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Wang H, Hao Y, Wang P, Wang E, Ding S, Chen B. A Multi-Sequence MRI-Based Hierarchical Expert Diagnostic Method for the Molecular Subtype of Breast Cancer. IEEE J Biomed Health Inform 2025; 29:2885-2898. [PMID: 40031779 DOI: 10.1109/jbhi.2024.3486182] [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: 03/05/2025]
Abstract
The molecular subtype of breast cancer is significant for patients' treatment and prognosis. The application of multi-sequence MRI technology provides a new non-invasive diagnostic method, which can more accurately assess the vascular status of tumors and reveal fine structures. However, providing interpretable classification results remains a challenge. Recently, although many convolutional neural network (CNN) and fine-grained classification methods based on MRI inputs have been proposed. However, most of these methods operate in a âblack-boxâ without a detailed explanation of the intermediate processes, resulting in a lack of interpretability of the breast cancer classification process. To address this problem, we proposes a multi-sequence MRI-based hierarchical expert diagnostic method for the molecular subtype of breast cancer. With the strong differentiation module, this method first identifies enhanced features in breast tumors, ensuring that the subsequent classification process is precisely focused on the lesion features. In addition, inspired by the co-diagnosis of multiple experts in clinical diagnosis, we set up a mechanism of collaborative diagnostic corrective learning by hierarchical experts to provide an interpretable classification process. Compared with previous studies, the framework learns features with a strong distinguishing ability for breast tumor classification. Specifically, multiple experts corrected each other's learning to give more accurate and interpretable classification results, significantly improving clinical diagnosis's practical value. We conducted extensive experiments on a breast dataset and compared it quantitatively with other methods, and we achieved the best performance in terms of accuracy (0.889) and F1 Score (0.893). We make the code public on GitHub: https://github.com/yanfangHao/HED.
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Yang X, Li J, Sun H, Chen J, Xie J, Peng Y, Shang T, Pan T. Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients. BREAST CANCER (DOVE MEDICAL PRESS) 2025; 17:187-200. [PMID: 39990966 PMCID: PMC11846489 DOI: 10.2147/bctt.s488200] [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: 07/23/2024] [Accepted: 01/21/2025] [Indexed: 02/25/2025]
Abstract
Background Accurate identification of the molecular subtypes of breast cancer is essential for effective treatment selection and prognosis prediction. Aim This study aimed to evaluate the diagnostic performance of a radiomics model, which integrates breast mammography and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the molecular subtypes of breast cancer. Methods We retrospectively included 462 female patients with pathologically confirmed breast cancer, including 53 cases of triple-negative, 94 cases of HER2 overexpression, 95 cases of luminal A, and 215 cases of luminal B breast cancer. Radiomics analysis was performed using FAE software, wherein the radiomic features were examined about the hormone receptor status. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy. Results In multivariate analysis, radiomic features were the only independent predictive factors for molecular subtypes. The model that incorporates multimodal fusion features from breast mammography and DCE-MRI images exhibited superior overall performance compared to using either modality independently. The AUC values (or accuracies) for six pairings were as follows: 0.648 (0.627) for luminal A vs luminal B, 0.819 (0.793) for luminal A vs HER2 overexpression, 0.725 (0.696) for luminal A vs triple-negative subtype, 0.644 (0.560) for luminal B vs HER2 overexpression, 0.625 (0.636) for luminal B vs triple-negative subtype, and 0.598 (0.500) for triple-negative subtype vs HER2 overexpression. Conclusion The radionics model utilizing multimodal fusion features from breast mammography combined with DCE-MRI images showed high performance in distinguishing molecular subtypes of breast cancer. It is of significance to accurately predict prognosis and determine treatment strategy of breast cancer by molecular classification.
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Affiliation(s)
- Xianwei Yang
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China
| | - Jing Li
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China
| | - Hang Sun
- School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, People’s Republic of China
| | - Jing Chen
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China
| | - Jin Xie
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China
| | - Yonghui Peng
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China
| | - Tao Shang
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China
| | - Tongyong Pan
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, People’s Republic of China
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Acosta-Jiménez S, González-Chávez SA, Camarillo-Cisneros J, Pacheco-Tena C, Barcenas-López M, González-Lozada LE, Hernández-Orozco C, Burboa-Delgado JH, Ochoa-Albíztegui RE. Preliminary Results: Comparison of Convolutional Neural Network Architectures as an Auxiliary Clinical Tool Applied to Screening Mammography in Mexican Women. J Med Biol Eng 2024; 44:390-400. [PMID: 40027073 PMCID: PMC11870662 DOI: 10.1007/s40846-024-00868-6] [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: 02/19/2024] [Accepted: 04/08/2024] [Indexed: 03/05/2025]
Abstract
Purpose Mammography is the modality of choice for the early detection of breast cancer. Deep learning, using convolutional neural networks (CNNs) specifically, have achieved extraordinary results in the classification of diseases, including breast cancer, on imaging. The images used to train a CNN varies based on several factors, such as imaging technique, imaging equipment, and study population; these factors significantly affect the accuracy of the CNN models. The aim of this study was to develop a novel CNN for the classification of mammograms as benign or malignant and to compare its utility to that of popular pre-trained CNNs in the literature using transfer learning. All CNNs were trained to detect breast cancer on mammograms using mammograms from a created database of Mexican women (MAMMOMX-PABIOM) and from a public database of UK women (MIAS). Methods A database (MAMMOMX-PABIOM) was built comprising 1,070 mammography images of 235 Mexican patients from 4 hospitals in Mexico. The study also used mammographic images from the Mammographic Image Analysis Society (MIAS) public database, which comprises mammography images from the UK National Breast Screening Programme. A novel CNN was developed and trained based on different configurations of training data; the accuracy of the models resulting from the novel CNN were compared with models resulting from more advanced pre-trained CNNs (DenseNet121, MobileNetV2, ResNet 50, VGG16) which were built using transfer learning. Results Of the models resulting from pre-trained CNNs using transfer learning, the model based on MobileNetV2 and training data from the MAMMOMX-PABIOM database achieved the highest validation accuracy of 70.10%. In comparison, the novel CNN, when trained with the data configuration A6, which comprises data from both the MAMMOMX-PABIOM database and the MIAS database, produced a much higher accuracy of 99.14%. Conclusion Although transfer learning is a widely used technique when training, data is scarce. The novel CNN produced much higher accuracy values across all configurations of training data compared to the accuracy values of pre-trained CNNs using transfer learning. In addition, this study addresses the gap in that neither a national database of mammograms of Mexican women exists, nor a deep learning tool for the classification of mammograms as benign or malignant that is focused on this population.
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Affiliation(s)
- Samara Acosta-Jiménez
- Laboratorio PABIOM, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Chihuahua, México
| | - Susana Aideé González-Chávez
- Laboratorio PABIOM, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Chihuahua, México
| | - Javier Camarillo-Cisneros
- Laboratorio de Física Química Computacional, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Chihuahua, México
| | - César Pacheco-Tena
- Laboratorio PABIOM, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Chihuahua, México
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Barzegar M, Schiaffino S. Editorial for "A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI". J Magn Reson Imaging 2024; 59:1436-1437. [PMID: 37501333 DOI: 10.1002/jmri.28908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 07/07/2023] [Indexed: 07/29/2023] Open
Abstract
Level of Evidence5Technical Efficacy Stage2
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Affiliation(s)
- Mojtaba Barzegar
- National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
- Brain mapping Foundation, Los Angeles, California, USA
- Society for Brain Mapping and Therapeutics, Los Angeles, California, USA
- Intelligent Quantitative Bio-Medical imaging (IQBMI), Tehran, Iran
| | - Simone Schiaffino
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
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Yang Y, Xiang T, Lv X, Li L, Lui LM, Zeng T. Double Transformer Super-Resolution for Breast Cancer ADC Images. IEEE J Biomed Health Inform 2024; 28:917-928. [PMID: 38079366 DOI: 10.1109/jbhi.2023.3341250] [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: 02/06/2024]
Abstract
Diffusion-weighted imaging (DWI) has been extensively explored in guiding the clinic management of patients with breast cancer. However, due to the limited resolution, accurately characterizing tumors using DWI and the corresponding apparent diffusion coefficient (ADC) is still a challenging problem. In this paper, we aim to address the issue of super-resolution (SR) of ADC images and evaluate the clinical utility of SR-ADC images through radiomics analysis. To this end, we propose a novel double transformer-based network (DTformer) to enhance the resolution of ADC images. More specifically, we propose a symmetric U-shaped encoder-decoder network with two different types of transformer blocks, named as UTNet, to extract deep features for super-resolution. The basic backbone of UTNet is composed of a locally-enhanced Swin transformer block (LeSwin-T) and a convolutional transformer block (Conv-T), which are responsible for capturing long-range dependencies and local spatial information, respectively. Additionally, we introduce a residual upsampling network (RUpNet) to expand image resolution by leveraging initial residual information from the original low-resolution (LR) images. Extensive experiments show that DTformer achieves superior SR performance. Moreover, radiomics analysis reveals that improving the resolution of ADC images is beneficial for tumor characteristic prediction, such as histological grade and human epidermal growth factor receptor 2 (HER2) status.
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Zhang H, Niu S, Chen H, Wang L, Wang X, Wu Y, Shi J, Li Z, Hu Y, Yang Z, Jiang X. Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer. Front Cell Dev Biol 2024; 11:1220320. [PMID: 38264355 PMCID: PMC10804450 DOI: 10.3389/fcell.2023.1220320] [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: 05/12/2023] [Accepted: 12/18/2023] [Indexed: 01/25/2024] Open
Abstract
This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher's exact test, Student's t-test, and Mann-Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710-0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652-0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686-0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506-0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.
