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Zeng Y, Liu Y, Li J, Feng B, Lu J. Value of Computed Tomography Scan for Detecting Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma. Ann Surg Oncol 2025; 32:1635-1650. [PMID: 39586955 DOI: 10.1245/s10434-024-16568-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Accepted: 11/10/2024] [Indexed: 11/27/2024]
Abstract
BACKGROUND The necessity of computed tomography (CT) scan for detecting potential lymph node metastasis (LNM) in early esophageal squamous cell carcinoma (ESCC) before endoscopic and surgical treatments is under debate. METHODS Patients with histologically proven ESCC limited to the mucosa or submucosa were examined retrospectively. Diagnostic performance of CT for detecting LNM was analyzed by comparing original CT reports with pathology reports. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS A total of 625 patients from three tertiary referral hospitals were included. The rate of pathologically confirmed LNM was 12.5%. Based on original CT reports, the sensitivity, specificity, accuracy, PPV, and NPV of CT to determine LNM in T1 ESCC were 41.0%, 83.2%, 77.9%, 25.8%, and 90.8% respectively. For mucosal cancers (T1a), these parameters were 50.0%, 81.7%, 80.9%, 6.8%, and 98.4%, respectively. For submucosal cancers (T1b), they were 40.0%, 85.0%, 75.0%, 43.0%, and 83.3%, respectively. Additionally, the diagnostic performance of CT for LNM was relatively better for ESCC in the lower esophagus. Pathologically, 69.2% of patients with LNM did not exhibit lymphovascular invasion (LVI), and the sensitivity of CT for recognizing LNM in these patients (33.3%) was lower than those with LVI (58.3%). CONCLUSIONS Computed tomography can detect nearly half of the LNM cases in early ESCC with high specificity. The performance of CT further improved in LNM cases with LVI. Therefore, we conclude that routine preoperative CT for the assessment of potential LNM risk in patients with early ESCC is necessary.
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Affiliation(s)
- Yunqing Zeng
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yaping Liu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Jinhou Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Department of Gastroenterology, Taian City Central Hospital, Taian, Shandong, China
| | - Bingcheng Feng
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jiaoyang Lu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
- Medical Integration and Practice Center, Shandong University, Jinan, China.
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Shi L, Li C, Bai Y, Cao Y, Zhao S, Chen X, Cheng Z, Zhang Y, Li H. CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma. Eur Radiol 2025; 35:1594-1604. [PMID: 39470794 DOI: 10.1007/s00330-024-11141-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 08/26/2024] [Accepted: 09/19/2024] [Indexed: 11/01/2024]
Abstract
OBJECTIVE To develop and validate a CT-based radiomics model to predict pathologic complete response (pCR) after neoadjuvant immunotherapy plus chemoradiotherapy (NICRT) in locally advanced esophageal squamous cell carcinoma (ESCC). METHODS A total of 105 patients with locally advanced ESCC receiving NICRT from February 2019 to December 2023 were enrolled. Patients were randomly divided into the training cohort and the test cohort at a 3:1 ratio. Enhanced CT scans were obtained before NICRT treatment. The 2D and 3D regions of interest were segmented, and features were extracted, followed by feature selection. Six algorithms were applied to construct the radiomics and clinical models. These models were evaluated by area under curve (AUC), accuracy, sensitivity, and specificity, and their respective optimal algorithms were further compared. RESULTS Forty-eight patients (45.75%) achieved pCR after NICRT. The AUC values of three algorithms in 2D radiomics models were higher than those in the 3D radiomics model and clinical model. Among these, the 2D radiomics model based on eXtreme Gradient Boosting (XGBoost) exhibited the best performance, with an AUC of 0.89 (95% CI, 0.81-0.97), accuracy of 0.85, sensitivity of 0.86, and specificity of 0.84 in the training cohort, and an AUC of 0.80 (95% CI, 0.64-0.97), accuracy of 0.77, sensitivity of 0.84, and specificity of 0.69 in the test cohort. Calibration curves also showed good agreement between predicted and actual response, and the decision curve analysis further confirmed its clinical applicability. CONCLUSION The 2D radiomics model can effectively predict pCR to NICRT in locally advanced ESCC. KEY POINTS Question Can CT-based radiomics predict pathologic complete response (pCR) after neoadjuvant immunotherapy plus chemoradiotherapy (NICRT) in locally advanced esophageal squamous cell carcinoma (ESCC)? Findings The model based on eXtreme Gradient Boosting (XGBoost) performed best, with an AUC of 0.89 in the training and 0.80 in the test cohort. Clinical relevance This CT-based radiomics model exhibits promising performance for predicting pCR to NICRT in locally advanced ESCC, which may be valuable in personalized treatment plan optimization.
