The value of radiomics based on 2-[18 F]FDG PET/CT in predicting WHO/ISUP grade of clear cell renal cell carcinoma.

IF 3.1 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

EJNMMI Research Pub Date : 2024-11-21 DOI:10.1186/s13550-024-01182-7

Yun Han, Guanyun Wang, Jingfeng Zhang, Yue Pan, Jianbo Cui, Can Li, Yanmei Wang, Xiaodan Xu, Baixuan Xu

{"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":"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.","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}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

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。

背景：目的：开发并验证基于2-[18F]氟-D-葡萄糖正电子发射断层扫描/计算机断层扫描（2-[18F]FDG PET/CT）参数的放射组学，用于预测世界卫生组织/国际泌尿病理学会（WHO/ISUP）透明细胞肾细胞癌（ccRCC）的分级：我们的研究共纳入了209名患者，他们在2016年12月至2023年12月期间接受了2-[18F]FDG PET/CT扫描，共发现214个病灶。所有ccRCC病变均分为低级别（WHO/ISUP I-II级）和高级别（WHO/ISUP III-IV级）。病变按 7:3 的比例分为训练组和测试组。放射组学特征通过最大标准化摄取值（SUVmax）阈值（0,2.5%,25%,40%），利用最小冗余和最大相关性（mRMR）以及最小绝对收缩和选择算子（LASSO）回归算法进行提取。建立了临床模型、放射组学模型和综合模型。绘制了接收者操作特征曲线（ROC）、决策曲线和校准曲线，以评估预测性能：训练组 PET-0、PET-2.5%、PET-25%、PET-40% 模型的曲线下面积（AUC）分别为 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）；测试组为 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）。由于预测和验证性能完美，我们选择了SUVmax阈值为40%的PET图像中的感兴趣体积（VOI）来构建放射组学模型和组合模型。在训练组中，临床模型和放射组学模型的AUC分别为0.859（灵敏度=0.846，特异度=0.747）和0.909（灵敏度=0.808，特异度=0.751）；在测试组中，临床模型和放射组学模型的AUC分别为0.882（灵敏度=0.857，特异度=0.857）和0.901（灵敏度=0.905，特异度=0.833）。在综合模型中，训练组的AUC为0.916，灵敏度为0.923，特异性为0.808；训练组的AUC为0.916，灵敏度为0.881，特异性为0.792：基于2-[18F]FDG PET/CT的放射组学有助于预测ccRCC的WHO/ISUP分级。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

EJNMMI Research RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING&nb-

CiteScore

5.90

自引率

3.10%

发文量

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.