Predicting Renal Cell Carcinoma Subtypes and Fuhrman Grading Using Multiphasic CT-Based Texture Analysis and Machine Learning Techniques.

Abstract

Objectives  The aim of this study is to evaluate computed tomography texture analysis (CTTA) on multiphase CT scans for distinguishing clear cell renal cell carcinoma (ccRCC) from non-ccRCC and predicting Fuhrman's grade in ccRCC using open-source Python libraries. Materials and Methods  Conducted retrospectively, the study included 144 patients with RCCs (108 ccRCCs and 36 non-ccRCCs) who underwent preoperative multiphasic CT. Ninety ccRCCs were categorized into 71 low-grade and 19 high-grade ccRCCs. Tumor was marked on the largest axial tumor slice using "LabelMe" across different CT phases. First- and second-order texture features were computed using Python's scipy, numpy, and opencv libraries. Multivariable logistic regression analysis and machine learning (ML) models were used to evaluate CTTA parameters from different CT phases for RCC classification. The best ML model for distinguishing ccRCC and non-ccRCC was externally validated using data from the 2019 Kidney and Kidney Tumor Segmentation Challenge. Results  Entropy in the corticomedullary (CM) phase was the best individual parameter for distinguishing ccRCC from non-ccRCC with (F1 score: 0.83). The support vector machine (SVM) based ML model, incorporating CM phase features, performed the best, with an F1 score of 0.87. External validation for the same model yielded an accuracy of 0.82 and an F1 score of 0.81. ML models and individual texture parameters showed less accuracy for classifying low- versus high-grade ccRCCs, with a maximum F1 score of 0.76 for the CM phase SVM model. Other CT phases yielded inferior results for both classification tasks. Conclusion  CTTA employing open-source Python tools is a viable tool for differentiating ccRCCs from non-ccRCCs and predicting ccRCC grade.