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Affiliation(s)
- Liqiang Shi
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chengqiang Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yaya Bai
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuqin Cao
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shengguang Zhao
- Department of Radiotherapy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaoyan Chen
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yajie Zhang
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Hecheng Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Wengert GJ, Lu H, Aboagye EO, Langs G, Poetsch N, Schwartz E, Bagó-Horváth Z, Fotopoulou C, Polterauer S, Helbich TH, Rockall AG. CT-based radiomic prognostic vector (RPV) predicts survival and stromal histology in high-grade serous ovarian cancer: an external validation study. Eur Radiol 2024:10.1007/s00330-024-11267-5. [PMID: 39661150 DOI: 10.1007/s00330-024-11267-5] [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: 04/01/2024] [Revised: 10/22/2024] [Accepted: 11/07/2024] [Indexed: 12/12/2024]
Abstract
OBJECTIVES In women with high-grade serous ovarian cancer (HGSOC), a CT-based radiomic prognostic vector (RPV) predicted stromal phenotype and survival after primary surgery. The study's purpose was to fully externally validate RPV and its biological correlate. MATERIALS AND METHODS In this retrospective study, ovarian masses on CT scans of HGSOC patients, who underwent primary cytoreductive surgery in an ESGO-certified Center between 2002 and 2017, were segmented for external RPV score calculation and then correlated with overall survival (OS) and progression-free survival (PFS). A subset of tissue samples subjected to fibronectin immunohistochemistry were evaluated by a gynaeco-pathologist for stromal content. Kaplan-Meier log-rank test and a Cox proportional hazards model were used for outcome analysis. RESULTS Among 340 women with HGSOC, 244 ovarian lesions were available for segmentation in 198 women (mean age 59.8 years, range 34-92). Median OS was 48.69 months (IQR: 27.0-102.5) and PFS was 19.3 months (IQR: 13-32.2). Using multivariate Cox analysis, poor OS was associated with RPV-high (HR 3.17; 95% CI: 1.32-7.60; p = 0.0099), post-operative residual disease (HR 2.04; 95% CI: 1.30-3.20; p = 0.0020), and FIGO stage III/IV (HR 1.79; 95% CI: 1.11-2.86; p = 0.016). Age did not influence OS. RPV-high tissue had higher stromal content based on fibronectin expression (mean 48.9%, SD 10.5%) compared to RPV-low cases (mean 14.9%, SD 10.5%, p < 0.0001). RPV score was not significantly associated with PFS. CONCLUSION Patients with HGSOC and RPV-high ovarian mass on pre-operative CT had significantly worse OS following primary surgery and a higher stromal content compared to RPV-low masses, externally validating the RPV and its biological interpretation. KEY POINTS Question Can the performance of a previously described RPV in women with HGSOC be replicated when licenced to an external institution? Findings External validation of RPV among 244 ovarian lesions demonstrated that, on multivariate analysis, OS was associated with RPV, stage, and postoperative residual disease, replicating previous findings. Clinical relevance External validation of a radiomic tool is an essential step in translation to clinical applicability and provides the basis for prospective validation. In clinical practice, this RPV may allow more personalized decision-making for women with ovarian cancer being considered for extensive cytoreductive surgery.