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Affiliation(s)
- Hongxiao Zhang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Shuxian Niu
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Lihua Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Yujiao Wu
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Jiaxin Shi
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Zhuoning Li
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Yanjun Hu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Zhiguang Yang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
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Sang L, Liu Z, Huang C, Xu J, Wang H. Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer. Clin Radiol 2024; 79:60-66. [PMID: 37838543 DOI: 10.1016/j.crad.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/28/2023] [Accepted: 09/12/2023] [Indexed: 10/16/2023]
Abstract
AIM To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics nomograms for predicting the hormone receptor (HR) status of HER2-positive breast cancer. MATERIALS AND METHODS Patients with HER2-positive invasive breast cancer were divided randomly into training (68 patients) and validation (30 patients) sets. All were classified as either HR-positive (HR+) or negative (HR-) at histopathology. Two radiologists outlined the three-dimensional (3D) volumetric regions of interest (VOI) on the MRI images. Features (n=1,096) were extracted from the T2-weighted imaging (WI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images separately. Dimensionality was reduced using feature screening. Binary radiomics prediction models were established using a logistic regression classifier and were validated in the validation set. To construct a nomogram, independent predictors were identified using multivariate logistic regression analysis. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS Ten radiomics features were obtained after feature dimensionality reduction based on the merged T2WI, ADC, and DCE images. The diagnostic efficacy of the radiomics signature using the three sequences was better than that of any single sequence (training set AUC: 0.797; validation set AUC: 0.75). Using multivariate logistic regression analysis, the independent predictors for identifying HR status were combined radiomics signature and peritumoural oedema. Nomograms constructed by combining the radiomics signature and peritumoural oedema showed good discrimination in both the training and validation sets (AUC: 0.815 and 0. 805, respectively). CONCLUSION A multiparametric MRI-based nomogram incorporating the radiomics signature and peritumoural oedema can assess the HR status of HER2-positive breast cancer. The resulting model can improve diagnostic accuracy, improving patient outcomes.
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Affiliation(s)
- L Sang
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China
| | - Z Liu
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China
| | - C Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co. Ltd, Beijing, China
| | - J Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co. Ltd, Beijing, China
| | - H Wang
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China.
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Bhalla K, Xiao Q, Luna JM, Podany E, Ahmad T, Ademuyiwa FO, Davis A, Bennett DL, Gastounioti A. Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward. BJR ARTIFICIAL INTELLIGENCE 2024; 1:ubae016. [PMID: 40201726 PMCID: PMC11974408 DOI: 10.1093/bjrai/ubae016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 09/27/2024] [Accepted: 11/10/2024] [Indexed: 04/10/2025]
Abstract
Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10%-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Towards this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumour visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumours that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics, and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive, and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions, and prognostic evaluations for TNBC.
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Affiliation(s)
- Kanika Bhalla
- Breast Image Computing Lab, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Qi Xiao
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - José Marcio Luna
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Emily Podany
- Division of Hematology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Division of Oncology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Tabassum Ahmad
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Foluso O Ademuyiwa
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Division of Oncology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Andrew Davis
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Division of Oncology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Debbie Lee Bennett
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Aimilia Gastounioti
- Breast Image Computing Lab, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
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Uncu UY, Aydin Aksu S. Correlation of Perfusion Metrics with Ki-67 Proliferation Index and Axillary Involvement as a Prognostic Marker in Breast Carcinoma Cases: A Dynamic Contrast-Enhanced Perfusion MRI Study. Diagnostics (Basel) 2023; 13:3260. [PMID: 37892081 PMCID: PMC10606869 DOI: 10.3390/diagnostics13203260] [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: 09/05/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
Our study aims to reveal clinically helpful prognostic markers using quantitative radiologic data from perfusion magnetic resonance imaging for patients with locally advanced carcinoma, using the Ki-67 index as a surrogate. Patients who received a breast cancer diagnosis and had undergone dynamic contrast-enhanced magnetic resonance imaging of the breast for pretreatment evaluation and follow-up were searched retrospectively. We evaluated the MRI studies for perfusion parameters and various categories and compared them to the Ki-67 index. Axillary involvement was categorized as low (N0-N1) or high (N2-N3) according to clinical stage. A total sum of 60 patients' data was included in this study. Perfusion parameters and Ki-67 showed a significant correlation with the transfer constant (Ktrans) (ρ = 0.554 p = 0.00), reverse transfer constant (Kep) (ρ = 0.454 p = 0.00), and initial area under the gadolinium curve (IAUGC) (ρ = 0.619 p = 0.00). The IAUGC was also significantly different between axillary stage groups (Z = 2.478 p = 0.013). Outside of our primary hypothesis, associations between axillary stage and contrast enhancement (x2 = 8.023 p = 0.046) and filling patterns (x2 = 8.751 p = 0.013) were detected. In conclusion, these parameters are potential prognostic markers in patients with moderate Ki-67 indices, such as those in our study group. The relationship between axillary status and perfusion parameters also has the potential to determine patients who would benefit from limited axillary dissection.
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Affiliation(s)
- Ulas Yalim Uncu
- Department of Radiology, Van Training and Research Hospital, University of Health Sciences, 65300 Van, Turkey
| | - Sibel Aydin Aksu
- Department of Radiology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, 34668 Istanbul, Turkey;
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Feng S, Yin J. Dynamic contrast-enhanced magnetic resonance imaging radiomics analysis based on intratumoral subregions for predicting luminal and nonluminal breast cancer. Quant Imaging Med Surg 2023; 13:6735-6749. [PMID: 37869317 PMCID: PMC10585575 DOI: 10.21037/qims-22-1073] [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: 10/06/2022] [Accepted: 08/14/2023] [Indexed: 10/24/2023]
Abstract
Background Breast cancer is a heterogeneous disease with different morphological and biological characteristics. The molecular subtypes of breast cancer are closely related to the treatment and prognosis of patients. In order to predict the luminal type of breast cancer in a noninvasive manner, our study developed and validated a radiomics nomogram combining clinical factors with a radiomics score based on the features of the intratumoral subregion to distinguish between luminal and nonluminal breast cancer. Methods From January 2018 to January 2020, 153 women with clinically and pathologically diagnosed breast cancer with an average age of 50.08 years were retrospectively analyzed. Using a semiautomatic segmentation method, the whole tumor was divided into 3 subregions on the basis of the time required for the contrast agent to reach its peak; 540 features were extracted from 3 subregions and the whole tumor region. Subsequently, 2 machine learning classifiers were developed. The least absolute shrinkage and selection operator method was used for feature selection and radiomics score (Rad-score) construction. Moreover, multivariable logistic regression analysis was applied to select independent factors from the Rad-score and clinical factors to establish a prediction model in the form of a nomogram. The performance of the nomogram was evaluated through calibration, discrimination, and clinical usefulness. Results The prediction performance of texture features from the rapid subregion was the best in the 3 intratumoral subregions, and the area under the receiver operating characteristic curve (AUC) values in the training and validation cohort were 0.805 (95% CI: 0.719-0.892) and 0.737 (95% CI: 0.581-0.893), respectively. The Rad-score, consisting of 5 features from the rapid subregion, was associated with the luminal type of breast cancer (P=0.001 and P=0.035 in the training and validation cohorts, respectively). The predictors included in the personalized prediction nomogram included Rad-score, human epidermal growth factor receptor 2 (HER2) status, and tumor histological grade. The nomogram showed good discrimination, with an area under the receiver operating characteristic curve in the training and validation cohorts of 0.830 (95% CI: 0.746-0.896) and 0.879 (95% CI: 0.748-0.957), respectively. The calibration curve of the 2 cohorts and decision curve analysis demonstrated that the nomogram had good calibration and clinical usefulness. Conclusions We proposed a nomogram model that combined clinical factors and Rad-score, which showed good performance in predicting the luminal type of breast cancer.
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Affiliation(s)
- Shuqian Feng
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Uysal E, Topaloğlu ÖF, Arı A, Özer H, Koplay M. Can magnetic resonance imaging texture analysis change the breast imaging reporting and data system category of breast lesions? Clin Imaging 2023; 97:44-49. [PMID: 36889114 DOI: 10.1016/j.clinimag.2023.02.016] [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: 08/13/2022] [Revised: 02/19/2023] [Accepted: 02/28/2023] [Indexed: 03/07/2023]
Abstract
PURPOSE This study aimed to reveal magnetic resonance imaging (MRI) texture analysis (TA)'s contribution to categorizing breast lesions according to the Breast Imaging-Reporting and Data System (BI-RADS) lexicon. METHOD Two hundred and seventeen women with BI-RADS category 3, 4, and 5 lesions on breast MRI were included in the study. For TA, the region of interest was drawn manually to encompass the entire lesion on the fat-suppressed T2W and the first post-contrast T1W images. To identify the independent predictors of breast cancer, multivariate logistic regression analyses were performed using texture parameters. Estimated benign and malignant groups were formed according to the TA regression model. RESULTS Texture parameters extracted from T2WI, including median, gray-level co-occurrence matrix (GLCM) contrast, GLCM correlation, GLCM joint entropy, GLCM sum entropy, and GLCM sum of squares, and parameters extracted from T1WI, including maximum, GLCM contrast, GLCM joint entropy, GLCM sum entropy, were independent predictors of breast cancer. In the estimated new groups according to the TA regression model, 19 (91%) of the benign 4a lesions were downgraded to BI-RADS category 3. CONCLUSIONS The addition of quantitative parameters obtained by MRI TA to BI-RADS criteria significantly increased the accuracy rate in differentiating benign and malignant breast lesions. When categorizing BI-RADS 4a lesions, the use of MRI TA in addition to conventional imaging findings may reduce unnecessary biopsy rates.
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Affiliation(s)
- Emine Uysal
- Department of Radiology, Faculty of Medicine, Selçuk University, Selçuklu, Konya, Turkey.
| | - Ömer Faruk Topaloğlu
- Department of Radiology, Faculty of Medicine, Selçuk University, Selçuklu, Konya, Turkey
| | - Ayşe Arı
- Department of Radiology, Faculty of Medicine, Selçuk University, Selçuklu, Konya, Turkey
| | - Halil Özer
- Department of Radiology, Faculty of Medicine, Selçuk University, Selçuklu, Konya, Turkey
| | - Mustafa Koplay
- Department of Radiology, Faculty of Medicine, Selçuk University, Selçuklu, Konya, Turkey
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Brancato V, Brancati N, Esposito G, La Rosa M, Cavaliere C, Allarà C, Romeo V, De Pietro G, Salvatore M, Aiello M, Sangiovanni M. A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:1552. [PMID: 36772592 PMCID: PMC9921618 DOI: 10.3390/s23031552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/13/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.
