used in combination with ACR TI-RADS, a potential additional reduction of 49 to 66% in unnecessary FNBs was demonstrated. CONCLUSION This preliminary study suggests that QUS may provide a method to classify TNs when used by itself or when combined with a conventional gray-scale US risk-stratification system and can potentially reduce the need to biopsy TNs.
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Affiliation(s)
- Poorani N. Goundan
- Section of Endocrinology, Diabetes and Nutrition, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
- *Correspondence: Poorani N. Goundan,
| | - Jonathan Mamou
- Lizzi Center for Biomedical Engineering, Riverside Research, New York, NY, United States
| | | | - Jason Smith
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Harshal Patel
- Section of Endocrinology, Diabetes and Nutrition, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | | | - Ernest J. Feleppa
- Lizzi Center for Biomedical Engineering, Riverside Research, New York, NY, United States
| | - Stephanie L. Lee
- Section of Endocrinology, Diabetes and Nutrition, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
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Yan L, Luo Y, Song Q, Li N, Xiao J, Zhang Y, Zhu Y. Inter-observer reliability in ultrasound measurement of benign thyroid nodules in the follow-up of radiofrequency ablation: a retrospective study. Int J Hyperthermia 2020; 37:1336-1344. [PMID: 33251890 DOI: 10.1080/02656736.2020.1849826] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
PURPOSE To investigate the inter-observer reliability of ultrasound (US) measurement in volume and volume reduction rate (VRR) of benign thyroid nodules during the follow-up of radiofrequency ablation (RFA). MATERIALS This retrospective study evaluated 76 patients with 88 benign thyroid nodules who underwent RFA. Two independent observers performed the US measurements during the same follow-up visit. The Bland-Altman analysis was used to evaluate the inter-observer reliability of volume and VRR (logarithmic transformation). The mean difference and 95% limits-of-agreement (LOA) were obtained after antilogarithm. RESULTS No significant differences were found in the volume and VRR measurements during a mean follow-up time of 35.52 ± 12.82 months. After antilogarithms, the mean difference of volume was 0.9961, 0.9987, 1.0016, 0.9972, 0.9977 and 0.9969 at 1, 3, 6, 12, 24 and 36 months, respectively. The 95% LOA of volume became wider over the follow-up period, and the largest one was between 0.8471 and 1.1733 at 36 months. The 95% LOA of VRR became narrower over the follow-up period, and the largest one was between 0.9541 and 1.0469 at 1 month. The incidence of regrowth was 20.45% and the largest 95% LOA of regrowth nodules was between 0.9028 and 1.284 at 12 months. At the same follow-up period, VRR had a narrower 95% LOA than volume. Compared with volume ≥10 ml, nodules <10 ml revealed a larger 95% LOA in both the volume and VRR. CONCLUSION The inter-observer reliability of the US measurements of benign thyroid nodules during the follow-up period of RFA was acceptable. The variation by different observers could not affect the evaluation of efficacy.
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Affiliation(s)
- Lin Yan
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China.,Health Management Center, The Second Hospital of Dalian Medical University, Dalian, China
| | - Yukun Luo
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qing Song
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Nan Li
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jing Xiao
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ying Zhang
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yaqiong Zhu
- Department of Ultrasound, First Medical Center, Chinese PLA General Hospital, Beijing, China
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Zhou J, Yin L, Wei X, Zhang S, Song Y, Luo B, Li J, Qian L, Cui L, Chen W, Wen C, Peng Y, Chen Q, Lu M, Chen M, Wu R, Zhou W, Xue E, Li Y, Yang L, Mi C, Zhang R, Wu G, Du G, Huang D, Zhan W. 2020 Chinese guidelines for ultrasound malignancy risk stratification of thyroid nodules: the C-TIRADS. Endocrine 2020; 70:256-279. [PMID: 32827126 DOI: 10.1007/s12020-020-02441-y] [Citation(s) in RCA: 167] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 07/28/2020] [Indexed: 02/05/2023]
Abstract
Thyroid nodules are very common all over the world, and China is no exception. Ultrasound plays an important role in determining the risk stratification of thyroid nodules, which is critical for clinical management of thyroid nodules. For the past few years, many versions of TIRADS (Thyroid Imaging Reporting and Data System) have been put forward by several institutions with the aim to identify whether nodules require fine-needle biopsy or ultrasound follow-up. However, no version of TIRADS has been widely adopted worldwide till date. In China, as many as ten versions of TIRADS have been used in different hospitals nationwide, causing a lot of confusion. With the support of the Superficial Organ and Vascular Ultrasound Group of the Society of Ultrasound in Medicine of the Chinese Medical Association, the Chinese-TIRADS that is in line with China's national conditions and medical status was established based on literature review, expert consensus, and multicenter data provided by the Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound.