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Affiliation(s)
- Georg J Wengert
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria.
| | - Haonan Lu
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Georg Langs
- Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Nina Poetsch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Zsuzsanna Bagó-Horváth
- Department of Pathology and Comprehensive Cancer Center, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Christina Fotopoulou
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Stephan Polterauer
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Andrea G Rockall
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
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Yuan P, Huang ZH, Yang YH, Bao FC, Sun K, Chao FF, Liu TT, Zhang JJ, Xu JM, Li XN, Li F, Ma T, Li H, Li ZH, Zhang SF, Hu J, Qi Y. A 18F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma. Cancer Imaging 2024; 24:153. [PMID: 39533388 PMCID: PMC11556142 DOI: 10.1186/s40644-024-00799-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 10/29/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from 18F-fluorodeoxyglucose (18F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicting cervical lymph node metastasis (CLNM) in patients with esophageal squamous cell carcinoma (ESCC). METHODS The study included 300 ESCC patients from the First Affiliated Hospital of Zhengzhou University who were divided into a training cohort and an internal testing cohort with an 8:2 ratio. Another 111 patients from Shanghai Chest Hospital were included as the external cohort. For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor volume (GTV) and cervical lymph node (CLN) delineated layer by layer and 256 PET/CT-based DL features from the maximum cross-section of GTV and CLN images We input these features into seven different machine learning algorithms and ultimately selected logistic regression (LR) as the model classifier. Subsequently, we evaluated seven models (Clinical, Radiomics, Radiomics-Clinical, DL-Clinical, DL-Radiomics, DL-Radiomics-Clinical) using Radiomics features, DL features and clinical feature. RESULTS The DL-Radiomics-Clinical (DRC) model demonstrated higher AUC of 0.955 and 0.916 compared to the other six models in both internal and external testing cohorts respectively. The DRC model achieved the highest accuracy among the seven models in both the internal and external test sets, with scores of 0.951 and 0.892, respectively. CONCLUSIONS Through the combination of Radiomics features and DL features from PET/CT imaging and clinical feature, we developed a predictive model exhibiting exceptional classification capabilities. This model can be considered as a non-invasive method for predication of CLNM in patients with ESCC. It might facilitate decision-making regarding to the extend of lymph node dissection, and to select candidates for postoperative adjuvant therapy.
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Affiliation(s)
- Ping Yuan
- Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China.
| | - Zhen-Hao Huang
- Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Yun-Hai Yang
- Surgical Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Fei-Chao Bao
- Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ke Sun
- Department of nuclear medicine and radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Fang-Fang Chao
- Department of nuclear medicine and radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Ting-Ting Liu
- Department of nuclear medicine and radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Jing-Jing Zhang
- Department of nuclear medicine and radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Jin-Ming Xu
- Thoracic Surgery, The First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang province, China
| | - Xiang-Nan Li
- Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Feng Li
- Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Tao Ma
- Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Hao Li
- School of Artiffcial Intelligence, Sun Yat-sen University, Zhuhai, Guangdong province, China
| | - Zi-Hao Li
- Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China
| | - Shan-Feng Zhang
- School of Basic Medical Science, Zhengzhou University, Zhengzhou, Henan province, China
| | - Jian Hu
- Thoracic Surgery, The First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang province, China.
| | - Yu Qi
- Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan province, China.