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Affiliation(s)
- Valentina Brancato
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Nadia Brancati
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Giusy Esposito
- Bio Check Up S.r.l., Via Riviera di Chiaia 9a, 80122 Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Massimo La Rosa
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Carlo Cavaliere
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Ciro Allarà
- Bio Check Up S.r.l., Via Riviera di Chiaia 9a, 80122 Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Giuseppe De Pietro
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
| | - Marco Salvatore
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Marco Aiello
- IRCCS SYNLAB SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy
| | - Mara Sangiovanni
- Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 111, 80131 Naples, Italy
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Classifying Breast Cancer Metastasis Based on Imaging of Tumor Primary and Tumor Biology. Diagnostics (Basel) 2023; 13:diagnostics13030437. [PMID: 36766541 PMCID: PMC9914718 DOI: 10.3390/diagnostics13030437] [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: 11/24/2022] [Revised: 01/14/2023] [Accepted: 01/21/2023] [Indexed: 01/27/2023] Open
Abstract
The molecular classification of breast cancer has allowed for a better understanding of both prognosis and treatment of breast cancer. Imaging of the different molecular subtypes has revealed that biologically different tumors often exhibit typical features in mammography, ultrasound, and MRI. Here, we introduce the molecular classification of breast cancer and review the typical imaging features of each subtype, examining the predictive value of imaging with respect to distant metastases.
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Bae MS. Impact of Molecular Subtype Definitions on AI Classification of Breast Cancer at MRI. Radiology 2023; 307:e223041. [PMID: 36594840 DOI: 10.1148/radiol.223041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Min Sun Bae
- From the Department of Radiology, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon 22332, Republic of Korea
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Lafcı O, Celepli P, Seher Öztekin P, Koşar PN. DCE-MRI Radiomics Analysis in Differentiating Luminal A and Luminal B Breast Cancer Molecular Subtypes. Acad Radiol 2023; 30:22-29. [PMID: 35595629 DOI: 10.1016/j.acra.2022.04.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 03/30/2022] [Accepted: 04/06/2022] [Indexed: 12/26/2022]
Abstract
RATIONALE AND OBJECTIVES The aim of the present study was to investigate the association between Luminal A and Luminal B molecular subtypes and radiomic features of dynamic contrast‑enhanced magnetic resonance imaging in patients with invasive breast cancer. MATERIALS AND METHODS Seventy-three patients with histopathologically proven invasive ductal cancer (IDC) were selected. Tumors were classified into molecular subtypes: Luminal A (estrogen receptor (ER)-positive and/or progesterone receptor (PR)-positive, human epidermal growth factor receptor type 2 (HER2) -negative, proliferation marker Ki-67<20) and Luminal B (ER-positive and/or PR-positive, HER2-positive or HER2-negative with high Ki-67 ≥20). A total of 81 tumoral lesions were evaluated on T1-weighted fat-suppressed sagittal post-contrast late-phase MRI images after the required "pre-process" steps and 3D segmentations were made. Forty-three radiomic features including: 1 conventional, 4 shape, 6 histogram, 7 Grey-Level Co-occurrence Matrix (GLCM), 11 Grey-Level Run-Length Matrix (GLRLM), 3 Neighborhood Grey-Level Difference Matrix (NGLDM), 11 Grey-Level Zone-Length Matrix (GLZLM) were extracted by using the software LIFEX. RESULTS A statistically significant difference was found in radiomic features including; a) Histogram: "skewness", b) Shape: "volume-ml, volume-voxel," c) GLCM: "entropy.log10, entropy.log2, energy", d) GLRLM: "GLNU, RLNU, HGRE," e) NGLDM: "busyness," f) GLZLM: "GLNU, HGZE, ZLNU, SZE" between two different molecular subtypes. The model combining Shape-volume (ml) and GLZLM-HGZE yielded 0.746 area under the curve (AUC), 0.744 sensitivity, 0.643 specificity and 0.694 accuracy. CONCLUSION Radiomic properties that may distinguish Luminal A and Luminal B molecular subtypes of IDC were identified. The radiomic features were thought to reflect the intratumoral heterogeneity in molecular subtypes. This study demonstrated that the characterization of Luminal A and Luminal B tumors could be made non-invasively by radiomics analysis.
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Affiliation(s)
- Oğuz Lafcı
- Department of Radiology, Ankara Training and Research Hospital, University of Health Sciences, Hacettepe Mh. Ulucanlar Cd. No:89 Altındağ, Ankara, Turkey.
| | - Pınar Celepli
- Department of Pathology, Ankara Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Pelin Seher Öztekin
- Department of Radiology, Ankara Training and Research Hospital, University of Health Sciences, Hacettepe Mh. Ulucanlar Cd. No:89 Altındağ, Ankara, Turkey
| | - Pınar Nercis Koşar
- Department of Radiology, Ankara Training and Research Hospital, University of Health Sciences, Hacettepe Mh. Ulucanlar Cd. No:89 Altındağ, Ankara, Turkey
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16
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Chen H, Lan X, Yu T, Li L, Tang S, Liu S, Jiang F, Wang L, Huang Y, Cao Y, Wang W, Wang X, Zhang J. Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study. Front Oncol 2022; 12:1076267. [PMID: 36644636 PMCID: PMC9837803 DOI: 10.3389/fonc.2022.1076267] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/01/2022] [Indexed: 12/31/2022] Open
Abstract
Introduction To develop and validate a radiogenomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer compared to a genomics and radiomics model. Methods This retrospective study integrated transcriptomic data from The Cancer Genome Atlas with matched MRI data from The Cancer Imaging Archive for the same set of 111 patients with breast cancer, which were used as the training and testing groups. Fifteen patients from one hospital were enrolled as the external validation group. Radiomics features were extracted from dynamic contrast-enhanced (DCE)-MRI of breast cancer, and genomics features were derived from differentially expressed gene analysis of transcriptome data. Boruta was used for genomics and radiomics data dimension reduction and feature selection. Logistic regression was applied to develop genomics, radiomics, and radiogenomics models to predict ALNM. The performance of the three models was assessed by receiver operating characteristic curves and compared by the Delong test. Results The genomics model was established by nine genomics features, and the radiomics model was established by three radiomics features. The two models showed good discrimination performance in predicting ALNM in breast cancer, with areas under the curves (AUCs) of 0.80, 0.67, and 0.52 for the genomics model and 0.72, 0.68, and 0.71 for the radiomics model in the training, testing and external validation groups, respectively. The radiogenomics model integrated with five genomics features and three radiomics features had a better performance, with AUCs of 0.84, 0.75, and 0.82 in the three groups, respectively, which was higher than the AUC of the radiomics model in the training group and the genomics model in the external validation group (both P < 0.05). Conclusion The radiogenomics model combining radiomics features and genomics features improved the performance to predict ALNM in breast cancer.
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Zhang Y, Li G, Bian W, Bai Y, He S, Liu Y, Liu H, Liu J. Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1394. [PMID: 36660694 PMCID: PMC9843333 DOI: 10.21037/atm-22-5986] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/20/2022] [Indexed: 01/01/2023]
Abstract
Background In the era of precision therapy, early classification of breast cancer (BRCA) molecular subtypes has clinical significance for disease management and prognosis. We explored the accuracy of machine learning (ML) models for early classification of BRCA molecular subtypes through a systematic review of the literature currently available. Methods We retrieved relevant studies published in PubMed, EMBASE, Cochrane, and Web of Science until 15 April 2022. A prediction model risk of bias assessment tool (PROBAST) was applied for the assessment of risk of bias of a genomics-based ML model, and the Radiomics Quality Score (RQS) was simultaneously used to evaluate the quality of this radiomics-based ML model. A random effects model was adopted to analyze the predictive accuracy of genomics-based ML and radiomics-based ML for Luminal A, Luminal B, Basal-like or triple-negative breast cancer (TNBC), and human epidermal growth factor receptor 2 (HER2). The PROSPERO of our study was prospectively registered (CRD42022333611). Results Of the 38 studies were selected for analysis, 14 ML models were based on gene-transcriptomic, with only 4 external validations; and 43 ML models were based on radiomics, with only 14 external validations. Meta-analysis results showed that c-statistic values of the ML based on radiomics for the identification of BRCA molecular subtypes Luminal A, Luminal B, Basal-like or TNBC, and HER2 were 0.76 [95% confidence interval (CI): 0.60-0.96], 0.78 (95% CI: 0.69-0.87), 0.89 (95% CI: 0.83-0.91), and 0.83 (95% CI: 0.81-0.86), respectively. The c-statistic values of ML based on the gene-transcriptomic analysis cohort for the identification of the previously described BRCA molecular subtypes were 0.96 (95% CI: 0.93-0.99), 0.96 (95% CI: 0.93-0.99), 0.98 (95% CI: 0.95-1.00), and 0.97 (95% CI: 0.96-0.98) respectively. Additionally, the sensitivity of the ML model based on radiomics for each molecular subtype ranged from 0.79 to 0.85, while the sensitivity of the ML model based on gene-transcriptomic was between 0.92 and 0.99. Conclusions Both radiomics and gene transcriptomics produced ideal effects on BRCA molecular subtype prediction. Compared with radiomics, gene transcriptomics yielded better prediction results, but radiomics was simpler and more convenient from a clinical point of view.
