Session Item

The gold standard for the evaluation of prostate cancer (PCa) aggressiveness is the Gleason score (GS), which requires a histopathological analysis to discriminate between clinically significant (CS, GS≥ 7) and non-significant (non-CS, GS=6) cases. The aim of this study was to develop a non-invasive tool able to predict the GS classification of PCa, based on the information extracted from multiparametric magnetic resonance imaging (mpMRI), by using machine learning (ML) tools. Additionally, the impact on the model performance of the feature selection method, as well as the inclusion of clinical data and qualitative image information was assessed.

 This retrospective cohort included 86 adult male patients with positive biopsy for PCa, made by fusion technique (mpMRI-ultrasound) at Hospital Clínico de la Pontificia Universidad Católica de Chile between 2017 and 2021, with lesions greater than 5 mm. 2D segmentations of the target prostate lesions were made by experienced radiologists in T2 weighted (T2w)/Apparent Diffusion Coefficient (ADC) map images at a 3T scanner. A radiomic analysis was performed considering first order, textural and shape features, besides clinical information, including qualitative image information such as PIRADS-v2. Splitting the dataset on train/test (80%) and validation sets (20%), univariate and multivariate models were built using manual and automatic feature selection algorithms. In order to evaluate the performance of the models, twofold cross-validation (CV) was employed with an 80%/20% split for the train/test groups respectively. In particular, we used the Repeated Stratified KFold CV technique with 1000 repetitions, with the Area Under the Curve (AUC) as the evaluation metric. The manual selection method was based on individual feature performance and correlation, using parametric and non-parametric statistical hypothesis tests, Pearson correlation, and predictive power with bootstrap AUC analysis. A comparison between models was performed using Frequentist and Bayesian correlated t-tests.

The best model found was multivariate, obtained using the automatic feature selection algorithm Recursive Feature Elimination (RFE), with Logistic Regression as estimator with nine features, including image (T2w and ADC) and clinical information. The train/test mean AUC was 0.91 (0.06) [0.75−0.99] (p-value<0.05), with a validation AUC of 0.91 for a classification of high-lower aggressiveness (GS≥7 vs GS=6). The Bayesian tests confirmed that our best model performed is better than the best univariate model and multivariate models considering only image features or clinical information, with probability values of 0.94, 0.69 and 0.78, respectively.

Combining MRI-based radiomic and clinical information can significantly improve the model performance to classify PCa aggressiveness. An additional cohort will be required to evaluate the applicability of this tool in a multi-center and multi-scanner setting.