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Li X, Dong J, Li B, Aimei O, Sun Y, Wu X, Liu W, Li R, Li Z, Yang Y. Prediction of Neoadjuvant Chemoradiotherapy Sensitivity in Patients With Esophageal Squamous Cell Carcinoma Using CT-Based Radiomics Combined With Clinical Features. Dose Response 2024; 22:15593258241301525. [PMID: 39588071 PMCID: PMC11587189 DOI: 10.1177/15593258241301525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 09/21/2024] [Accepted: 10/03/2024] [Indexed: 11/27/2024] Open
Abstract
Background: For patients with resectable locally advanced esophageal squamous cell carcinoma (ESCC), the current standard treatment is neoadjuvant chemoradiotherapy (nCRT) plus radical surgery. Objective: This study aimed to establish a predictive model, based on computed tomography (CT) radiomics features and clinical parameters, to predict sensitivity to nCRT in patients with ESCC pre-treatment. The goal was to provide risk stratification and decision-making recommendations for clinical treatments and offer more valuable information for developing personalized therapies. Methods: This retrospective study involved 102 patients diagnosed with ESCC through biopsy who underwent nCRT. To select radiomics features, we used the least absolute shrinkage and selection operator (LASSO) algorithm. A combined model was constructed, integrating the selected clinically relevant parameters with the Rad-Score. To assess the performance of this combined model, we utilized calibration curves and receiver operating characteristic (ROC) curves. Results: Nine optimal radiomics features were selected using the LASSO algorithm. The support vector machine (SVM) classifier was identified as having the best predictive performance. The area under the curve (AUC) of the SVM training group was 0.937 (95% CI: 0.856-1.000), and of the validation group was 0.831 (95% CI: 0.679-0.983). Smoking and alcohol history, neutrophil to lymphocyte ratio, serum aspartate aminotransferase to alanine aminotransferase ratio, and carcinoembryonic antigen and fibrinogen levels were independent predictors of sensitivity to nCRT in patients with ESCC. The AUCs of the combined model for the training and validation groups were 0.870 (95% CI: 0.774-0.964) and 0.821 (95% CI: 0.669-0.972), respectively. The calibration curve showed that the nomogram's predictions were close to the actual clinical observations, indicating that the model exhibited good predictive performance. Conclusion: Our combined model based on Rad-Score and clinical characteristics showed high predictive performance for predicting sensitivity to nCRT in patients with ESCC. It may be useful for predicting treatment effects in clinical practice and demonstrates the significant potential of radiomics in predicting and optimizing treatment decisions.
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Affiliation(s)
- Xindi Li
- Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China
- Department of Oncology, Shandong University, Shandong Provincial Third Hospital, Jinan, China
| | - Jigang Dong
- Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China
- Qingdao Jiaozhou Central Hospital, Qingdao, China
| | - Baosheng Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ouyang Aimei
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yahong Sun
- Department of Oncology, Shandong University, Shandong Provincial Third Hospital, Jinan, China
| | - Xia Wu
- Department of Oncology, Shandong University, Shandong Provincial Third Hospital, Jinan, China
| | - Wenjuan Liu
- Shandong Provincial Key Laboratory of Radiation Oncology, Cancer Research Center, Shandong Cancer Hospital and Institute, Jinan, China
| | - Ruobing Li
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Zhongyuan Li
- School of Medical Imaging, Shandong Second Medical University, Weifang, China
| | - Yu Yang
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Ma D, Zhou T, Chen J, Chen J. Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis. BMC Med Imaging 2024; 24:144. [PMID: 38867143 PMCID: PMC11170881 DOI: 10.1186/s12880-024-01278-5] [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: 01/26/2024] [Accepted: 04/22/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Esophageal cancer, a global health concern, impacts predominantly men, particularly in Eastern Asia. Lymph node metastasis (LNM) significantly influences prognosis, and current imaging methods exhibit limitations in accurate detection. The integration of radiomics, an artificial intelligence (AI) driven approach in medical imaging, offers a transformative potential. This meta-analysis evaluates existing evidence on the accuracy of radiomics models for predicting LNM in esophageal cancer. METHODS We conducted a systematic review following PRISMA 2020 guidelines, searching Embase, PubMed, and Web of Science for English-language studies up to November 16, 2023. Inclusion criteria focused on preoperatively diagnosed esophageal cancer patients with radiomics predicting LNM before treatment. Exclusion criteria were applied, including non-English studies and those lacking sufficient data or separate validation cohorts. Data extraction encompassed study characteristics and radiomics technical details. Quality assessment employed modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) tools. Statistical analysis involved random-effects models for pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Heterogeneity and publication bias were assessed using Deek's test and funnel plots. Analysis was performed using Stata version 17.0 and meta-DiSc. RESULTS Out of 426 initially identified citations, nine studies met inclusion criteria, encompassing 719 patients. These retrospective studies utilized CT, PET, and MRI imaging modalities, predominantly conducted in China. Two studies employed deep learning-based radiomics. Quality assessment revealed acceptable QUADAS-2 scores. RQS scores ranged from 9 to 14, averaging 12.78. The diagnostic meta-analysis yielded a pooled sensitivity, specificity, and AUC of 0.72, 0.76, and 0.74, respectively, representing fair diagnostic performance. Meta-regression identified the use of combined models as a significant contributor to heterogeneity (p-value = 0.05). Other factors, such as sample size (> 75) and least absolute shrinkage and selection operator (LASSO) usage for feature extraction, showed potential influence but lacked statistical significance (0.05 < p-value < 0.10). Publication bias was not statistically significant. CONCLUSION Radiomics shows potential for predicting LNM in esophageal cancer, with a moderate diagnostic performance. Standardized approaches, ongoing research, and prospective validation studies are crucial for realizing its clinical applicability.