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Affiliation(s)
- Yiwen Zhang
- College of Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Guofeng Li
- Department of Traditional Chinese Medicine Surgery, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, China
| | - Wenqing Bian
- Intensive Care Unit, Zibo Maternal and Child Health Hospital, Zibo, China
| | - Yuzhuo Bai
- Department of Traditional Chinese Medicine Surgery, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, China
| | - Shuangyan He
- College of Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Yulian Liu
- Department of Colorectal & Anal Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, China
| | - Huan Liu
- College of Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Jiaqi Liu
- Department of Breast Thyroid Surgery, Zibo Central Hospital, Zibo, China
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Multiparametric MRI Features of Breast Cancer Molecular Subtypes. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121716. [PMID: 36556918 PMCID: PMC9785392 DOI: 10.3390/medicina58121716] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/01/2022] [Accepted: 11/15/2022] [Indexed: 11/25/2022]
Abstract
Background and Objectives: Breast cancer (BC) molecular subtypes have unique incidence, survival and response to therapy. There are five BC subtypes described by immunohistochemistry: luminal A, luminal B HER2 positive and HER2 negative, triple negative (TNBC) and HER2-enriched. Multiparametric breast MRI (magnetic resonance imaging) provides morphological and functional characteristics of breast tumours and is nowadays recommended in the preoperative setting. Aim: To evaluate the multiparametric MRI features (T2-WI, ADC values and DCE) of breast tumours along with breast density and background parenchymal enhancement (BPE) features among different BC molecular subtypes. Materials and Methods: This was a retrospective study which included 344 patients. All underwent multiparametric breast MRI (T2WI, ADC and DCE sequences) and features were extracted according to the latest BIRADS lexicon. The inter-reader agreement was assessed using the intraclass coefficient (ICC) between the ROI of ADC obtained from the two breast imagers (experienced and moderately experienced). Results: The study population was divided as follows: 89 (26%) with luminal A, 39 (11.5%) luminal B HER2 positive, 168 (48.5%) luminal B HER2 negative, 41 (12%) triple negative (TNBC) and 7 (2%) with HER2 enriched. Luminal A tumours were associated with special histology type, smallest tumour size and persistent kinetic curve (all p-values < 0.05). Luminal B HER2 negative tumours were associated with lowest ADC value (0.77 × 10−3 mm2/s2), which predicts the BC molecular subtype with an accuracy of 0.583. TNBC were associated with asymmetric and moderate/marked BPE, round/oval masses with circumscribed margins and rim enhancement (all p-values < 0.05). HER2 enriched BC were associated with the largest tumour size (mean 37.28 mm, p-value = 0.02). Conclusions: BC molecular subtypes can be associated with T2WI, ADC and DCE MRI features. ADC can help predict the luminal B HER2 negative cases.
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Le NQK, Ho DKN, Ta HDK, Nguyen HT. Using ensemble learning and genetic algorithm on magnetic resonance imaging radiomics to classify molecular subtypes of breast cancer. PRECISION MEDICAL SCIENCES 2022. [DOI: 10.1002/prm2.12089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine Taipei Medical University Taipei Taiwan
- Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan
- Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan
| | - Dang Khanh Ngan Ho
- School of Nutrition and Health Sciences, College of Nutrition Taipei Medical University Taipei Taiwan
| | - Hoang Dang Khoa Ta
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology Taipei Medical University and Academia Sinica Taipei Taiwan
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology Taipei Medical University Taipei Taiwan
| | - Hieu Trung Nguyen
- Department of Orthopedic and Trauma, Faculty of Medicine University of Medicine and Pharmacy at Ho Chi Minh City Ho Chi Minh City Vietnam
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Kong QC, Tang WJ, Chen SY, Hu WK, Hu Y, Liang YS, Zhang QQ, Cheng ZX, Huang D, Yang J, Guo Y. Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma. Front Oncol 2022; 12:916988. [PMID: 36212484 PMCID: PMC9533710 DOI: 10.3389/fonc.2022.916988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
Objectives Triple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC). Methods We evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method. Results Of the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29–65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28–88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750−0.949) in the training set and 0.819 (95% CI: 0.693−0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035). Conclusions In conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC.
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Affiliation(s)
- Qing-cong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wen-jie Tang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Si-yi Chen
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wen-ke Hu
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yue Hu
- Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yun-shi Liang
- Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Qiong-qiong Zhang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zi-xuan Cheng
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Di Huang
- Department of Breast Surgery, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
- *Correspondence: Di Huang, ; Jing Yang, ; Yuan Guo,
| | - Jing Yang
- Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
- *Correspondence: Di Huang, ; Jing Yang, ; Yuan Guo,
| | - Yuan Guo
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
- *Correspondence: Di Huang, ; Jing Yang, ; Yuan Guo,
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Sheng W, Xia S, Wang Y, Yan L, Ke S, Mellisa E, Gong F, Zheng Y, Tang T. Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning. Front Oncol 2022; 12:964605. [PMID: 36172153 PMCID: PMC9510620 DOI: 10.3389/fonc.2022.964605] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundMost studies of molecular subtype prediction in breast cancer were mainly based on two-dimensional MRI images, the predictive value of three-dimensional volumetric features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting breast cancer molecular subtypes has not been thoroughly investigated. This study aimed to look into the role of features derived from DCE-MRI and how they could be combined with clinical data to predict invasive ductal breast cancer molecular subtypes.MethodsFrom January 2019 to December 2021, 190 Chinese women with invasive ductal breast cancer were studied (32 triple-negative, 59 HER2-enriched, and 99 luminal lesions) in this institutional review board-approved retrospective cohort study. The image processing software extracted 1130 quantitative radiomic features from the segmented lesion area, including shape-based, first-order statistical, texture, and wavelet features. Three binary classifications of the subtypes were performed: triple-negative vs. non-triple-negative, HER2-overexpressed vs. non-HER2-overexpressed, and luminal (A + B) vs. non-luminal. For the classification, five machine learning methods (random forest, logistic regression, support vector machine, naïve Bayes, and eXtreme Gradient Boosting) were employed. The classifiers were chosen using the least absolute shrinkage and selection operator method. The area evaluated classification performance under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean.ResultsEXtreme Gradient Boosting model showed the best performance in luminal and non-luminal groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8282, 0.7524, 0.6542, 0.6964, 0.6086, 0.3458, 0.8524 and 0.7016, respectively. Meanwhile, the random forest model showed the best performance in HER2-overexpressed and non-HER2-overexpressed groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8054, 0.2941, 0.9744, 0.7679, 0.4348, 0.0256, 0.8333 and 0.5353, respectively. Furthermore, eXtreme Gradient Boosting model showed the best performance in the triple-negative and non-triple-negative groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.9031, 0.9362, 0.4444, 0.8571, 0.9167, 0.5556, 0.8980 and 0.6450.ConclusionClinical data and three-dimension imaging features from DCE-MRI were identified as potential biomarkers for distinguishing between three molecular subtypes of invasive ductal carcinomas breast cancer. In the future, more extensive studies will be required to evaluate the findings.
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Affiliation(s)
- Weiyong Sheng
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Shouli Xia
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yaru Wang
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lizhao Yan
- Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Songqing Ke
- Department of Science and Technology Research Management, Wuhan Blood Center, Wuhan, China
| | - Evelyn Mellisa
- Department of Emergency Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fen Gong
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yun Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Yun Zheng, ; Tiansheng Tang,
| | - Tiansheng Tang
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
- *Correspondence: Yun Zheng, ; Tiansheng Tang,
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22
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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23
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MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2703350. [PMID: 35845886 PMCID: PMC9282990 DOI: 10.1155/2022/2703350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/04/2022] [Accepted: 05/25/2022] [Indexed: 11/21/2022]
Abstract
Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.
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24
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Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14132966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images.
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25
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Xu A, Chu X, Zhang S, Zheng J, Shi D, Lv S, Li F, Weng X. Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study. Front Oncol 2022; 12:799232. [PMID: 35664741 PMCID: PMC9160981 DOI: 10.3389/fonc.2022.799232] [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: 11/04/2021] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods A total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established. Results Of the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively. Conclusion The DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.
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Affiliation(s)
- Aqiao Xu
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Xiufeng Chu
- Department of Surgical, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing Zheng
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Dabao Shi
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Shasha Lv
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Feng Li
- Department of Research Collaboration, Research & Development Center (R&D), Beijing Deepwise & League of Doctor of Philosophy (PHD) Technology Co., Ltd, Beijing, China
| | - Xiaobo Weng
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
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26
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Yin XX, Hadjiloucas S, Zhang Y, Tian Z. MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106510. [PMID: 34852935 DOI: 10.1016/j.cmpb.2021.106510] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 11/01/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper aims to overview multidimensional mining algorithms in relation to Magnetic Resonance Imaging (MRI) radiogenomics for computer aided detection and diagnosis of breast tumours. The work also aims to address a new problem in radiogenomics mining: how to combine structural radiomics information with non-structural genomics information for improving the accuracy and efficacy of Neoadjuvant Chemotherapy (NAC). METHODS This requires the automated extraction of parameters from non-structural breast radiomics data, and finding feature vectors with diagnostic value, which then are combined with genomics data. In order to address the problem of weakly labelled tumour images, a Generative Adiversarial Networks (GAN) based deep learning strategy is proposed for the classification of tumour types; this has significant potential for providing accurate real-time identification of tumorous regions from MRI scans. In order to efficiently integrate in a deep learning framework different features from radiogenomics datasets at multiple spatio-temporal resolutions, pyramid structured and multi-scale densely connected U-Nets are proposed. A bidirectional gated recurrent unit (BiGRU) combined with an attention based deep learning approach is also proposed. RESULTS The aim is to accurately predict NAC responses by combining imaging and genomic datasets. The approaches discussed incorporate some of the latest developments in of current signal processing and artificial intelligence and have significant potential in advancing and provide a development platform for future cutting-edge biomedical radiogenomics analysis. CONCLUSIONS The association of genotypic and phenotypic features is at the core of the emergent field of Precision Medicine. It makes use of advances in biomedical big data analysis, which enables the correlation between disease-associated phenotypic characteristics, genetics polymorphism and gene activation to be revealed.
