Session Item

The most common systems used to risk-stratify PCa patients has been repeatedly shown to have suboptimal prognostic and low-end stratification performances, possibly leading to over- or under-treatment of the patients. The aim of the present study is to test the ability of high-performance mathematical models employing clinical radiological and radiomics features to improve the accuracy of non-invasive prediction of pathological features of PCa and therefore improving patients’ stratification in a large cohort of patients treated and managed at the same institution.

A cohort of 949 patients who have undergone mpMRI of the prostate and prostatectomy in IEO between 2015 and 2018 was selected. The prostate gland was segmented with an internally-developed deep learning segmentation algorithm. Gradient-boosted decision tree models were separately trained using pre-treatment clinical features alone and in combination with radiological and/or radiomic features to predict pathological scores, as well as biochemical and clinical progression (Figure 1). Models were validated with 32-times repeated 5-fold cross-validation and evaluated in terms of their AUC values. The behavior of all features-model within different risk groups and PI-RADS categories was analyzed, assessing radiomic contribution through the cumulative SHAP value and the mean absolute error (MAE). A comparison regarding misclassified patients between the models and the clinical workflow was performed as well.

The AUC performance of the four models can be seen in Figure 2a. The model including all variables resulted the best model in most endpoints. Radiomics appear to bring a measurable boost in model performances, although small. Considering the model including all features, SHAP subgroup analyses showed that, although the mean/median influence of radiomic features is low, their contribution to individual patients prediction can be very high; moreover, MAE values resulted lower in low-risk and low-PIRADS classes (Figure 2b). The best prediction model outperformed the naïve one in all the considered endpoints in terms of AUC, whereas the accuracies were comparable (Figure 2c).

Our results highlight the potential benefit of mathematical models including clinical, radiological and radiomics variables for pathological features prediction in prostate cancer. These results are of considerable interest to inform the clinical decision-making process and can provide valuable information for personalizing therapy, helping identify the correct stage of the disease and guiding the clinical course.