Machine Learning-Based Prediction of Gleason Grade Group upgrading in Patients with Localized Prostate Cancer Awaiting Surgery

Background: Despite the improved precision of the MRI fusion prostate biopsy, discrepancies persist between the Gleason grade group (GG) biopsy and the pathological Gleason GG. Our study employs machine learning to predict the upgrading of the Gleason GG, aiding treatment decisions. Material & Methods: Since 2009, we retrospectively reviewed localized prostate cancer patients who underwent prostatectomy, considering seven potential factors contributing to the upgrading: age, prostate specific antigen (PSA) level, PSA density, biopsy GG, Prostate Imaging-Reporting and Data Systems, percent positive cores and surgical waiting time. Pearson'scorrelation and principal component analysis(PCA) were used to explore the data. Various machine learning models were employed for comparison. Results : Of 418 patients, neither the Pearson correlation nor the PCA revealed strong correlations with GG upgrading. Logistic regression (LR) achieved the best F1 score, though all models had F1 scores below 0.5, indicating prediction challenges. LR and Neural Network analysis identified biopsy GG, age, and percent positive cores as significant predictors. Conclusions: No specific features strongly correlated with GG upgrading. Despite high accuracy, intelligent concepts struggled to predict upgrades effectively. Physician expertise and patient characteristics remain crucial for management decisions. We agree that machine learning has great potential to improve prediction in the future.