Ultrasound radiomics model based on grayscale transrectal ultrasound-guided biopsy for diagnosing prostate cancer and predicting distant metastasis.

Objective: A prostate ultrasound (US) imaging omics model was established to assess its effectiveness in diagnosing prostate cancer (PCa), predicting Gleason score (GS), and determining the likelihood of distant metastasis.

Methods: US images of patients with prostate pathology confirmed by biopsy or surgery at our hospital were retrospectively analyzed. Regions of interest (ROI) segmentation, feature extraction, feature screening, and the construction and training of the radiomics model were performed.

Results: Area under the curve (AUC) for the magnetic resonance imaging Prostate Imaging Reporting and Data System (MRI PI-RADS) classification, radiomics alone, and radiomics combined with prostate-specific antigen (PSA) in diagnosing PCa were 70.74%, 71.13%, and 90.47%, respectively. AUCs for the MRI PI-RADS classification, radiomics alone, and radiomics combined with PSA in predicting the GS of PCa were 75.6%, 74.7%, and 88.9%, respectively. Furthermore, AUCs for MRI PI-RADS classification and radiomics alone in predicting distant metastasis of PCa were 66.7% and 90.8%, respectively.

Conclusion: The combination of ultrasonic imaging omics and serum PSA significantly improves the efficiency of PCa diagnosis, GS prediction, and distant metastasis prediction. This method is an important tool for PCa screening and follow-up.