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Affiliation(s)
- JianQiao Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200025, China.
| | - LiXue Yin
- Institute of Ultrasound in Medicine, The Affiliated Sichuan Provincial People's Hospital of Electronic Science and Technology University of China, Chengdu, 610071, China.
| | - Xi Wei
- Department of Diagnostic and Therapeutic Ultrasound, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Sheng Zhang
- Department of Diagnostic and Therapeutic Ultrasound, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - YanYan Song
- Department of Biostatistics, Institute of Medical Sciences, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China
| | - BaoMing Luo
- Department of Ultrasound, SunYat-sen Memorial Hospital, SunYat-sen University, Guangzhou, 510120, China
| | - JianChu Li
- Department of Ultrasound, Peking Union Medical College Hospital, Beijing, 100730, China
| | - LinXue Qian
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - LiGang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing, 100191, China
| | - Wen Chen
- Department of Ultrasound, Peking University Third Hospital, Beijing, 100191, China
| | - ChaoYang Wen
- Department of Ultrasound, Peking University International Hospital, Beijing, 102206, China
| | - YuLan Peng
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Qin Chen
- Department of Ultrasound, The Affiliated Sichuan Provincial People's Hospital of Electronic Science and Technology University of China, Chengdu, 610071, China
| | - Man Lu
- Department of Ultrasound, Sichuan Cancer Hospital, Chengdu, 610041, China
| | - Min Chen
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Rong Wu
- Department of Ultrasound, Shanghai First People's Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 201620, China
| | - Wei Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200025, China
| | - EnSheng Xue
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - YingJia Li
- Department of Ultrasound, Nanfang Hospital of Southern Medical University, Guangzhou, 510515, China
| | - LiChun Yang
- Department of Ultrasound, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, 650031, China
| | - ChengRong Mi
- Department of Ultrasound, General Hospital of Ningxia Medical University, Yinchuan, 750021, China
| | - RuiFang Zhang
- Department of Ultrasound, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China
| | - Gang Wu
- Department of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450003, China
| | - GuoQing Du
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - DaoZhong Huang
- Department of Ultrasound, Tongji Hospital, Tongji Medical Colloge, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - WeiWei Zhan
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200025, China.
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Non-enhanced ultrasound is not a satisfactory modality for measuring necrotic ablated volume after radiofrequency ablation of benign thyroid nodules: a comparison with contrast-enhanced ultrasound. Eur Radiol 2020; 31:3226-3236. [PMID: 33128600 DOI: 10.1007/s00330-020-07398-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/02/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To investigate the intra- and inter-observer reliability and agreement between gray-scale and Doppler ultrasound (US) and contrast-enhanced ultrasound (CEUS) in measuring ablated volume (Va) after radiofrequency ablation (RFA) for benign thyroid nodules. METHODS A total of 173 patients with 190 benign thyroid nodules who underwent RFA were included in this study. After RFA, the total volume of ablated nodule was divided into Va and the incompletely treated vital volume. Patients were followed up at 1, 3, 6, 12 months, and every 12 months thereafter. Two independent observers measured Va using US and CEUS during the same follow-up visit. The intra- and inter-observer reliability of the two measurement modalities was assessed using intraclass correlation coefficient (ICC) with 95% confidence interval. The Bland-Altman analysis was used to evaluate agreement, which was expressed as a mean difference with 95% limits of agreement (LOA). RESULTS The mean follow-up time was 23.17 ± 12.70 months. Va measured by US was significantly larger than by CEUS (p < 0.001). The intra- and inter-observer reliability decreased over the follow-up period and became moderate in both subgroups at 12 months (all ICC < 0.75). The mean difference and LOA became larger and wider during the follow-up. The best agreement was found in nodules < 10 ml at 1 month with a mean difference of 1.166 and LOA between 0.413 and 3.294. CONCLUSIONS The intra- and inter-observer reliability and agreement of US and CEUS in measuring Va were unsatisfactory. CEUS should be considered when Va was needed for further evaluation or in the case of nodules with suspected regrowth. KEY POINTS • Va measured by gray-scale and Doppler US was significantly larger than that by CEUS. • Va measured by gray-scale and Doppler US lacked intra- and inter-observer reliability and agreement with CEUS. • CEUS should be preceded to gray-scale and Doppler US for the measurement of Va.
