Figure 3

Workflow of our ML-based framework used to identify the best combination of radiomic features and classification algorithm for categorizing PCa patients into high-risk and lower-risk categories. Cross-validation was used to identify the best performing algorithm out of seven commonly used algorithms, which was then used to train the final classifier on the entire development set (68 PCa patients). This classifier was then evaluated on an independent validation set of 53 PCa patients in terms of a variety of performance measures, namely AUC, F_max, P_max and R_max.