Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology

Abstract This study aimed to enhance the accuracy of Gleason grade group (GG) upgrade prediction in prostate cancer (PCa) patients who underwent MRI-guided in-bore biopsy (MRGB) and radical prostatectomy (RP) through a combined analysis of prebiopsy and MRGB clinical data. A retrospective analysis of 95 patients with prostate cancer diagnosed by MRGB was conducted where all patients had undergone RP. Among the patients, 64. 2% had consistent GG results between in-bore biopsies and RP, whereas 28. 4% had upgraded and 7. 4% had downgraded results. GG1 biopsy results, lower biopsy core count, and fewer positive cores were correlated with upgrades in the entire patient group. In patients with GG>1 GG > 1, larger tumor sizes and fewer biopsy cores were associated with upgrades. By integrating MRGB data with prebiopsy clinical data, machine learning (ML) models achieved 85. 6% accuracy in predicting upgrades, surpassing the 64. 2% baseline from MRGB alone. ML analysis also highlighted the value of the minimum apparent diffusion coefficient (ADC₌₈₍ ADC min) for GG>1 GG > 1 patients. Incorporation of MRGB results with tumor size, ADC₌₈₍ ADC min value, number of biopsy cores, positive core count, and Gleason grade can be useful to predict GG upgrade at final pathology and guide patient selection for active surveillance.