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Ultrasonography for the Prediction of High-Volume Lymph Node Metastases in Papillary Thyroid Carcinoma: Should Surgeons Believe Ultrasound Results? World J Surg 2020; 44:4142-4148. [PMID: 32918103 PMCID: PMC7599182 DOI: 10.1007/s00268-020-05755-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/09/2020] [Indexed: 12/18/2022]
Abstract
Background Lymph node metastasis (LNM) often occurs in papillary thyroid carcinoma (PTC); the efficacy of ultrasound for predicting high-volume lymph node metastases (LNMs) in patients with PTC remains unexplored. Methods The medical records of 2073 consecutive PTC patients were reviewed. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated to evaluate the efficacy of ultrasound. Risk factors for LNM/high-volume LNMs and lymph node involvement on ultrasound (usLNM) were identified by univariate and multivariate analyses. Results Of all the patients, 936 (45.2%) patients had LNMs, and 254 (12.3%) patients had high-volume LNMs. The sensitivity of ultrasound for detecting LNM/high-volume LNMs was 27.9% and 63.8%, respectively; the specificity was 93.1% and 90.3%, respectively. The NPV for ultrasound in detecting high-volume LNMs was 94.7%. In multivariate analysis, male sex (OR = 2.108, p < 0.001), tumor diameter > 1.0 cm (OR = 2.304, p < 0.001) and usLNM (+) (OR = 12.553, p < 0.001) were independent clinical risk factors for high-volume LNMs. Tumor diameter > 1 cm (OR = 3.036, p < 0.001) and male sex (OR = 1.642, p < 0.001) were independent clinical risk factors for usLNM; a skilled sonographer (OR = 1.121, p = 0.358) was not significantly associated with usLNM. Conclusions Lymph node involvement found by ultrasound has great predictive value for high-volume LNMs; the NPV is very high for patients without lymph node involvement on ultrasound. The ultrasound results do not appear to be influenced by the experience of the sonographer. Electronic supplementary material The online version of this article (10.1007/s00268-020-05755-0) contains supplementary material, which is available to authorized users.
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Sun C, Zhang Y, Chang Q, Liu T, Zhang S, Wang X, Guo Q, Yao J, Sun W, Niu L. Evaluation of a deep learning-based computer-aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images. Med Phys 2020; 47:3952-3960. [PMID: 32473030 DOI: 10.1002/mp.14301] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 05/18/2020] [Accepted: 05/20/2020] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. METHODS The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand-crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. RESULTS The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. CONCLUSIONS The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system.
