Automatic Classification Of Benign And Malignant Kidney Masses Using Radiomics. A Retrospective Study On Kits19 Dataset

The study aims to assess the performance in differentiating benign from malignant kidney masses using a radiomics approach.For this retrospective study we worked with the scans of 210 patients from the publicly available KiTS19 dataset. Each scan had segmentations of the healthy kidney tissue, benign lesions and malignant tumors. In Phase 1 of our study, we reduced the number of radiomic features (105) extracted from the scans by using four feature selection and ranking algorithms: recursive feature elimination (RFE), fisher score, partial least square discriminant analysis (PLS-DA) and linear support vector machine (l-SVM). The features selected by each method were then used to train a series of random forest (RF) classifiers. In Phase 2, we trained a convolutional neural network (CNN) to automatically perform the segmentation of benign and malignant kidney masses. We then placed the best performing RF classifier from Phase 1 in series with the CNN to see if it corrected its prediction.The best classification performance was obtained when training a RF classifier with the 8 features selected by the RFE method (accuracy: 0.974). This RF model applied to the segmentations derived from the neural network improved the CNN's overall results: the dice score for malignant mass went from 0.74 to 0.79 and dice score for benign mass from 0.55 to 0.80.The studied radiomics approach proved to be an accurate solution to classify benign and malignant kidney masses. A deep learning algorithm has shown to also benefit from its predictive power.