Changhwan Sung, Jungsu S. Oh, Byung Soo Park, Su Ssan Kim, Si Yeol Song, Jong Jin Lee
{"title":"利用 18F-FDG PET/CT 评估肺癌患者放疗后复发情况的深度学习模型的诊断性能","authors":"Changhwan Sung, Jungsu S. Oh, Byung Soo Park, Su Ssan Kim, Si Yeol Song, Jong Jin Lee","doi":"10.1007/s12149-024-01925-5","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.</p><h3>Methods</h3><p>We retrospectively recruited 308 patients with lung cancer with RT-related changes observed on <sup>18</sup>F-fluorodeoxyglucose positron emission tomography–computed tomography (<sup>18</sup>F-FDG PET/CT) performed after RT. Patients were labelled as positive or negative for tumour recurrence through histologic diagnosis or clinical follow-up after <sup>18</sup>F-FDG PET/CT. A two-dimensional (2D) slice-based convolutional neural network (CNN) model was created with a total of 3329 slices as input, and performance was evaluated with five independent test sets.</p><h3>Results</h3><p>For the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98–0.99, 95–98%, and 87–95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM).</p><h3>Conclusion</h3><p>The 2D slice-based CNN model using <sup>18</sup>F-FDG PET imaging was able to distinguish well between RT-related changes and tumour recurrence in patients with lung cancer.</p></div>","PeriodicalId":8007,"journal":{"name":"Annals of Nuclear Medicine","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer\",\"authors\":\"Changhwan Sung, Jungsu S. Oh, Byung Soo Park, Su Ssan Kim, Si Yeol Song, Jong Jin Lee\",\"doi\":\"10.1007/s12149-024-01925-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.</p><h3>Methods</h3><p>We retrospectively recruited 308 patients with lung cancer with RT-related changes observed on <sup>18</sup>F-fluorodeoxyglucose positron emission tomography–computed tomography (<sup>18</sup>F-FDG PET/CT) performed after RT. Patients were labelled as positive or negative for tumour recurrence through histologic diagnosis or clinical follow-up after <sup>18</sup>F-FDG PET/CT. A two-dimensional (2D) slice-based convolutional neural network (CNN) model was created with a total of 3329 slices as input, and performance was evaluated with five independent test sets.</p><h3>Results</h3><p>For the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98–0.99, 95–98%, and 87–95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM).</p><h3>Conclusion</h3><p>The 2D slice-based CNN model using <sup>18</sup>F-FDG PET imaging was able to distinguish well between RT-related changes and tumour recurrence in patients with lung cancer.</p></div>\",\"PeriodicalId\":8007,\"journal\":{\"name\":\"Annals of Nuclear Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Nuclear Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12149-024-01925-5\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Nuclear Medicine","FirstCategoryId":"3","ListUrlMain":"https://link.springer.com/article/10.1007/s12149-024-01925-5","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer
Objective
We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.
Methods
We retrospectively recruited 308 patients with lung cancer with RT-related changes observed on 18F-fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET/CT) performed after RT. Patients were labelled as positive or negative for tumour recurrence through histologic diagnosis or clinical follow-up after 18F-FDG PET/CT. A two-dimensional (2D) slice-based convolutional neural network (CNN) model was created with a total of 3329 slices as input, and performance was evaluated with five independent test sets.
Results
For the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98–0.99, 95–98%, and 87–95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM).
Conclusion
The 2D slice-based CNN model using 18F-FDG PET imaging was able to distinguish well between RT-related changes and tumour recurrence in patients with lung cancer.
期刊介绍:
Annals of Nuclear Medicine is an official journal of the Japanese Society of Nuclear Medicine. It develops the appropriate application of radioactive substances and stable nuclides in the field of medicine.
The journal promotes the exchange of ideas and information and research in nuclear medicine and includes the medical application of radionuclides and related subjects. It presents original articles, short communications, reviews and letters to the editor.