Abstract
PURPOSE: To our knowledge the ideal methodology of quantifying secondary Gleason pattern 4 in men with Grade Group 2/Gleason score 3 + 4 = 7 on biopsy remains unknown. We compared various methods of Gleason pattern 4 quantification and evaluated associations with adverse pathology findings at radical prostatectomy. MATERIALS AND METHODS: A total of 457 men with Grade Group 2 prostate cancer on biopsy subsequently underwent radical prostatectomy at our institution. Only patients with 12 or more reviewed cores were included in analysis. We evaluated 3 methods of quantifying Gleason pattern 4, including the maximum percent of Gleason pattern 4 in any single core, the overall percent of Gleason pattern 4 (Gleason pattern 4 mm/total cancer mm) and the total length of Gleason pattern 4 in mm across all cores. Adverse pathology features at radical prostatectomy were defined as Gleason score 4 + 3 = 7 or greater (Grade Group 3 or greater), and any extraprostatic extension, seminal vesical invasion and/or lymph node metastasis. A training/test set approach and multivariable logistic regression were used to determine whether Gleason pattern 4 quantification methods could aid in predicting adverse pathology. RESULTS: On multivariable analysis all Gleason pattern 4 quantification methods were significantly associated with an increased risk of adverse pathology (p <0.0001) and an increased AUC beyond the base model. The largest AUC increase was 0.044 for the total length of Gleason pattern 4 (AUC 0.728, 95% CI 0.663-0.793). Decision curve analysis demonstrated an increased clinical net benefit with the addition of Gleason pattern 4 quantification to the base model. The total length of Gleason pattern 4 clearly provided the largest net benefit. CONCLUSIONS: Our findings support the inclusion of Gleason pattern 4 quantification in the pathology reports and risk prediction models of patients with Grade Group 2/Gleason score 3 + 4 = 7 prostate cancer. The total length of Gleason pattern 4 across all cores provided the strongest benefit to predict adverse pathology features.