{"title":"基于 2-[18 F]FDG PET/CT 的放射组学在预测透明细胞肾细胞癌 WHO/ISUP 分级中的价值。","authors":"Yun Han, Guanyun Wang, Jingfeng Zhang, Yue Pan, Jianbo Cui, Can Li, Yanmei Wang, Xiaodan Xu, Baixuan Xu","doi":"10.1186/s13550-024-01182-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The aim is to develop and validate radiomics based on 2-[<sup>18</sup>F]fluoro-D-glucose positron emission tomography/computed tomography (2-[<sup>18</sup>F]FDG PET/CT) parameters for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of clear cell renal cell carcinoma (ccRCC).</p><p><strong>Methods: </strong>A total of 209 patients with 214 lesions, who underwent 2-[<sup>18</sup>F]FDG PET/CT scans between December 2016 to December 2023, were included in our study. All ccRCC lesions were categorized into low grade (WHO/ISUP grade I-II) and high grade (WHO/ISUP grade III-IV). The lesions were allocated into a training group and a testing group in a ratio of 7:3. The radiomics features were extracted by a serious of maximum standardized uptake value (SUVmax) thresholds (0,2.5%,25%,40%) with the utilization of the minimum redundancy and maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithm. The clinical, radiomics and combined models were constructed. The receiver operating characteristic (ROC) curve, decision curve and calibration curves were plotted to assess the predicting performance.</p><p><strong>Results: </strong>The area under curve (AUC) of PET-0, PET-2.5%, PET-25%, PET-40% model in the training group were 0.881(95% CI: 0.822-0.940),0.883(95% CI: 0.825-0.942),0.889(95% CI: 0.831-0.946),0.887(95% CI: 0.826-0.948); and 0.878(95% CI: 0.777-0.978),0.876(95% CI: 0.776-0.977),0.871(95% CI: 0.769-0.972),0.882(95% CI: 0.786-0.979) in the testing group. Due to perfect prediction and verification performance, the volume of interest (VOI) from PET images with SUVmax threshold of 40% were selected to construct the radiomics model and combined model. The AUC of the clinical model and radiomics model was 0.859 (sensitivity = 0.846, specificity = 0.747) and 0.909 (sensitivity = 0.808, specificity = 0.751) in the training group, respectively; 0.882 (sensitivity = 0.857, specificity = 0.857) and 0.901 (sensitivity = 0.905, specificity = 0.833) in the testing group, respectively. In combined models, the AUC was 0.916, the sensitivity was 0.923 and the specificity was 0.808 in the training group; the AUC was 0.916, the sensitivity was 0.881 and the specificity was 0.792 in the training group.</p><p><strong>Conclusion: </strong>Radiomics based on 2-[<sup>18</sup>F]FDG PET/CT can be helpful to predict WHO/ISUP grade of ccRCC.</p>","PeriodicalId":11611,"journal":{"name":"EJNMMI Research","volume":"14 1","pages":"115"},"PeriodicalIF":3.1000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582283/pdf/","citationCount":"0","resultStr":"{\"title\":\"The value of radiomics based on 2-[18 F]FDG PET/CT in predicting WHO/ISUP grade of clear cell renal cell carcinoma.\",\"authors\":\"Yun Han, Guanyun Wang, Jingfeng Zhang, Yue Pan, Jianbo Cui, Can Li, Yanmei Wang, Xiaodan Xu, Baixuan Xu\",\"doi\":\"10.1186/s13550-024-01182-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The aim is to develop and validate radiomics based on 2-[<sup>18</sup>F]fluoro-D-glucose positron emission tomography/computed tomography (2-[<sup>18</sup>F]FDG PET/CT) parameters for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of clear cell renal cell carcinoma (ccRCC).</p><p><strong>Methods: </strong>A total of 209 patients with 214 lesions, who underwent 2-[<sup>18</sup>F]FDG PET/CT scans between December 2016 to December 2023, were included in our study. All ccRCC lesions were categorized into low grade (WHO/ISUP grade I-II) and high grade (WHO/ISUP grade III-IV). The lesions were allocated into a training group and a testing group in a ratio of 7:3. The radiomics features were extracted by a serious of maximum standardized uptake value (SUVmax) thresholds (0,2.5%,25%,40%) with the utilization of the minimum redundancy and maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithm. The clinical, radiomics and combined models were constructed. The receiver operating characteristic (ROC) curve, decision curve and calibration curves were plotted to assess the predicting performance.