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Affiliation(s)
- Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yukang Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China
| | - Qing Chang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Tianjiao Liu
- Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Shaohang Zhang
- Department of Ultrasound, Beijing Haidian Hospital, Haidian Section of Peking University Third Hospital, Beijing, 100080, China
| | - Xi Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Qianqian Guo
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jinpeng Yao
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Weidong Sun
- Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Lijuan Niu
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
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Kim DH, Chung SR, Choi SH, Kim KW. Accuracy of thyroid imaging reporting and data system category 4 or 5 for diagnosing malignancy: a systematic review and meta-analysis. Eur Radiol 2020; 30:5611-5624. [PMID: 32356157 DOI: 10.1007/s00330-020-06875-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/01/2020] [Accepted: 04/08/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To determine the accuracies of the American College of Radiology (ACR)-thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules. METHODS Original studies reporting the diagnostic accuracy of TIRADS for determining malignancy on ultrasound were identified in MEDLINE and EMBASE up to June 23, 2019. The meta-analytic summary sensitivity and specificity were obtained for TIRADS category 5 (TR-5) and category 4 or 5 (TR-4/5), using a bivariate random effects model. To explore study heterogeneity, meta-regression analyses were performed. RESULTS Of the 34 eligible articles (37,585 nodules), 25 used ACR-TIRADS, 12 used K-TIRADS, and seven used EU-TIRADS. For TR-5, the meta-analytic sensitivity was highest for EU-TIRADS (78% [95% confidence interval, 64-88%]), followed by ACR-TIRADS (70% [61-79%]) and K-TIRADS (64% [58-70%]), although the differences were not significant. K-TIRADS showed the highest meta-analytic specificity (93% [91-95%]), which was similar to ACR-TIRADS (89% [85-92%]) and EU-TIRADS (89% [77-95%]). For TR-4/5, all three TIRADS systems had sensitivities higher than 90%. K-TIRADS had the highest specificity (61% [50-72%]), followed by ACR-TIRADS (49% [43-56%]) and EU-TIRADS (48% [35-62%]), although the differences were not significant. Considerable threshold effects were noted with ACR- and K-TIRADS (p ≤ 0.01), with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity (p ≤ 0.05). CONCLUSIONS There was no significant difference among these three international TIRADS, but the trend toward higher sensitivity with EU-TIRADS and higher specificity with K-TIRADS. KEY POINTS • For TIRADS category 5, the meta-analytic sensitivity was highest for the EU-TIRADS, followed by the ACR-TIRADS and the K-TIRADS, although the differences were not significant. • For TIRADS category 5, K-TIRADS showed the highest meta-analytic specificity, which was similar to ACR-TIRADS and EU-TIRADS. • Considerable threshold effects were noted with ACR- and K-TIRADS, with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity.
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Affiliation(s)
- Dong Hwan Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Sang Hyun Choi
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
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Grani G, Lamartina L, Ramundo V, Falcone R, Lomonaco C, Ciotti L, Barone M, Maranghi M, Cantisani V, Filetti S, Durante C. Taller-Than-Wide Shape: A New Definition Improves the Specificity of TIRADS Systems. Eur Thyroid J 2020; 9:85-91. [PMID: 32257957 PMCID: PMC7109429 DOI: 10.1159/000504219] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 10/17/2019] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION A taller-than-wide (TTW) shape is a suspicious feature of thyroid nodules commonly defined as an anteroposterior/transverse diameter (AP/T) ratio >1. An intraobserver variability of up to 18% in AP diameter evaluations has been described, which may lead to overreporting of this feature. To potentially improve the reliability of the TTW definition, we propose an arbitrary ratio of ≥1.2. OBJECTIVE The aim of this study was to estimate the impact of this definition on diagnostic performance. METHODS We prospectively analyzed 553 thyroid nodules referred for cytology evaluation at an academic center. Before fine-needle aspiration, two examiners jointly defined all sonographic features considered in risk stratification systems developed by the American Thyroid Association (ATA), the American Association of Clinical Endocrinologists (AACE), the American College of Radiology (ACR TIRADS), the European Thyroid Association (EU-TIRADS), and the Korean Society of Thyroid Radiology (K-TIRADS). TTW was defined according to the current definition (AP/T diameter ratio >1) and an arbitrary alternative definition (AP/T ratio >1.2). RESULTS The alternative definition classified fewer nodules as TTW (28, 5.1% vs. 94, 17%). The current and proposed definitions have a sensitivity of 26.2 and 11.9% (p = 0.03) and a specificity of 83.8 and 95.5% (p < 0.001). Thus, as a single feature, the arbitrary definition has a lower sensitivity and a higher specificity. When applied to sonographic risk stratification systems, however, the proposed definition would increase the number of avoided biopsies (up to 58.2% for ACR TIRADS) and the specificity of all systems, without negative impact on sensitivity or diagnostic odds ratio. CONCLUSIONS Re-defining TTW nodules as those with an AP/T ratio ≥1.2 improves this marker's specificity for malignancy. Using this definition in risk stratification systems will increase their specificity, reducing the number of suggested biopsies without significantly diminishing their overall diagnostic performance.
