Multi-Multimodality Integrated Stack-Ensemble Learning for the Prediction of Gleason Grade and Prognostic Outcome in Prostate Cancer: A Proof-of-Concept Study

Purpose: To develop a generalizable model, namely PRISK, for the prediction of Gleason grade and prognostic outcome in prostate cancer (PCa) with multiple clinical factors and multiparametric (mp) MRI using stack-ensemble learning. Methods PRISK is developed to primarily assess PCa Gleason grade between benign (pG0), 3 + 3 (pG1), 3 + 4 (pG2), 4 + 3 (pG3) and ≥ 4 + 4 (pG4) and secondly predict the biochemical recurrence (BCR) after radical prostatectomy (RP). PRISK was developed with a stacked-ensemble learning of large-scale clinical identifications and mpMRI data in 671 training datasets, and was validated in 232 internal and 539 external datasets. Results The stacked-ensemble learning of mpMRI delivered a Radiomics-score and 5 transfer learning signatures from 5 deep transfer learning embedders. The PRISK, build with 10 clinical and imaging embedded predictors, achieved an area under the roc curve of 0.783, 0.798 and 0.762 in training, internal and external validation data for classifying Gleason grade, respectively. Specially, combined use of prostate-specific antigen (PSA), PI-RADS and Radiomics-score had excellent negative predictive value (94.1%) for clinical insignificant disease (pG0-1) and high positive predictive value (79.8%) for high-risk PCa (pG4). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; 95% confidence intervals [CIs], 1.20–2.08; p  = 0.001) and PRISK ≥ G3 (OR, 1.45; 95% CI, 1.12–1.88; p  = 0.005) were independent predictors of BCR, with a C-index of 0.76 (95% CI, 0.73–0.79) for predicting BCR by Cox analysis. Conclusions We concluded that the PRISK can offer a noninvasive alternative to stratify PCa Gleason grade. This enables a step towards PCa risk stratification.