Radiomics feature based benign vs. malignant characterization of solid renal masses on MRI

Magnetic resonance imaging (MRI) is well suited for Solid renal masses (SRMs) characterization (e.g., benign vs. malignant) due to its superior soft tissue contrast. Though renal mass detection and characterization using deep-learning (DL) methods have been extensively studied for computed tomography (CT) images, those same tasks are yet to be investigated on MRI images. SRMs need active surveillance as they consist of biologically diverse heterogeneous groups of benign or malignant masses. Among them, malignant clear cell renal carcinoma (ccRCC) is frequently aggressive. There are inter-observer and intra-observer differences in the assessment of SRMs by expert clinicians because of their experience and expertise. Therefore, it is essential to develop a machine learningbased noninvasive imaging diagnosis to distinguish SRMs as benign and malignant. Our retrospective study consisted of malignant (renal cell carcinoma- clear cell, papillary, and chromophobe) and benign (fat-poor angiomyolipoma-fpAML, oncocytomas) SRMs. We extracted first and second-order radiomics features from SRMs on T2W and T1W-CM MRI to train different machine learning (ML) models using the 5-fold cross-validation for benign vs malignant classification. The support vector machine (SVM) algorithm generated benign vs malignant classification accuracy of 90.00% with ROC-AUC of 76.19% on T2W MRI and the custom-designed multilayer perceptron model (MLP) model produced accuracy of 80.00% with ROC-AUC of 75.47% on T1W-CM MRI. Thus, ML-based radiomics features classification of SRMs extracted on MRI may be an alternative to biopsy using a non-invasive assessment of SRMs.