{"title":"利用 CT 成像对肺癌进行病理分级和预后评估的深度学习模型:NLST 和外部验证队列研究。","authors":"Runhuang Yang,Weiming Li,Siqi Yu,Zhiyuan Wu,Haiping Zhang,Xiangtong Liu,Lixin Tao,Xia Li,Jian Huang,Xiuhua Guo","doi":"10.1016/j.acra.2024.08.028","DOIUrl":null,"url":null,"abstract":"RATIONALE AND OBJECTIVES\r\nTo develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.\r\n\r\nMATERIAL AND METHODS\r\nThis study utilized 572 cases from the National Lung Screening Trial cohort, dividing them randomly into training (461 cases) and internal validation (111 cases) sets in an 8:2 ratio. Additionally, 224 cases from four cohorts obtained from the Cancer Imaging Archive, all diagnosed with non-small cell lung cancer, were included for external validation. The deep learning model, built on the MobileNetV3 architecture, was assessed in both internal and external validation sets using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The model's prognostic value was further analyzed using Cox proportional hazards models.\r\n\r\nRESULTS\r\nThe model achieved high accuracy, sensitivity, specificity, and AUC in the internal validation set (accuracy: 0.888, macro AUC: 0.968, macro sensitivity: 0.798, macro specificity: 0.956). External validation demonstrated comparable performance (accuracy: 0.807, macro AUC: 0.920, macro sensitivity: 0.799, macro specificity: 0.896). The model's predicted signatures correlated significantly with patient mortality and provided valuable insights for prognostic assessment (adjusted HR 2.016 [95% CI: 1.010, 4.022]).\r\n\r\nCONCLUSIONS\r\nThis study successfully developed and validated a deep learning model for the preoperative grading of lung cancer pathology. The model's accurate predictions could serve as a useful adjunct in treatment planning for lung cancer patients, enabling more effective and customized interventions to improve patient outcomes.","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts.\",\"authors\":\"Runhuang Yang,Weiming Li,Siqi Yu,Zhiyuan Wu,Haiping Zhang,Xiangtong Liu,Lixin Tao,Xia Li,Jian Huang,Xiuhua Guo\",\"doi\":\"10.1016/j.acra.2024.08.028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"RATIONALE AND OBJECTIVES\\r\\nTo develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.\\r\\n\\r\\nMATERIAL AND METHODS\\r\\nThis study utilized 572 cases from the National Lung Screening Trial cohort, dividing them randomly into training (461 cases) and internal validation (111 cases) sets in an 8:2 ratio. Additionally, 224 cases from four cohorts obtained from the Cancer Imaging Archive, all diagnosed with non-small cell lung cancer, were included for external validation. The deep learning model, built on the MobileNetV3 architecture, was assessed in both internal and external validation sets using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The model's prognostic value was further analyzed using Cox proportional hazards models.\\r\\n\\r\\nRESULTS\\r\\nThe model achieved high accuracy, sensitivity, specificity, and AUC in the internal validation set (accuracy: 0.888, macro AUC: 0.968, macro sensitivity: 0.798, macro specificity: 0.956). External validation demonstrated comparable performance (accuracy: 0.807, macro AUC: 0.920, macro sensitivity: 0.799, macro specificity: 0.896). The model's predicted signatures correlated significantly with patient mortality and provided valuable insights for prognostic assessment (adjusted HR 2.016 [95% CI: 1.010, 4.022]).\\r\\n\\r\\nCONCLUSIONS\\r\\nThis study successfully developed and validated a deep learning model for the preoperative grading of lung cancer pathology. The model's accurate predictions could serve as a useful adjunct in treatment planning for lung cancer patients, enabling more effective and customized interventions to improve patient outcomes.\",\"PeriodicalId\":50928,\"journal\":{\"name\":\"Academic Radiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.acra.2024.08.028\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.acra.2024.08.028","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts.
RATIONALE AND OBJECTIVES
To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.
MATERIAL AND METHODS
This study utilized 572 cases from the National Lung Screening Trial cohort, dividing them randomly into training (461 cases) and internal validation (111 cases) sets in an 8:2 ratio. Additionally, 224 cases from four cohorts obtained from the Cancer Imaging Archive, all diagnosed with non-small cell lung cancer, were included for external validation. The deep learning model, built on the MobileNetV3 architecture, was assessed in both internal and external validation sets using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The model's prognostic value was further analyzed using Cox proportional hazards models.
RESULTS
The model achieved high accuracy, sensitivity, specificity, and AUC in the internal validation set (accuracy: 0.888, macro AUC: 0.968, macro sensitivity: 0.798, macro specificity: 0.956). External validation demonstrated comparable performance (accuracy: 0.807, macro AUC: 0.920, macro sensitivity: 0.799, macro specificity: 0.896). The model's predicted signatures correlated significantly with patient mortality and provided valuable insights for prognostic assessment (adjusted HR 2.016 [95% CI: 1.010, 4.022]).
CONCLUSIONS
This study successfully developed and validated a deep learning model for the preoperative grading of lung cancer pathology. The model's accurate predictions could serve as a useful adjunct in treatment planning for lung cancer patients, enabling more effective and customized interventions to improve patient outcomes.
期刊介绍:
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.