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Affiliation(s)
- Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China.
| | - Sillas Hadjiloucas
- Department of Biomedical Engineering, The University of Reading, RG6 6AY, UK
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
| | - Zhihong Tian
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
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27
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The application of radiomics in predicting gene mutations in cancer. Eur Radiol 2022; 32:4014-4024. [DOI: 10.1007/s00330-021-08520-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/11/2021] [Accepted: 12/14/2021] [Indexed: 12/24/2022]
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Zheng H, Miao Q, Liu Y, Raman SS, Scalzo F, Sung K. Integrative Machine Learning Prediction of Prostate Biopsy Results From Negative Multiparametric MRI. J Magn Reson Imaging 2022; 55:100-110. [PMID: 34160114 PMCID: PMC8678175 DOI: 10.1002/jmri.27793] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Multiparametric MRI (mpMRI) is commonly recommended as a triage test prior to any prostate biopsy. However, there exists limited consensus on which patients with a negative prostate mpMRI could avoid prostate biopsy. PURPOSE To identify which patient could safely avoid prostate biopsy when the prostate mpMRI is negative, via a radiomics-based machine learning approach. STUDY TYPE Retrospective. SUBJECTS Three hundred thirty patients with negative prostate 3T mpMRI between January 2016 and December 2018 were included. FIELD STRENGTH/SEQUENCE A 3.0 T/T2-weighted turbo spin echo (TSE) imaging (T2 WI) and diffusion-weighted imaging (DWI). ASSESSMENT The integrative machine learning (iML) model was trained to predict negative prostate biopsy results, utilizing both radiomics and clinical features. The final study cohort comprised 330 consecutive patients with negative mpMRI (PI-RADS < 3) who underwent systematic transrectal ultrasound-guided (TRUS) or MR-ultrasound fusion (MRUS) biopsy within 6 months. A secondary analysis of biopsy naïve subcohort (n = 227) was also conducted. STATISTICAL TESTS The Mann-Whitney U test and Chi-Squared test were utilized to evaluate the significance of difference of clinical features between prostate biopsy positive and negative groups. The model performance was validated using leave-one-out cross-validation (LOOCV) and measured by AUC, sensitivity, specificity, and negative predictive value (NPV). RESULTS Overall, 306/330 (NPV 92.7%) of the final study cohort patients had negative biopsies, and 207/227 (NPV 91.2%) of the biopsy naïve subcohort patients had negative biopsies. Our iML model achieved NPVs of 98.3% and 98.0% for the study cohort and subcohort, respectively, superior to prostate-specific antigen density (PSAD)-based risk assessment with NPVs of 94.9% and 93.9%, respectively. DATA CONCLUSION The proposed iML model achieved high performance in predicting negative prostate biopsy results for patients with negative mpMRI. With improved NPVs, the proposed model can be used to stratify patients who in whom we might obviate biopsies, thus reducing the number of unnecessary biopsies. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Haoxin Zheng
- Radiological Sciences, University of California — Los Angeles, Los Angeles, CA 90095, USA,Computer Science, University of California — Los Angeles, Los Angeles, CA 90095, USA
| | - Qi Miao
- Radiological Sciences, University of California — Los Angeles, Los Angeles, CA 90095, USA,Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang City 110001, Liaoning Province, China
| | - Yongkai Liu
- Radiological Sciences, University of California — Los Angeles, Los Angeles, CA 90095, USA
| | - Steven S. Raman
- Radiological Sciences, University of California — Los Angeles, Los Angeles, CA 90095, USA
| | - Fabien Scalzo
- Computer Science, University of California — Los Angeles, Los Angeles, CA 90095, USA,Neurology, University of California — Los Angeles, Los Angeles, CA 90095, USA
| | - Kyunghyun Sung
- Radiological Sciences, University of California — Los Angeles, Los Angeles, CA 90095, USA
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Zhang C, Gu J, Zhu Y, Meng Z, Tong T, Li D, Liu Z, Du Y, Wang K, Tian J. AI in spotting high-risk characteristics of medical imaging and molecular pathology. PRECISION CLINICAL MEDICINE 2021; 4:271-286. [PMID: 35692858 PMCID: PMC8982528 DOI: 10.1093/pcmedi/pbab026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 02/07/2023] Open
Abstract
Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.
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Affiliation(s)
- Chong Zhang
- Department of Big Data Management and Application, School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jionghui Gu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangyang Zhu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tong Tong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongyang Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China
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30
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Niu S, Jiang W, Zhao N, Jiang T, Dong Y, Luo Y, Yu T, Jiang X. Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI. J Cancer Res Clin Oncol 2021; 148:97-106. [PMID: 34623517 DOI: 10.1007/s00432-021-03822-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 09/27/2021] [Indexed: 12/27/2022]
Abstract
PURPOSE This study aimed to investigate the efficacy of digital mammography (DM), digital breast tomosynthesis (DBT), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI separately and combined in the prediction of molecular subtypes of breast cancer. METHODS A total of 241 patients were enrolled and underwent breast MD, DBT, DW and DCE scans. Radiomics features were calculated from intra- and peritumoral regions, and selected with least absolute shrinkage and selection operator (LASSO) regression to develop radiomics signatures (RSs). Prediction performance of intra- and peritumoral regions in the four modalities were evaluated and compared with area under the receiver-operating characteristic (ROC) curve (AUC), specificity and sensitivity as comparison metrics. RESULTS The RSs derived from combined intra- and peritumoral regions improved prediction AUCs compared with those from intra- or peritumoral regions alone. DM plus DBT generated better AUCs than the DW plus DCE on predicting Luminal A and Luminal B in the training (Luminal A: 0.859 and 0.805; Luminal B: 0.773 and 0.747) and validation (Luminal A: 0.906 and 0.853; Luminal B: 0.807 and 0.784) cohort. For the prediction of HER2-enriched and TN, the DW plus DCE yielded better AUCs than the DM plus DBT in the training (HER2-enriched: 0.954 and 0.857; TN: 0.877 and 0.802) and validation (HER2-enriched: 0.974 and 0.907; TN: 0.938 and 0.874) cohort. CONCLUSIONS Peritumoral regions can provide complementary information to intratumoral regions for the prediction of molecular subtypes. Compared with MRI, the mammography showed higher AUCs for the prediction of Luminal A and B, but lower AUCs for the prediction of HER2-enriched and TN.
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Affiliation(s)
- Shuxian Niu
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Nannan Zhao
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Tao Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China.
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China.
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Yang Z, Chen X, Zhang T, Cheng F, Liao Y, Chen X, Dai Z, Fan W. Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes. Front Oncol 2021; 11:628824. [PMID: 34604024 PMCID: PMC8481692 DOI: 10.3389/fonc.2021.628824] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 08/30/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives To assess breast cancer receptor status and molecular subtypes by using the CAIPIRINHA-Dixon-TWIST-VIBE and readout-segmented echo-planar diffusion weighted imaging techniques. Methods A total of 165 breast cancer patients were retrospectively recruited. Patient age, estrogen receptor, progesterone receptor, human epidermal growth factorreceptor-2 (HER-2) status, and the Ki-67 proliferation index were collected for analysis. Quantitative parameters (Ktrans, Ve, Kep), semiquantitative parameters (W-in, W-out, TTP), and apparent diffusion coefficient (ADC) values were compared in relation to breast cancer receptor status and molecular subtypes. Statistical analysis were performed to compare the parameters in the receptor status and molecular subtype groups.Multivariate analysis was performed to explore confounder-adjusted associations, and receiver operating characteristic curve analysis was used to assess the classification performance and calculate thresholds. Results Younger age (<49.5 years, odds ratio (OR) =0.95, P=0.004), lower Kep (<0.704,OR=0.14, P=0.044),and higher TTP (>0.629 min, OR=24.65, P=0.011) were independently associated with progesterone receptor positivity. A higher TTP (>0.585 min, OR=28.19, P=0.01) was independently associated with estrogen receptor positivity. Higher Kep (>0.892, OR=11.6, P=0.047), lower TTP (<0.582 min, OR<0.001, P=0.004), and lower ADC (<0.719 ×10-3 mm2/s, OR<0.001, P=0.048) had stronger independent associations with triple-negative breast cancer (TNBC) compared to luminal A, and those parameters could differentiate TNBC from luminal A with the highest AUC of 0.811. Conclusions Kep and TTP were independently associated with hormone receptor status. In addition, the Kep, TTP, and ADC values had stronger independent associations with TNBC than with luminal A and could be used as imaging biomarkers for differentiate TNBC from Luminal A.
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Affiliation(s)
- Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, China.,Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, China
| | - Xiaofeng Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, China.,Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, China
| | - Tianhui Zhang
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
| | - Fengyan Cheng
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
| | - Yuting Liao
- Pharmaceutical Diagnostics, GE Healthcare, Guangzhou, China
| | - Xiangguan Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, China.,Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, China
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, Shantou, China
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
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Xie T, Zhao Q, Fu C, Grimm R, Gu Y, Peng W. Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm). Eur Radiol 2021; 32:1634-1643. [PMID: 34505195 DOI: 10.1007/s00330-021-08244-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 07/21/2021] [Accepted: 08/03/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVES To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm). METHODS This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis. RESULTS For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001). CONCLUSIONS Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI. KEY POINTS • For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, People's Republic of China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
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Bitencourt A, Daimiel Naranjo I, Lo Gullo R, Rossi Saccarelli C, Pinker K. AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021; 142:109882. [PMID: 34392105 PMCID: PMC8387447 DOI: 10.1016/j.ejrad.2021.109882] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/15/2021] [Accepted: 07/26/2021] [Indexed: 12/22/2022]
Abstract
Significant advances in imaging analysis and the development of high-throughput methods that can extract and correlate multiple imaging parameters with different clinical outcomes have led to a new direction in medical research. Radiomics and artificial intelligence (AI) studies are rapidly evolving and have many potential applications in breast imaging, such as breast cancer risk prediction, lesion detection and classification, radiogenomics, and prediction of treatment response and clinical outcomes. AI has been applied to different breast imaging modalities, including mammography, ultrasound, and magnetic resonance imaging, in different clinical scenarios. The application of AI tools in breast imaging has an unprecedented opportunity to better derive clinical value from imaging data and reshape the way we care for our patients. The aim of this study is to review the current knowledge and future applications of AI-enhanced breast imaging in clinical practice.
