Payam Jannatdoust , Parya Valizadeh , Mohammad-Taha Pahlevan-Fallahy , Amir Hassankhani , Melika Amoukhteh , Sadra Behrouzieh , Delaram J. Ghadimi , Cem Bilgin , Ali Gholamrezanezhad
{"title":"基于 CT 的放射组学和深度学习预测食管癌淋巴结转移的诊断准确性","authors":"Payam Jannatdoust , Parya Valizadeh , Mohammad-Taha Pahlevan-Fallahy , Amir Hassankhani , Melika Amoukhteh , Sadra Behrouzieh , Delaram J. Ghadimi , Cem Bilgin , Ali Gholamrezanezhad","doi":"10.1016/j.clinimag.2024.110225","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning.</p></div><div><h3>Methods</h3><p>A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS).</p></div><div><h3>Results</h3><p>Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %–90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %–89 %), with no significant difference (<em>p</em> = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (<em>p</em> = 0.054), significant after removing outliers (<em>p</em> < 0.01). Incorporating clinical data improved sensitivity in validation sets (<em>p</em> = 0.029). No significant difference was found between models based on CE or non-CE imaging (<em>p</em> = 0.281) or arterial or venous phase imaging (<em>p</em> = 0.927).</p></div><div><h3>Conclusion</h3><p>Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.</p></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer\",\"authors\":\"Payam Jannatdoust , Parya Valizadeh , Mohammad-Taha Pahlevan-Fallahy , Amir Hassankhani , Melika Amoukhteh , Sadra Behrouzieh , Delaram J. Ghadimi , Cem Bilgin , Ali Gholamrezanezhad\",\"doi\":\"10.1016/j.clinimag.2024.110225\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning.</p></div><div><h3>Methods</h3><p>A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS).</p></div><div><h3>Results</h3><p>Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %–90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %–89 %), with no significant difference (<em>p</em> = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (<em>p</em> = 0.054), significant after removing outliers (<em>p</em> < 0.01). Incorporating clinical data improved sensitivity in validation sets (<em>p</em> = 0.029). No significant difference was found between models based on CE or non-CE imaging (<em>p</em> = 0.281) or arterial or venous phase imaging (<em>p</em> = 0.927).</p></div><div><h3>Conclusion</h3><p>Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.</p></div>\",\"PeriodicalId\":50680,\"journal\":{\"name\":\"Clinical Imaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0899707124001554\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Imaging","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0899707124001554","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer
Background
Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning.
Methods
A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS).
Results
Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %–90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %–89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927).
Conclusion
Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.
期刊介绍:
The mission of Clinical Imaging is to publish, in a timely manner, the very best radiology research from the United States and around the world with special attention to the impact of medical imaging on patient care. The journal''s publications cover all imaging modalities, radiology issues related to patients, policy and practice improvements, and clinically-oriented imaging physics and informatics. The journal is a valuable resource for practicing radiologists, radiologists-in-training and other clinicians with an interest in imaging. Papers are carefully peer-reviewed and selected by our experienced subject editors who are leading experts spanning the range of imaging sub-specialties, which include:
-Body Imaging-
Breast Imaging-
Cardiothoracic Imaging-
Imaging Physics and Informatics-
Molecular Imaging and Nuclear Medicine-
Musculoskeletal and Emergency Imaging-
Neuroradiology-
Practice, Policy & Education-
Pediatric Imaging-
Vascular and Interventional Radiology