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Affiliation(s)
- Dong Ma
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China
| | - Teli Zhou
- Guangzhou Shiyuan Clinics Co., Ltd, Guangzhou, Guangdong, 510530, China
| | - Jing Chen
- The Fifth Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, 510900, China
| | - Jun Chen
- Dingxi People's Hospital, Dingxi, Gansu, 743000, China.
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Hinzpeter R, Mirshahvalad SA, Kulanthaivelu R, Kohan A, Ortega C, Metser U, Liu A, Farag A, Elimova E, Wong RKS, Yeung J, Jang RWJ, Veit-Haibach P. Gastro-Esophageal Cancer: Can Radiomic Parameters from Baseline 18F-FDG-PET/CT Predict the Development of Distant Metastatic Disease? Diagnostics (Basel) 2024; 14:1205. [PMID: 38893731 PMCID: PMC11171817 DOI: 10.3390/diagnostics14111205] [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: 04/30/2024] [Revised: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
We aimed to determine if clinical parameters and radiomics combined with sarcopenia status derived from baseline 18F-FDG-PET/CT could predict developing metastatic disease and overall survival (OS) in gastroesophageal cancer (GEC). Patients referred for primary staging who underwent 18F-FDG-PET/CT from 2008 to 2019 were evaluated retrospectively. Overall, 243 GEC patients (mean age = 64) were enrolled. Clinical, histopathology, and sarcopenia data were obtained, and primary tumor radiomics features were extracted. For classification (early-stage vs. advanced disease), the association of the studied parameters was evaluated. Various clinical and radiomics models were developed and assessed. Accuracy and area under the curve (AUC) were calculated. For OS prediction, univariable and multivariable Cox analyses were performed. The best model included PET/CT radiomics features, clinical data, and sarcopenia score (accuracy = 80%; AUC = 88%). For OS prediction, various clinical, CT, and PET features entered the multivariable analysis. Three clinical factors (advanced disease, age ≥ 70 and ECOG ≥ 2), along with one CT-derived and one PET-derived radiomics feature, retained their significance. Overall, 18F-FDG PET/CT radiomics seems to have a potential added value in identifying GEC patients with advanced disease and may enhance the performance of baseline clinical parameters. These features may also have a prognostic value for OS, improving the decision-making for GEC patients.
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Affiliation(s)
- Ricarda Hinzpeter
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Seyed Ali Mirshahvalad
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Roshini Kulanthaivelu
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Andres Kohan
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Claudia Ortega
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Ur Metser
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Amy Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 1X6, Canada;
| | - Adam Farag
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Elena Elimova
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Rebecca K. S. Wong
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (R.K.S.W.); (R.W.-J.J.)
| | - Jonathan Yeung
- Division of Thoracic Surgery, Department of Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Raymond Woo-Jun Jang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (R.K.S.W.); (R.W.-J.J.)