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Affiliation(s)
- Almir Bitencourt
- Department of Imaging, A.C.Camargo Cancer Center, Sao Paulo, SP, Brazil; Dasa, Sao Paulo, SP, Brazil
| | - Isaac Daimiel Naranjo
- Department of Radiology, Breast Imaging Service, Guy's and St. Thomas' NHS Trust, Great Maze Pond, London, UK
| | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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You C, Zhang Y, Chen Y, Hu X, Hu D, Wu J, Gu Y, Peng W. Evaluation of Background Parenchymal Enhancement and Histogram-Based Diffusion-Weighted Image in Determining the Molecular Subtype of Breast Cancer. J Comput Assist Tomogr 2021; 45:711-716. [PMID: 34546678 DOI: 10.1097/rct.0000000000001239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
RATIONALE AND OBJECTIVES This study aimed to evaluate the value of background parenchymal enhancement (BPE) and diffusion-weighted image (DWI) histogram features in differentiating among different molecular subtypes of breast cancers and investigate the relationship between BPE and DWI features. MATERIALS AND METHODS We prospectively enrolled 142 patients with breast cancer between January and November 2018. All patients underwent breast magnetic resonance imaging before core needle biopsy. The quantitative BPE from dynamic enhanced images and the first-order histogram features extracted from DWI were analyzed. Univariate analysis of variance was used to compare differences in DWI histogram features and BPE characteristics among different molecular subtypes. Spearman test was used to compare the correlation between these imaging indexes. RESULTS A total of 142 patients had 142 lesions, including 17 cases of triple-negative breast cancer, 12 cases of luminal A type breast cancer, 39 cases of luminal B type breast cancer, and 74 cases of human epidermal growth factor receptor 2-positive breast cancer. The apparent diffusion coefficient (ADC) 95th percentile, ADC kurtosis, and BPE were significantly different among 4 subtype groups (P < 0.05), especially between the triple-negative subtype and any other subtype (P < 0.05 in pairwise comparisons). There was a weak but significant correlation between BPE and kurtosis of ADC (r = -0.176, P = 0.036). CONCLUSIONS Diffusion-weighted image histogram features (95th percentile ADC value and kurtosis value of ADC) and BPE features were different in the 4 molecular subtypes of breast cancer, especially in the triple-negative breast cancer subtype. Background parenchymal enhancement was negatively correlated with the kurtosis value of ADC.
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Affiliation(s)
- Chao You
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Yunyan Zhang
- Department of Radiology, Shanghai Proton and Heavy Ion Center
| | - Yanqiong Chen
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Xiaoxin Hu
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Danting Hu
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Jiong Wu
- Department of Breast Surgery, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China
| | - Yajia Gu
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Weijun Peng
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
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Iima M, Kataoka M, Honda M, Ohashi A, Ohno Kishimoto A, Ota R, Uozumi R, Urushibata Y, Feiweier T, Toi M, Nakamoto Y. The Rate of Apparent Diffusion Coefficient Change With Diffusion Time on Breast Diffusion-Weighted Imaging Depends on Breast Tumor Types and Molecular Prognostic Biomarker Expression. Invest Radiol 2021; 56:501-508. [PMID: 33660629 DOI: 10.1097/rli.0000000000000766] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
INTRODUCTION The aim of this study was to investigate the variation of apparent diffusion coefficient (ADC) values with diffusion time according to breast tumor type and prognostic biomarkers expression. MATERIALS AND METHODS A total of 201 patients with known or suspected breast tumors were prospectively enrolled in this study, and 132 breast tumors (86 malignant and 46 benign) were analyzed. Diffusion-weighted imaging scans with 2 diffusion times were acquired on a clinical 3-T magnetic resonance imaging scanner using oscillating and pulsed diffusion-encoding gradients (effective diffusion times, 4.7 and 96.6 milliseconds) and b values of 0 and 700 s/mm2. Diagnostic performances to differentiate malignant and benign breast tumors for ADC values at short and long diffusion times (ADCshort and ADClong), ΔADC (the rate of change in ADC values with diffusion time), ADC0-1000 (ADC value from a standard protocol), and standard reading including dynamic contrast-enhanced magnetic resonance imaging (BI-RADS) were investigated. The correlations of ADCshort, ADClong, and ΔADC values with hormone receptor expression and breast cancer subtypes were also analyzed. RESULTS The ADC values were lower, and ΔADC was higher in malignant tumors compared with benign tumors. The specificity of ADC values at all diffusion times and ΔADC values for differentiating malignant and benign breast tumors was superior to that of BI-RADS (87.0%-95.7% vs 73.9%), whereas the sensitivity was inferior (87.2%-90.7% vs 100%). Lower ADCshort and ADC0-1000 in ER-positive compared with ER-negative cancers (false discovery rate [FDR]-adjusted P = 0.037 and 0.018, respectively) and lower ADCshort, ADClong, and ADC0-1000 in progesterone receptor-positive compared with progesterone receptor-negative cancers (FDR-adjusted P = 0.037, 0.036, and 0.018, respectively) were found. Ki-67-positive cancers had larger ΔADCs than Ki-67-negative cancers (FDR-adjusted P = 0.018). CONCLUSIONS The ADC values vary with different diffusion time and vary in correlation with molecular biomarkers, especially Ki-67. Those results suggest that the diffusion time, which should be reported, might be a useful parameter to consider for breast cancer management.
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Affiliation(s)
| | - Masako Kataoka
- From the Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Maya Honda
- From the Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | | | | | - Rie Ota
- From the Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Ryuji Uozumi
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | | | - Masakazu Toi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuji Nakamoto
- From the Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
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A radiomic signature model to predict the chemoradiation-induced alteration in tumor-infiltrating CD8 + cells in locally advanced rectal cancer. Radiother Oncol 2021; 162:124-131. [PMID: 34265357 DOI: 10.1016/j.radonc.2021.07.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 07/02/2021] [Accepted: 07/03/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND PURPOSE Regarding the altered tumor immune status following cytotoxic treatment, this study aims to develop a radiomic signature to predict CD8+ tumor-infiltrating lymphocyte (TIL) density changes in chemoradiotherapy (CRT) of rectal cancer. MATERIALS AND METHODS We used the magnetic resonance imaging (MRI) and immunohistochemistry data before and after neoadjuvant CRT. The discovery datasets consisted of pre-CRT dataset A1 (n = 113), post-CRT datasets A2 (n = 32; predominance of tumor) and A3 (n = 20; pure fibrosis). The developed model was validated in dataset B (n = 28). Thirty-eight radiomic features from T2-weighted MRI scans were incorporated into the least absolute shrinkage and selection operator method. RESULTS In pre-CRT dataset A1, the area under the receiver operating characteristic curve (AUC) values of radiomic score for predicting CD8+ TILs were 0.760 and 0.729 for training and validation subsets, respectively. A significant correlation was observed between the signature and CD8+ TIL density in the post-CRT dataset A2 (Pearson's R = -0.372, P = 0.036), whereas no association was found in dataset A3 (Pearson's R = -0.069, P = 0.77). The association was also observed in the validation dataset B (Pearson's R = -0.374, P = 0.049). In dataset A2, the radiomic score difference predicted changes in CD8+ TIL density (AUC = 0.824). CONCLUSION We established the MRI-derived radiomic signature for predicting CRT-induced alterations in CD8+ TILs. This study suggests the clinical utility of radiomics-immunophenotype modeling to evaluate tumor immune status following neoadjuvant chemoradiation in rectal cancer.
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Beheshtirouy S, Mirzaei F, Eyvazi S, Tarhriz V. Recent Advances in Therapeutic Peptides for Breast Cancer Treatment. Curr Protein Pept Sci 2021; 22:74-88. [PMID: 33208071 DOI: 10.2174/1389203721999201117123616] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/22/2020] [Accepted: 10/28/2020] [Indexed: 11/22/2022]
Abstract
Breast cancer is a heterogeneous malignancy and is the second leading cause of mortality among women around the world. Increasing the resistance to anti-cancer drugs in breast cancer cells persuades researchers to search the novel therapeutic approaches for the treatment of this malignancy. Among the novel methods, therapeutic peptides that target and disrupt tumor cells have been of great interest. Therapeutic peptides are short amino acid monomer chains with high specificity to bind and modulate a protein interaction of interest. Several advantages of peptides, such as specific binding on tumor cells surface, low molecular weight, and low toxicity on normal cells, make the peptides appealing therapeutic agents against solid tumors, particularly breast cancer. Also, the National Institutes of Health (NIH) describes therapeutic peptides as a suitable candidate for the treatment of drug-resistant breast cancer. In this review, we attempt to review the different therapeutic peptides against breast cancer cells that can be used in the treatment and diagnosis of the malignancy. Meanwhile, we presented an overview of peptide vaccines that have been developed for the treatment of breast cancer.
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Affiliation(s)
- Samad Beheshtirouy
- Department of Cardiothoracic Surgery, Imam Reza Hospital, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Farhad Mirzaei
- Department of Neurosurgery, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Shirin Eyvazi
- Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Vahideh Tarhriz
- Molecular Medicine Research Center, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
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丁 妍, 韩 梦, 刘 月. [AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2021; 52:162-165. [PMID: 33829685 PMCID: PMC10408927 DOI: 10.12182/20210360102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Indexed: 11/23/2022]
Abstract
One of the most important application of artificial intelligence (AI) in pathology is prediction, using morphological features, of patient prognosis and response to specific treatments. As one of the most common kinds of malignancies in the world and the crucial important cause of death due to malignant tumor among women, breast cancer has become the center of attention in clinical services. Axillary lymph node metastasis is an important prognostic factor in breast cancer. The accuracy of the assessment of axillary lymph node metastasis bears heavily on clinical diagnosis and treatment. At present, based on the principle of non-invasive procedures, many studies have been done to develop models that can be used to predict sentinel lymph node metastasis of breast cancer. However, different clinical and pathological parameters are used in these predictive models. How to analyze the clinical and pathological data of breast cancer patients in a more comprehensive way and how to establish a prediction model with better precision have become the future direction of development. In this paper, we describe the research progress of AI in pathology and the current status of its use in breast cancer research. We have conducted in-depth reflection and looked into the future of ways to predict effectively breast cancer lymph node metastasis and to establish more accurate and effective deep-learning algorithm based on AI assistance so as to continuously improve the diagnosis and treatment of breast cancer.
