Benchmarking Machine Learning Algorithms for Diagnosis of Renal Cell Carcinoma

Abstract

Background: Accurate differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC) is important in RCC diagnosis. Objectives: This study aimed to evaluate the performance of different supervised machine learning (ML) algorithms for RCC based on computed tomography (CT) examinations. Patients and Methods: The CT images of known cases of RCC or renal AML were collected and divided into training and testing groups. The texture features of CT images were drawn and quantified in MaZda software; a total of 352 features were drawn from each image. Top 10 features with statistical significance for differentiation of RCC from benign tumors in the training group were selected to establish diagnosis models based on 16 supervised ML algorithms. Next, the models were compared regarding accuracy and specificity. The trained models were further examined by comparison with data from the testing group. Results: Among 16 classifiers trained in this study, the logistic regression, linear discriminant analysis, k-nearest neighbor algorithm, support vector machines (SVMs), ridge classifier, AdaBoost classifier, gradient boosting classifier, and CatBoost classifier showed good performance in discriminating RCC from AML (accuracy, ≥ 0.7; area under the [receiver operating characteristic [ROC]] curve [AUC] ≥ 0.75) in both training and testing datasets. Conclusion: Based on the ML algorithms for big data, diagnostic classifiers can be valuable tools for an accurate diagnosis of RCC. By comparing different algorithms, the present results indicated potential algorithms for the development of RCC diagnostic classifiers.