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Affiliation(s)
- Giorgio Grani
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
- *Giorgio Grani, MD, PhD, Department of Translational and Precision Medicine, “Sapienza” University of Rome, Viale del Policlinico 155, IT–00161 Rome (Italy), E-Mail
| | - Livia Lamartina
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
| | - Valeria Ramundo
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
| | - Rosa Falcone
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
| | - Cristiano Lomonaco
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
| | - Laura Ciotti
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
| | - Martina Barone
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
| | - Marianna Maranghi
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
| | - Vito Cantisani
- Diagnostic and Ultrasound Innovations Unit, Azienda Ospedaliera Universitaria Policlinico Umberto I, “Sapienza” University of Rome, Rome, Italy
| | - Sebastiano Filetti
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
| | - Cosimo Durante
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome, Italy
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Lee SB, Lee Y, Kim SJ, Yoon JH, Kim SH, Kim SJ, Jung HK, Hahn S, Baek HJ. Intraobserver and interobserver reliability in sonographic size measurements of gallbladder polyps. Eur Radiol 2020; 30:206-212. [PMID: 31399751 DOI: 10.1007/s00330-019-06385-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 07/16/2019] [Accepted: 07/19/2019] [Indexed: 11/30/2022]
Abstract
OBJECTIVES To evaluate the intraobserver and interobserver reliability of gallbladder polyp measurements using transabdominal US and the factors that affect reliability. METHODS From November 2017 to February 2018, two radiologists measured the maximum diameter of 91 gallbladder polyps using transabdominal US. Intraobserver and interobserver agreement were determined using 95% Bland-Altman limits of agreement and intraclass correlation coefficients (ICCs). The effects of image settings, polyp location, and polyp size were evaluated by comparing ICCs using z tests. RESULTS The intraobserver agreement rates were 0.960 (95% confidence interval [CI], 0.939-0.973) for observer 1 and 0.962 (95% CI, 0.943-0.975) for observer 2. The ICCs between the two observers were 0.963 (95% CI, 0.926-0.979) for the first measurement and 0.973 (95% CI, 0.950-0.984) for the second measurement. The 95% limits of agreement on repeated measurements were 22.3-25.2% of the mean, and those between the two observers were 25.5-34.2% of the mean. ICCs for large polyps (≥ 5 mm) were significantly higher than those for small polyps (< 5 mm). There were no significant differences in the ICCs between image settings and polyp location. CONCLUSIONS Polyp size measurements using transabdominal US are highly repeatable and reproducible. Polyp size significantly affects the reliability of measurement. Diameter changes of approximately less than 25% may fall within the measurement error; this should be considered while interpreting the change in size during follow-up US, especially for small polyps. KEY POINTS • Gallbladder polyp size measurement using transabdominal US is highly repeatable and reproducible. • Diameter changes of approximately less than 25% should be interpreted carefully, especially in small polyps.
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Affiliation(s)
- Seul Bi Lee
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea.,Department of Radiology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Yedaun Lee
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea.
| | - Seung Jin Kim
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea
| | - Jung Hee Yoon
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea
| | - Seung Ho Kim
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea
| | - Suk Jung Kim
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea
| | - Hyun Kyung Jung
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea
| | - Seok Hahn
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Haeundae-ro 875, Haeundae-gu, Busan, Republic of Korea
| | - Hye Jin Baek
- Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
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Xie X, Yu Y. Effect of the location and size of thyroid nodules on the diagnostic performance of ultrasound elastography: A retrospective analysis. Clinics (Sao Paulo) 2020; 75:e1720. [PMID: 32578824 PMCID: PMC7297523 DOI: 10.6061/clinics/2020/e1720] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 03/17/2020] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES Ultrasound-guided fine-needle aspiration biopsies are recommended for the detection of suspicious thyroid nodules. However, the best approach regarding suspicious ultrasound features for thyroid nodules is still unclear. This study aimed to evaluate the effect of location and size of thyroid nodules on the diagnostic performance of strain ultrasound elastography. In addition, this study evaluated whether ultrasound elastography predicts malignancy in thyroid nodules. METHODS Data regarding the size, depth, and distance from the carotid artery of nodules, the elasticity contrast index, and the nature of nodules were analyzed. RESULTS There was no significant difference in the depth (p=0.092) and the distance from the carotid artery (p=0.061) between benign and suspicious nodules. Suspicious nodules were smaller than benign nodules (p<0.0001, q=23.84) and had a higher elasticity contrast index (p<0.0001, q=21.05). The depth of nodules and the size of the nodule were not associated with the correct value of the elasticity contrast index (p>0.05 for both). The diagnostic performance of ultrasound elastography was not affected by the distance of the nodules from the carotid artery if they were located ≥15 mm from the carotid artery (p=0.5960). However, if the suspicious nodules were located <15 mm from the carotid artery, the diagnostic accuracy was hampered (p=0.006). CONCLUSIONS The strain ultrasound elastography should be carefully evaluated when small thyroid nodules are located near the carotid artery.