</p><p><strong>Results: </strong>The area under curve (AUC) of PET-0, PET-2.5%, PET-25%, PET-40% model in the training group were 0.881(95% CI: 0.822-0.940),0.883(95% CI: 0.825-0.942),0.889(95% CI: 0.831-0.946),0.887(95% CI: 0.826-0.948); and 0.878(95% CI: 0.777-0.978),0.876(95% CI: 0.776-0.977),0.871(95% CI: 0.769-0.972),0.882(95% CI: 0.786-0.979) in the testing group. Due to perfect prediction and verification performance, the volume of interest (VOI) from PET images with SUVmax threshold of 40% were selected to construct the radiomics model and combined model. The AUC of the clinical model and radiomics model was 0.859 (sensitivity = 0.846, specificity = 0.747) and 0.909 (sensitivity = 0.808, specificity = 0.751) in the training group, respectively; 0.882 (sensitivity = 0.857, specificity = 0.857) and 0.901 (sensitivity = 0.905, specificity = 0.833) in the testing group, respectively. In combined models, the AUC was 0.916, the sensitivity was 0.923 and the specificity was 0.808 in the training group; the AUC was 0.916, the sensitivity was 0.881 and the specificity was 0.792 in the training group.</p><p><strong>Conclusion: </strong>Radiomics based on 2-[<sup>18</sup>F]FDG PET/CT can be helpful to predict WHO/ISUP grade of ccRCC.</p>\",\"PeriodicalId\":11611,\"journal\":{\"name\":\"EJNMMI Research\",\"volume\":\"14 1\",\"pages\":\"115\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582283/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EJNMMI Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13550-024-01182-7\",\"RegionNum\":3,\"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":"EJNMMI Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13550-024-01182-7","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
The value of radiomics based on 2-[18 F]FDG PET/CT in predicting WHO/ISUP grade of clear cell renal cell carcinoma.
Background: The aim is to develop and validate radiomics based on 2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) parameters for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of clear cell renal cell carcinoma (ccRCC).
Methods: A total of 209 patients with 214 lesions, who underwent 2-[18F]FDG PET/CT scans between December 2016 to December 2023, were included in our study. All ccRCC lesions were categorized into low grade (WHO/ISUP grade I-II) and high grade (WHO/ISUP grade III-IV). The lesions were allocated into a training group and a testing group in a ratio of 7:3. The radiomics features were extracted by a serious of maximum standardized uptake value (SUVmax) thresholds (0,2.5%,25%,40%) with the utilization of the minimum redundancy and maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithm. The clinical, radiomics and combined models were constructed. The receiver operating characteristic (ROC) curve, decision curve and calibration curves were plotted to assess the predicting performance.
Results: The area under curve (AUC) of PET-0, PET-2.5%, PET-25%, PET-40% model in the training group were 0.881(95% CI: 0.822-0.940),0.883(95% CI: 0.825-0.942),0.889(95% CI: 0.831-0.946),0.887(95% CI: 0.826-0.948); and 0.878(95% CI: 0.777-0.978),0.876(95% CI: 0.776-0.977),0.871(95% CI: 0.769-0.972),0.882(95% CI: 0.786-0.979) in the testing group. Due to perfect prediction and verification performance, the volume of interest (VOI) from PET images with SUVmax threshold of 40% were selected to construct the radiomics model and combined model. The AUC of the clinical model and radiomics model was 0.859 (sensitivity = 0.846, specificity = 0.747) and 0.909 (sensitivity = 0.808, specificity = 0.751) in the training group, respectively; 0.882 (sensitivity = 0.857, specificity = 0.857) and 0.901 (sensitivity = 0.905, specificity = 0.833) in the testing group, respectively. In combined models, the AUC was 0.916, the sensitivity was 0.923 and the specificity was 0.808 in the training group; the AUC was 0.916, the sensitivity was 0.881 and the specificity was 0.792 in the training group.
Conclusion: Radiomics based on 2-[18F]FDG PET/CT can be helpful to predict WHO/ISUP grade of ccRCC.
EJNMMI ResearchRADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING&nb-
CiteScore
5.90
自引率
3.10%
发文量
72
审稿时长
13 weeks
期刊介绍:
EJNMMI Research publishes new basic, translational and clinical research in the field of nuclear medicine and molecular imaging. Regular features include original research articles, rapid communication of preliminary data on innovative research, interesting case reports, editorials, and letters to the editor. Educational articles on basic sciences, fundamental aspects and controversy related to pre-clinical and clinical research or ethical aspects of research are also welcome. Timely reviews provide updates on current applications, issues in imaging research and translational aspects of nuclear medicine and molecular imaging technologies.
The main emphasis is placed on the development of targeted imaging with radiopharmaceuticals within the broader context of molecular probes to enhance understanding and characterisation of the complex biological processes underlying disease and to develop, test and guide new treatment modalities, including radionuclide therapy.