Radiomics: A New Biomedical Workflow to Create a Predictive Model

Abstract

‘Radiomics’ is utilized to improve the prediction of patient overall survival and/or outcome. Target segmentation, feature extraction, feature selection, and classification model are the fundamental blocks of a radiomics workflow. Nevertheless, these blocks can be affected by several issues, i.e. high inter- and intra-observer variability. To overcome these issues obtaining reproducible results, we propose a novel radiomics workflow to identify a relevant prognostic model concerning a real clinical problem. In the specific, we propose an operator-independent segmentation system with the consequent automatic extraction of radiomics features, and a novel feature selection approach to create a relevant predictive model in 46 patients with prostate lesion underwent magnetic resonance imaging. In the specific, using an operator-independent method of target segmentation based on an active contour, ad-hoc automated high-throughput analysis tool capable of calculating a total of 290 radiomics features for each imaging sequence, a novel statistical system for feature reduction and selection, and the discriminant analysis as a method for feature classification, we propose a performant and replicable radiomics workflow for the diagnosis of prostate cancer. The proposed workflow revealed three and five relevant features on T2-weighted and apparent diffusion coefficient (ADC) maps images, respectively, that were significantly correlated with the histopathological results. In the specific, good performance in lesion discrimination was obtained using the combination of the selected features (accuracy 76.76% and 75.20%, for T2-weighted and ADC maps images, respectively) in an operator-independent and automatic way.