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Affiliation(s)
- Xinxin Xie
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China, 230022
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China, 230022
- *Corresponding author. E-mail:
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Pampena R, Raucci M, Mirra M, Lombardi M, Piana S, Kyrgidis A, Peccerillo F, Paganelli A, Garbarino F, Pellacani G, Longo C. The role of ultrasound examination for early identification of lymph-node metastasis of cutaneous squamous cell carcinoma: results from a single institutional center. Ital J Dermatol Venerol 2019; 156:479-483. [PMID: 31804052 DOI: 10.23736/s2784-8671.19.06487-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Metastasis from cutaneous squamous cell carcinoma (cSCC) mainly involve the regional nodal basin, with an incidence ranging from 2-4% until 15% in case of high-risk tumors. When dealing with high-risk cSCC, ultrasound examination is recommended every 3-4 months during follow-up. We aimed to determine the role of US examination in the early diagnosis of nodal metastasis from cSCC. METHODS We conducted a retrospective cohort study enrolling consecutive cases of histopathologically verified cSCCs from January 2007 to March 2018. All the enrolled cases were followed for at least one year and all cases of histopathologically verified metastasis were registered. We also reported if ultrasound of the regional basin was performed between the primary diagnosis and metastasis and how the latter was identified, through ultrasounds or clinically. A Kaplan-Meier survival analysis was conducted on patients undergoing ultrasounds during follow-up. RESULTS A total of 1881 cases, belonging to 1441 patients were included. Thirty-one cases of nodal metastasis diagnosed after the primary tumor, in as many patients, were identified. All of the selected metastasis derived from high-risk primary cSCCs. Only in 19 cases ultrasound examination was performed during follow-up; of these, 10 were diagnosed through ultrasounds and 9 clinically. Survival analysis demonstrated that the time interval between primary tumor and metastasis was significantly lower for patients with metastasis diagnosed by ultrasounds than clinically (P=0.036). CONCLUSIONS Our study highlighted the need to optimize the use of nodal ultrasound examination for high-risk cSCCs in order to early detect metastasis.
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Affiliation(s)
- Riccardo Pampena
- Centro Oncologico ad Alta Tecnologia Diagnostica, AUSL - IRCCS Reggio Emilia, Reggio Emilia Italy -
| | - Margherita Raucci
- Centro Oncologico ad Alta Tecnologia Diagnostica, AUSL - IRCCS Reggio Emilia, Reggio Emilia Italy
| | - Marica Mirra
- Centro Oncologico ad Alta Tecnologia Diagnostica, AUSL - IRCCS Reggio Emilia, Reggio Emilia Italy
| | - Mara Lombardi
- Centro Oncologico ad Alta Tecnologia Diagnostica, AUSL - IRCCS Reggio Emilia, Reggio Emilia Italy
| | - Simonetta Piana
- Unit of Pathology, AUSL - IRCCS Reggio Emilia, Reggio Emilia, Italy
| | | | | | - Alessia Paganelli
- Unit of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
| | - Federico Garbarino
- Unit of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
| | - Giovanni Pellacani
- Unit of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
| | - Caterina Longo
- Centro Oncologico ad Alta Tecnologia Diagnostica, AUSL - IRCCS Reggio Emilia, Reggio Emilia Italy.,Unit of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
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