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

Yingjie Xv,Yineng Zheng,Xiang Zhou,Hao Tan,Mingzhao Xiao,Fajin Lv,Haoming Guo

doi:10.1186/s13244-021-01107-1

Abstract

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.

Highlights

Renal cell carcinoma accounts for 5% and 3% of all diagnosed cancers in men and women, respectively, and clear cell renal cell carcinoma (CCRCC) represents the most common subtype (∼ 80%) [1,2,3]
Radiomics feature extraction All images were preprocessed before radiomics feature extraction as follows: first, the images and Region of interest (ROI) were resampled to an isotropic voxel size of 1 × 1 × 1 mm3 using B-spline interpolation; second, we focused on the chosen region and divided by standard deviation to normalize the images; third, the gray level of the image was discretized by a fixed bin width of 25 in the histogram
330 patients were diagnosed with low-grade CCRCC, whereas the rest were diagnosed with high-grade CCRCC

Summary

Introduction

Renal cell carcinoma accounts for 5% and 3% of all diagnosed cancers in men and women, respectively, and clear cell renal cell carcinoma (CCRCC) represents the most common subtype (∼ 80%) [1,2,3]. The four-tiered Fuhrman grading system (FGS) for the pathological classification of CCRCC is widely used before, the 2016 World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system has achieved widespread usage and has replaced the FGS globally [7, 8]. This system can be simplified as two-tiered classification combining grade I and II as low-grade and grade III and IV as high-grade. Low-grade cancers are generally considered less aggressive than high-grade ones [9]. The two-tiered classification has been verified to predict cancer-specific mortality and guide clinical practice in the same way as four-tiered systems, while it can reduce inter-observer variability and promote clinical practice [10, 11]

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