Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Insights into Imaging

Fajin Lv¹  Haoming Guo¹  Yineng Zheng¹  Yingjie Xv²  Hao Tan³  Xiang Zhou³  Mingzhao Xiao³ 

[1] Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, 400016, Chongqing, Yuzhong, China;Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, 400016, Chongqing, Yuzhong, China;Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, 400016, Chongqing, Yuzhong, China;Department of Urology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, 400016, Chongqing, Yuzhong, China;

关键词: Machine learning;    Tomography (X-ray computed);    Clear cell renal cell carcinoma;    WHO/ISUP grading;    Prediction model;   

DOI  :  10.1186/s13244-021-01107-1

来源: Springer

【 摘 要 】

PurposeTo investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs).MethodsThis retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses.ResultsSVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model.ConclusionA combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

【 授权许可】

CC BY