Keywords: Prostate, Prostate, radiomics, deep learning, Gleason score
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.
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