| | - Patrick Veit-Haibach
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
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Gao D, Wu YP, Chen TW. Review and prospects of new progress in intelligent imaging research on lymph node metastasis in esophageal carcinoma. META-RADIOLOGY 2024; 2:100081. [DOI: 10.1016/j.metrad.2024.100081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Wu YP, Wu L, Ou J, Cao JM, Fu MY, Chen TW, Ouchi E, Hu J. Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability. Eur J Radiol 2024; 170:111197. [PMID: 37992611 DOI: 10.1016/j.ejrad.2023.111197] [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: 09/25/2023] [Revised: 10/12/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Abstract
PURPOSE To develop CT radiomics models of resectable esophageal squamous cell carcinoma (ESCC) and lymph node (LN) to preoperatively identify LN+. MATERIALS AND METHODS 299 consecutive patients with ESCC were enrolled in the study, 140 of whom were LN+ and 159 were LN-. Of the 299 patients, 249 (from the same hospital) were randomly divided into a training cohort (n = 174) and a test cohort (n = 75). The remaining 50 patients, from a second hospital, were assigned to an external validation cohort. In the training cohort, preoperative contrast-enhanced CT radiomics features of ESCC and LN were extracted, then integrated with clinical features to develop three models: ESCC, LN and combined. The performance of these models was assessed using area under receiver operating characteristic curve (AUC), and F-1 score, which were validated in both the test cohort and external validation cohort. RESULTS An ESCC model was developed for the training cohort utilizing the 8 tumor radiomics features, and an LN model was constructed using 9 nodal radiomics features. A combined model was constructed using both ESCC and LN extracted features, in addition to cT stage and LN+ distribution. This combined model had the highest predictive ability among the three models in the training cohort (AUC = 0.948, F1-score = 0.878). The predictive ability was validated in both the test and external validation cohorts (AUC = 0.885 and 0.867, F1-score = 0.816 and 0.773, respectively). CONCLUSION To preoperatively determine LN+, the combined model is superior to models of ESCC and LN alone.
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Affiliation(s)
- Yu-Ping Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Lan Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Ou
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Jin-Ming Cao
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China; Department of Radiology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, China
| | - Mao-Yong Fu
- Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Tian-Wu Chen
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
| | - Erika Ouchi
- Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Jiani Hu
- Department of Radiology, Wayne State University, Detroit, MI, USA
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Zhu C, Mu F, Wang S, Qiu Q, Wang S, Wang L. Prediction of distant metastasis in esophageal cancer using a radiomics-clinical model. Eur J Med Res 2022; 27:272. [PMID: 36463269 PMCID: PMC9719117 DOI: 10.1186/s40001-022-00877-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/16/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3-10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC. METHODS A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. RESULTS Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742-0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626-0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652-0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075-0.345), and its IDI was 0.071 (95% CI 0.030-0.112), P = 0.001. CONCLUSIONS We developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.
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Affiliation(s)
- Chao Zhu
- grid.415468.a0000 0004 1761 4893Department of Oncology, Qingdao Central Hospital Affiliated to Qingdao University, Qingdao, 266042 Shandong China ,grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Fengchun Mu
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Songping Wang
- grid.415468.a0000 0004 1761 4893Department of Oncology, Qingdao Central Hospital Affiliated to Qingdao University, Qingdao, 266042 Shandong China
| | - Qingtao Qiu
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Shuai Wang
- grid.268079.20000 0004 1790 6079Department of Radiation Oncology, Affiliated Hospital of Weifang Medical University, Weifang, 261000 Shandong China
| | - Linlin Wang
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning. JOURNAL OF ONCOLOGY 2022; 2022:8534262. [PMID: 36147442 PMCID: PMC9489385 DOI: 10.1155/2022/8534262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/26/2022] [Accepted: 08/13/2022] [Indexed: 11/18/2022]
Abstract
Purpose To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC). Methods Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis. Results No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features (p < 0.05). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists. Conclusion The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.
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O'Shea RJ, Rookyard C, Withey S, Cook GJR, Tsoka S, Goh V. Radiomic assessment of oesophageal adenocarcinoma: a critical review of 18F-FDG PET/CT, PET/MRI and CT. Insights Imaging 2022; 13:104. [PMID: 35715706 PMCID: PMC9206060 DOI: 10.1186/s13244-022-01245-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/28/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES Radiomic models present an avenue to improve oesophageal adenocarcinoma assessment through quantitative medical image analysis. However, model selection is complicated by the abundance of available predictors and the uncertainty of their relevance and reproducibility. This analysis reviews recent research to facilitate precedent-based model selection for prospective validation studies. METHODS This analysis reviews research on 18F-FDG PET/CT, PET/MRI and CT radiomics in oesophageal adenocarcinoma between 2016 and 2021. Model design, testing and reporting are evaluated according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score and Radiomics Quality Score (RQS). Key results and limitations are analysed to identify opportunities for future research in the area. RESULTS Radiomic models of stage and therapeutic response demonstrated discriminative capacity, though clinical applications require greater sensitivity. Although radiomic models predict survival within institutions, generalisability is limited. Few radiomic features have been recommended independently by multiple studies. CONCLUSIONS Future research must prioritise prospective validation of previously proposed models to further clinical translation.
