The Value of Combined Clinical-Radiomics-Deep LearningModels for Prediction Gleason Grade Group

Motivation: Gleason Score (GS) could only be obtained through biopsy or radical prostatectomy (RP), which might carry a multitude of complications and pose additional financial burdens and emotional strain. Goal(s): To explore the predictive value of mixed model combined clinical features, radiomics features and deep learning features for GS. Approach: The mixed model was constructed to classify grade group 0 (GG0) (benign), GG1, GG2, GG3, GG4 and GG5. DenseNet was used to establish the model. Results: The mixed model had the best predictive ability, with Kw of 0.74 and relative accuracy of 0.76. Impact: Clinicians could obtain GS without biopsy or surgery, which could avoid a lot of complications and financial burdens. Future studies could integrate automated VOI segmentation algorithm to optimize AI model.