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Affiliation(s)
- 妍 丁
- 河北医科大学第四医院 病理科 （石家庄 050011）Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - 梦雪 韩
- 河北医科大学第四医院 病理科 （石家庄 050011）Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - 月平 刘
- 河北医科大学第四医院 病理科 （石家庄 050011）Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
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Differences in tumour heterogeneity based on dynamic contrast-enhanced MRI between tumour and peritumoural stroma for predicting Ki-67 status of invasive ductal carcinoma. Clin Radiol 2021; 76:470.e13-470.e22. [PMID: 33648758 DOI: 10.1016/j.crad.2020.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 12/18/2020] [Indexed: 10/22/2022]
Abstract
AIM To evaluate and compare the heterogeneity of intratumour and peritumour areas in the prediction of Ki-67 of invasive ductal carcinoma (IDC) and the predictive accuracy of different contrast frames based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS This study included 88 patients with histologically confirmed IDC with 57 patients with high Ki-67 status and 31 patients with low Ki-67 status. All patients underwent DCE-MRI before surgery. A grey-level co-occurrence matrix (GLCM) was performed on slice-matched images from six frames by drawing the region of the interest (ROI) on the inner and outer regions of the tumours. The correlations between texture characteristics and Ki-67 status of lesions were analysed, using the Mann-Whitney test and receiver operating characteristic curve analysis. RESULTS In the high-Ki-67 group, the entropy was significantly higher than that of the low-Ki-67 group (p<0.001). The entropy obtained, based on the tumour boundary as a band-like area inside and outside at the first post-contrast series, revealed the highest receiver operating characteristic (AUC = 0.765). In the multivariate analysis, a higher entropy value (>4.305; p<0.001) remained independently associated with a high-Ki-67 status after adjustment for menopausal status, tumour size, histologic grade, oestrogen receptor (ER) status, and progesterone receptor (PR) status. The other parameters did not show significant differences between the high- and low-Ki-67 groups. CONCLUSION Heterogeneity analysis based on DCE-MRI could discriminate between high- and low-Ki-67 status. Texture characteristics from the band-like region inside and outside the tumour boundary could predict the Ki-67 status and showed higher accuracy.
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Leithner D, Bernard-Davila B, Martinez DF, Horvat JV, Jochelson MS, Marino MA, Avendano D, Ochoa-Albiztegui RE, Sutton EJ, Morris EA, Thakur SB, Pinker K. Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes. Mol Imaging Biol 2021; 22:453-461. [PMID: 31209778 PMCID: PMC7062654 DOI: 10.1007/s11307-019-01383-w] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Purpose To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. Procedures In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. Results For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). Conclusions Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.
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Affiliation(s)
- Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Blanca Bernard-Davila
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Joao V Horvat
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Maria Adele Marino
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Daly Avendano
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Nuevo Leon, Mexico
| | - R Elena Ochoa-Albiztegui
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth J Sutton
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, 7th Floor, New York, NY, 10065, USA. .,Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging Service, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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Son J, Lee SE, Kim EK, Kim S. Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis. Sci Rep 2020; 10:21566. [PMID: 33299040 PMCID: PMC7726048 DOI: 10.1038/s41598-020-78681-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 11/26/2020] [Indexed: 12/23/2022] Open
Abstract
We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.
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Affiliation(s)
- Jinwoo Son
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Si Eun Lee
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Eun-Kyung Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| | - Sungwon Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
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Huang Y, Zheng C, Zhang X, Cheng Z, Yang Z, Hao Y, Shen J. The Usefulness of Bayesian Network in Assessing the Risk of Triple-Negative Breast Cancer. Acad Radiol 2020; 27:e282-e291. [PMID: 32035756 DOI: 10.1016/j.acra.2019.12.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 12/12/2019] [Accepted: 12/25/2019] [Indexed: 12/31/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate a Bayesian network (BN) model learned from epidemiological and clinical information, and various MRI parameters for predicting the risk of triple-negative breast cancer (TNBC). MATERIALS AND METHODS For this retrospective study, 214 women (mean age ± standard deviation, 50.5±10.6 years) with breast cancer were included between April 2016 and April 2018. All patients underwent MRI, including dynamic contrast-enhanced (DCE)-MRI. The morphologic MRI features, the pattern of the time-signal intensity curve (TIC) and the kinetic parameters were obtained for each lesion. The epidemiological and clinical parameters and those imaging parameters were used to construct BN model to estimate TNBC risk. ROC curves upon probability estimates were used to determine the performance of the BN using area under the ROC curves (Az), sensitivity, specificity, and accuracy. RESULTS A BN model consisted of 16 epidemiological and clinical characteristics, morphologic MRI features, and quantitative DCE-MRI parameters were established. The posttest probability table showed that patients with age <35 years, mass-like lesions, type I TIC, and MaxCon ≥ 0.186 were at the highest risk of TNBC. The constructed BN model had an Az of 0.663 (95% confidence interval [CI]: 0.654, 0.672), sensitivity of 0.660 (95% CI: 0.644, 0.675), specificity of 0.740 (95% CI: 0.726, 0.753) and accuracy of 0.724 (95% CI: 0.714, 0.733) in classifying TNBC. CONCLUSION The BN model integrating epidemiological and clinical characteristics, morphologic and kinetic MRI parameters provide a noninvasive analytical approach for preoperative prediction of the risk of TNBC.
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Affiliation(s)
- Yun Huang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan II Road, Guangzhou 510080, China
| | - Chushan Zheng
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China
| | - Ziliang Cheng
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China
| | - Yuantao Hao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan II Road, Guangzhou 510080, China
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China.
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Wang Q, Mao N, Liu M, Shi Y, Ma H, Dong J, Zhang X, Duan S, Wang B, Xie H. Radiomic analysis on magnetic resonance diffusion weighted image in distinguishing triple-negative breast cancer from other subtypes: a feasibility study. Clin Imaging 2020; 72:136-141. [PMID: 33242692 DOI: 10.1016/j.clinimag.2020.11.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 09/02/2020] [Accepted: 11/12/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE This work aimed to explore whether radiomic features on magnetic resonance diffusion weighted image (MR DWI) can be used to identify triple-negative breast cancer (TNBC) and other subtypes (non-TNBC). MATERIALS AND METHODS This retrospective study included 221 unilateral patients who underwent breast MR imaging prior to neoadjuvant chemotherapy. The subtypes of breast cancer include luminal A (n = 63), luminal B (n = 103), human epidermal growth factor receptor-2 (HER2) overexpressing (n = 30), and triple negative (n = 25). Radiomic features were extracted using Omini-Kinetic software on DWI. Student's t-test and Mann-Whitney U test were used to compare the features between TNBC and non-TNBC patients. Logistic regression analysis and receiver operating characteristic (ROC) curve were used to evaluate the diagnostic efficiency of radiomic features. The Fisher discriminant model was employed to distinguish TNBC and non-TNBC patients automatically. An additional validation dataset with 169 patients was utilized to validate the model. RESULTS A total of 76 imaging features were extracted from each lesion on DWI images, and 12 radiomic features were statistically significant between TNBC and non-TNBC patients (P < 0.05). The area of receiver operating characteristic curve (AUC) was 0.817 to apply logistic regression analysis. The accuracy of Fisher discriminant model in distinguishing TNBC and non-TNBC patients was 95.4%, and leave-one-out cross validation was achieved with an accuracy of 83.7%. The same classification analysis of the validation dataset showed an accuracy of 83.4% and an AUC of 0.804. CONCLUSION Breast lesions exhibit differences in radiomic features from DWI, enabling good discrimination between TNBC and non-TNBC tumors.
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Affiliation(s)
- Qinglin Wang
- Department of Radiology, Yantai Yuhuangding Hospital, QingDao University School of Medicine, Yantai, Shandong 264000, PR China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, QingDao University School of Medicine, Yantai, Shandong 264000, PR China
| | - Meijie Liu
- Institute of medical imaging, Binzhou Medical University, Yantai, Shandong 264000, PR China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, QingDao University School of Medicine, Yantai, Shandong 264000, PR China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, QingDao University School of Medicine, Yantai, Shandong 264000, PR China
| | - Jianjun Dong
- Department of Radiology, Yantai Yuhuangding Hospital, QingDao University School of Medicine, Yantai, Shandong 264000, PR China
| | | | | | - Bin Wang
- Institute of medical imaging, Binzhou Medical University, Yantai, Shandong 264000, PR China.
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, QingDao University School of Medicine, Yantai, Shandong 264000, PR China.
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Arefan D, Chai R, Sun M, Zuley ML, Wu S. Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features. Med Phys 2020; 47:6334-6342. [PMID: 33058224 DOI: 10.1002/mp.14538] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 08/07/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
PURPOSE The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of two-dimensional (2D) and three-dimensional (3D) analysis. METHODS A retrospective study including 154 breast cancer patients all confirmed by pathology; 80 with ALN metastasis and 74 without. All MRI scans were achieved at a 3.0 Tesla scanner with 7 post-contrast MR phases sequentially acquired with a temporal resolution of 60 s. MRI radiomic features were extracted separately from a 2D single slice (i.e., the representative slice) and the 3D tumor volume. Several machine learning classifiers were built and compared using 2D or 3D analysis to distinguish positive vs negative ALN status. We performed independent test and 10-fold cross validation with multiple repetitions, and used bootstrap test, least absolute shrinkage selection operator, and receiver operating characteristic (ROC) curve analysis as statistical tests. RESULTS The highest area under the ROC curve (AUC) was 0.81 (95% confidence intervals [CI]: 0.80-0.83) and 0.82 (95% CI: 0.81-0.82) for 2D and 3D analysis, respectively; the corresponding accuracy was 79% and 80%. The linear discriminant analysis (LDA) classifier achieved the highest classification performance. None of the AUC differences between 2D and 3D analysis was statistically significant for the several tested machine learning classifiers (all P> 0.05). CONCLUSIONS Radiomic features from segmented tumor region in breast MRI were associated with ALN status. The separate radiomic analysis on 3D tumor volume showed a similar effect to the 2D analysis on the single representative slice in the tested machine learning classifiers.