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Affiliation(s)
- Robert J O'Shea
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK.
| | - Chris Rookyard
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK
| | - Sam Withey
- Department of Radiology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Gary J R Cook
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK
| | - Sophia Tsoka
- Department of Informatics, School of Natural and Mathematical Sciences, King's College London, London, UK
| | - Vicky Goh
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Rd, London, SE1 7EU, UK
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, UK
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
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Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Cheng J, Ren C, Liu G, Shui R, Zhang Y, Li J, Shao Z. Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer. Cancers (Basel) 2022; 14:cancers14040950. [PMID: 35205699 PMCID: PMC8870230 DOI: 10.3390/cancers14040950] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/26/2022] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Accurate clinical axillary evaluation plays an important role in the diagnosis of and treatment planning for breast cancer (BC). This study aimed to develop a machine learning model integrating dedicated breast PET and clinical characteristics for prediction of axillary lymph node status in cT1-2N0-1M0 BC non-invasively. The performance of this integrating model in identifying pN0 and pN1 with the AUC was 0.94. We achieved an NPV of 96.88% in the cN0 and PPV of 92.73% in the cN1 subgroup. The higher true positive and true negative rate could delineate clinical subtypes and apply more precise treatment for patients with early-stage BC. Abstract Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (18F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.
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Affiliation(s)
- Jingyi Cheng
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; (J.C.); (Y.Z.)
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai 201321, China
| | - Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China;
| | - Guangyu Liu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China;
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
| | - Ruohong Shui
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China;
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yingjian Zhang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; (J.C.); (Y.Z.)
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai 201321, China
| | - Junjie Li
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China;
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
- Correspondence: (J.L.); (Z.S.); Tel.: +86-021-64175590 (ext. 88809) (J.L. & Z.S.); Fax: +86-021-64176650 (J.L. & Z.S.)
| | - Zhimin Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China;
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
- Correspondence: (J.L.); (Z.S.); Tel.: +86-021-64175590 (ext. 88809) (J.L. & Z.S.); Fax: +86-021-64176650 (J.L. & Z.S.)
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Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
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Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 2021; 49:2462-2481. [PMID: 34939174 PMCID: PMC9206619 DOI: 10.1007/s00259-021-05658-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/12/2021] [Indexed: 10/24/2022]
Abstract
PURPOSE Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines. METHODS A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality. RESULTS Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings. CONCLUSION A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.
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Karahan Şen NP, Aksu A, Çapa Kaya G. A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods. Ann Nucl Med 2021; 35:1030-1037. [PMID: 34106428 DOI: 10.1007/s12149-021-01638-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/03/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline 18F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology. METHODS The initial staging 18F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann-Whitney U test) was used for each feature, in pairwise comparisons of independent variables. A p value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with p < 0.05. Correlation between the significant features was evaluated with Spearman correlation test; features with correlation coefficient < 0.8 were evaluated with several ML algorithms. RESULTS In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination. CONCLUSION Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.
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Affiliation(s)
- Nazlı Pınar Karahan Şen
- Department of Nuclear Medicine, Dokuz Eylul University Faculty of Medicine, İnciraltı mah. Mithatpaşa cad. no:1606 Balçova, Izmir, Turkey.
| | - Ayşegül Aksu
- Başakşehir Çam ve Sakura City Hospital, Department of Nuclear Medicine, Istanbul, Turkey
| | - Gamze Çapa Kaya
- Department of Nuclear Medicine, Dokuz Eylul University Faculty of Medicine, İnciraltı mah. Mithatpaşa cad. no:1606 Balçova, Izmir, Turkey
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Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study. Cancers (Basel) 2021; 13:cancers13092145. [PMID: 33946826 PMCID: PMC8124289 DOI: 10.3390/cancers13092145] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/23/2021] [Accepted: 04/26/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. METHODS We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. RESULTS The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (p < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. CONCLUSIONS Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.
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