PD-4 Deep learning of pancreatic cancer histopathology images predicts recurrence after surgery

Pancreatic cancer is an aggressive malignancy, with heterogeneous outcomes after surgery that are poorly explained with currently available clinical and genomic features. New biomarkers may lead to improved personalized treatment strategies. In this study, we applied deep learning to digitized whole-slide histopathology images from pancreatic cancer resections to predict overall survival. All patients received curative-intent surgical resection for pancreatic ductal adenocarcinoma. The training cohort included publicly-available data from The Cancer Genome Atlas (N=111), the Australian International Cancer Genome Consortium (N=37), and the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (N=44). Patients from the Princess Margaret Cancer Centre comprised the test cohort (N=191). 17,512 4096x4096 pixel patches were represented within embeddings from a hierarchical image pyramid transformer, which was pre-trained with self-supervision on over 10,000 slides from 33 cancer types in The Cancer Genome Atlas. Slide-level predictions were generated using a fully connected network on mean pooled patch embeddings. The target of the deep learning system was overall survival, optimized using a Cox loss function. The training cohort was randomly split 75:25% for hyperparameter tuning and model selection. Our primary evaluation metric was the concordance index in the test cohort. To facilitate clinical interpretation, we grouped patients into high and low risk based on the median prediction in the tuning cohort. Survival times were estimated using the Kaplan-Meier approach and hazard ratios were estimated using univariate and multivariate Cox proportional hazard regressions. In the tuning cohort (N=65), the concordance index from the best model was 0.64 (95% confidence interval [CI], 0.54-0.73, P=0.005). The hazard ratio for death comparing high versus low risk cancers was 2.95 (95% CI 1.26-6.93, P=0.01). In the test cohort (79.6% grade 2, 8.4% grade 3, 76.4% node positive, 10.5% margin positive, 33.5% over 70 years, 49.2% female), 116/191 (60.7%) patients died. The deep learning system achieved a concordance index of 0.63 (95% CI 0.57-0.69, P < 0.001). The median overall survival in the high-risk group was 17.4 months (95% CI 13.7-23.6), compared to 32.4 (95% CI 26.2-41.5) in the low-risk group (univariate hazard ratio 1.99, 95% CI 1.37-2.90, P < 0.001; multivariate hazard ratio 2.11, 95% CI 1.39-3.21, P < 0.001). Predictions were weakly correlated with grade (Kendall Tau coefficient 0.23, P < 0.001). The prognostic value of the deep learning system persisted when applied to grade 2 cancers (concordance index 0.63, 0.57-0.70, P < 0.001). A deep learning system applied to whole-slide histopathology images predicts overall survival after surgical resection of pancreatic cancer, providing complementary information beyond traditional grading. Our system could identify patients who may benefit from personalized intensified therapy within clinical trials. Furthermore, our approach could be applied in future research to guide other therapeutic decisions, for example, in the neoadjuvant and metastatic settings.