Convolutional neural network quantification of Gleason pattern 4 and association with biochemical recurrence in intermediate grade prostate tumors

Abstract

Differential classification of prostate cancer (CaP) grade group (GG) 2 and 3 tumors remains challenging, likely due to the subjective quantification of percentage of Gleason pattern 4 (%GP4). Artificial intelligence assessment of %GP4 may improve its accuracy and reproducibility and provide information for prognosis prediction. To investigate this potential, a convolutional neural network (CNN) model was trained to objectively identify and quantify Gleason pattern (GP) 3 and 4 areas, estimate %GP4, and assess whether CNN-assessed %GP4 is associated with biochemical recurrence (BCR) risk in intermediate risk GG 2 and 3 tumors. The study was conducted in a radical prostatectomy cohort (1999–2012) of African American men from the Henry Ford Health System (Detroit, Michigan). A CNN model that could discriminate four tissue types (stroma, benign glands, GP3 glands, and GP4 glands) was developed using histopathologic images containing GG 1 (n = 45) and 4 (n = 20) tumor foci. The CNN model was applied to GG 2 (n = 153) and 3 (n = 62) for %GP4 estimation, and Cox proportional hazard modeling was used to assess the association of %GP4 and BCR, accounting for other clinicopathologic features including GG. The CNN model achieved an overall accuracy of 86% in distinguishing the four tissue types. Further, CNN-assessed %GP4 was significantly higher in GG 3 compared with GG 2 tumors (p = 7.2*10− 11). %GP4 was associated with an increased risk of BCR (adjusted HR = 1.09 per 10% increase in %GP4, p = 0.010) in GG 2 and 3 tumors. Within GG 2 tumors specifically, %GP4 was more strongly associated with BCR (adjusted HR = 1.12, p = 0.006). Our findings demonstrate the feasibility of CNN-assessed %GP4 estimation, which is associated with BCR risk. This objective approach could be added to the standard pathological assessment for patients with GG 2 and 3 tumors and act as a surrogate for specialist genitourinary pathologist evaluation when such consultation is not available.

Contemporary Diagnostic Reporting for Prostatic Adenocarcinoma: Morphologic Aspects, Molecular Correlates, and Management Perspectives

“…Moreover, at least some experience documents that the percentage of Gleason pattern 4 is relatively reproducible, 49 particularly for foci of carcinoma that demonstrate significant (>10%) involvement of the core, 50 or when the Gleason 4 component is more clustered rather than admixed 51 . Future strategies might even employ artificial intelligence/machine learning techniques to facilitate standardization of percentage estimation 52 . Interestingly, higher percentages of Gleason 4 pattern are often associated with the presence of multiple subpatterns of Gleason grade 4 44 …”