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Affiliation(s)
- Dooman Arefan
- Department of Radiology, University of Pittsburgh, School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Ruimei Chai
- Department of Radiology, First Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Min Sun
- UPMC Hillman Cancer Center at St. Margaret, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15215, USA
| | - Margarita L Zuley
- Department of Radiology, University of Pittsburgh, School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.,Magee-Womens Hospital of University of Pittsburgh Medical Center, 300 Halket St, Pittsburgh, PA, 15213, USA
| | - Shandong Wu
- Departments of Radiology of Biomedical Informatics of Bioengineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
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Veeraraghavan H, Friedman CF, DeLair DF, Ninčević J, Himoto Y, Bruni SG, Cappello G, Petkovska I, Nougaret S, Nikolovski I, Zehir A, Abu-Rustum NR, Aghajanian C, Zamarin D, Cadoo KA, Diaz LA, Leitao MM, Makker V, Soslow RA, Mueller JJ, Weigelt B, Lakhman Y. Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers. Sci Rep 2020; 10:17769. [PMID: 33082371 PMCID: PMC7575573 DOI: 10.1038/s41598-020-72475-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 08/25/2020] [Indexed: 12/13/2022] Open
Abstract
To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) can identify DNA mismatch repair deficient (MMR-D) and/or tumor mutational burden-high (TMB-H) endometrial cancers (ECs). Patients who underwent targeted massively parallel sequencing of primary ECs between 2014 and 2018 and preoperative CE-CT were included (n = 150). Molecular subtypes of EC were assigned using DNA polymerase epsilon (POLE) hotspot mutations and immunohistochemistry-based p53 and MMR protein expression. TMB was derived from sequencing, with > 15.5 mutations-per-megabase as a cut-point to define TMB-H tumors. After radiomic feature extraction and selection, radiomic features and clinical variables were processed with the recursive feature elimination random forest classifier. Classification models constructed using the training dataset (n = 105) were then validated on the holdout test dataset (n = 45). Integrated radiomic-clinical classification distinguished MMR-D from copy number (CN)-low-like and CN-high-like ECs with an area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI 0.58–0.91). The model further differentiated TMB-H from TMB-low (TMB-L) tumors with an AUROC of 0.87 (95% CI 0.73–0.95). Peritumoral-rim radiomic features were most relevant to both classifications (p ≤ 0.044). Radiomic analysis achieved moderate accuracy in identifying MMR-D and TMB-H ECs directly from CE-CT. Radiomics may provide an adjunct tool to molecular profiling, especially given its potential advantage in the setting of intratumor heterogeneity.
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Affiliation(s)
- Harini Veeraraghavan
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Claire F Friedman
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Deborah F DeLair
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Pathology, NYU Langone Medical Center, New York, NY, USA
| | - Josip Ninčević
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Radiology, Sisters of Charity Hospital, Zagreb, Croatia
| | - Yuki Himoto
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Diagnostic Radiology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Silvio G Bruni
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Radiology, Trillium Health Partners, Mississauga, ON, Canada
| | - Giovanni Cappello
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
| | - Iva Petkovska
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Stephanie Nougaret
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Radiology, Institute of Cancer Research of Montpellier (IRCM), INSERM U1194, Montpellier, France.,Department of Radiology, Montpellier Cancer Institute, University of Montpellier, Montpellier, France
| | - Ines Nikolovski
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ahmet Zehir
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nadeem R Abu-Rustum
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Carol Aghajanian
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Dmitriy Zamarin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Karen A Cadoo
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Luis A Diaz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Mario M Leitao
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Vicky Makker
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Robert A Soslow
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jennifer J Mueller
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Britta Weigelt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yulia Lakhman
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers. Eur Radiol 2020; 31:2559-2567. [PMID: 33001309 DOI: 10.1007/s00330-020-07274-x] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/27/2020] [Accepted: 09/09/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI. METHODS A total of 244 patients were analyzed, 99 in training dataset scanned at 1.5 T and 83 in testing-1 and 62 in testing-2 scanned at 3 T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2-), HER2+, and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the training dataset, by using a conventional CNN and the convolutional long short-term memory (CLSTM). Then, transfer learning was applied to re-tune the model using testing-1(2) and evaluated in testing-2(1). RESULTS In the training dataset, the mean accuracy evaluated using tenfold cross-validation was higher by using CLSTM (0.91) than by using CNN (0.79). When the developed model was applied to the independent testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%. CONCLUSIONS The recurrent network using CLSTM could track changes in signal intensity during DCE acquisition, and achieved a higher accuracy compared with conventional CNN during training. For datasets acquired using different settings, transfer learning can be applied to re-tune the model and improve accuracy. KEY POINTS • Deep learning can be applied to differentiate breast cancer molecular subtypes. • The recurrent neural network using CLSTM could track the change of signal intensity in DCE images, and achieved a higher accuracy compared with conventional CNN during training. • For datasets acquired using different scanners with different imaging protocols, transfer learning provided an efficient method to re-tune the classification model and improve accuracy.
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47
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Fan M, Liu Z, Xu M, Wang S, Zeng T, Gao X, Li L. Generative adversarial network-based super-resolution of diffusion-weighted imaging: Application to tumour radiomics in breast cancer. NMR IN BIOMEDICINE 2020; 33:e4345. [PMID: 32521567 DOI: 10.1002/nbm.4345] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/19/2020] [Accepted: 05/14/2020] [Indexed: 06/11/2023]
Abstract
Diffusion-weighted imaging (DWI) is increasingly used to guide the clinical management of patients with breast tumours. However, accurate tumour characterization with DWI and the corresponding apparent diffusion coefficient (ADC) maps are challenging due to their limited resolution. This study aimed to produce super-resolution (SR) ADC images and to assess the clinical utility of these SR images by performing a radiomic analysis for predicting the histologic grade and Ki-67 expression status of breast cancer. To this end, 322 samples of dynamic enhanced magnetic resonance imaging (DCE-MRI) and the corresponding DWI data were collected. A SR generative adversarial (SRGAN) and an enhanced deep SR (EDSR) network along with the bicubic interpolation were utilized to generate SR-ADC images from which radiomic features were extracted. The dataset was randomly separated into a development dataset (n = 222) to establish a deep SR model using DCE-MRI and a validation dataset (n = 100) to improve the resolution of ADC images. This random separation of datasets was performed 10 times, and the results were averaged. The EDSR method was significantly better than the SRGAN and bicubic methods in terms of objective quality criteria. Univariate and multivariate predictive models of radiomic features were established to determine the area under the receiver operating characteristic curve (AUC). Individual features from the tumour SR-ADC images showed a higher performance with the EDSR and SRGAN methods than with the bicubic method and the original images. Multivariate analysis of the collective radiomics showed that the EDSR- and SRGAN-based SR-ADC images performed better than the bicubic method and original images in predicting either Ki-67 expression levels (AUCs of 0.818 and 0.801, respectively) or the tumour grade (AUCs of 0.826 and 0.828, respectively). This work demonstrates that in addition to improving the resolution of ADC images, deep SR networks can also improve tumour image-based diagnosis in breast cancer.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Zuhui Liu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, Hangzhou, China
| | - Shiwei Wang
- Department of Radiology, First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, Hangzhou, China
| | - Tieyong Zeng
- Department of Mathematics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
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Orlando A, Dimarco M, Cannella R, Bartolotta TV. Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art. Artif Intell Med Imaging 2020; 1:6-18. [DOI: 10.35711/aimi.v1.i1.6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/17/2020] [Accepted: 06/19/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.
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Affiliation(s)
- Alessia Orlando
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Mariangela Dimarco
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Roberto Cannella
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Tommaso Vincenzo Bartolotta
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
- Department of Radiology, Fondazione Istituto Giuseppe Giglio, Ct.da Pietrapollastra, Palermo 90015, Italy
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Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics. J Clin Med 2020; 9:jcm9061853. [PMID: 32545851 PMCID: PMC7356091 DOI: 10.3390/jcm9061853] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 06/06/2020] [Accepted: 06/09/2020] [Indexed: 12/20/2022] Open
Abstract
We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77–0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75–0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.
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50
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Lu H, Yin J. Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status. Front Oncol 2020; 10:543. [PMID: 32373531 PMCID: PMC7186477 DOI: 10.3389/fonc.2020.00543] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 03/26/2020] [Indexed: 01/04/2023] Open
Abstract
Background: Breast tumor heterogeneity is related to risk factors that lead to aggressive tumor growth; however, such heterogeneity has not been thoroughly investigated. Purpose: To evaluate the performance of texture features extracted from heterogeneity subregions on subtraction MRI images for identifying human epidermal growth factor receptor 2 (HER2) 2+ status of breast cancers. Materials and Methods: Seventy-six patients with HER2 2+ breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging were enrolled, including 42 HER2 positive and 34 negative cases confirmed by fluorescence in situ hybridization. The lesion area was delineated semi-automatically on the subtraction MRI images at the second, fourth, and sixth phases (P-1, P-2, and P-3). A regionalization method was used to segment the lesion area into three subregions (rapid, medium, and slow) according to peak arrival time of the contrast agent. We extracted 488 texture features from the whole lesion area and three subregions independently. Wrapper, least absolute shrinkage and selection operator (LASSO), and stepwise methods were used to identify the optimal feature subsets. Univariate analysis was performed as well as support vector machine (SVM) with a leave-one-out-based cross-validation method. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the classifiers. Results: In univariate analysis, the variance from medium subregion at P-2 was the best-performing feature for distinguishing HER2 2+ status (AUC = 0.836); for the whole lesion region, the variance at P-2 achieved the best performance (AUC = 0.798). There was no significant difference between the two methods (P = 0.271). In the machine learning with SVM, the best performance (AUC = 0.929) was achieved with LASSO from rapid subregion at P-2; for the whole region, the highest AUC value was 0.847 obtained at P-2 with LASSO. The difference was significant between the two methods (P = 0.021). Conclusion: The texture analysis of heterogeneity subregions based on intratumoral regionalization method showed potential value for recognizing HER2 2+ status in breast cancer.
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Affiliation(s)
- Hecheng Lu
- School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China.,Department of Radiology, ShengJing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, ShengJing Hospital of China Medical University, Shenyang, China
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