Abstract PO-030: Deep-learning image-based tumor, stroma, and lymphocytes spatial relationships and clinicopathological features that affect survival in pancreatic cancer patients
Authors: Jaber Mustafa I. , Beziaeva Liudmila , Torphy Robert J. , Benz Stephen C. , Rabizadeh Shahrooz , Soon-Shiong Patrick , Szeto Christopher W 
Published: November 2020

Background: Stromal and lymphocyte density have each been implicated in differential survival in pancreatic cancer (Torphy, 2018 & Orhan, 2020). In this study, we developed an automated deep-learning system to provide risk-assessment upon spatial relationships between tumor, stroma, and lymphocyte regions in pancreatic pathology images. Methods: Diagnostic H&E-stained pathology images from 82 pancreatic adenocarcinoma patients who underwent chemotherapy were acquired from TCGA sources. Thirty-two patients were held out for testing purposes. Tumor, stroma, and lymphocytes image masks were generated using pre-trained convolutional neural networks, and their co-distribution was summarized in nine numerical image-based features. Optimal thresholds in these image-based features were identified using 2-way Gaussian mixture models. This process found four spatial image features that significantly contributed to low-risk of early death: Low lymphocyte count, lymphocytes adjacent to tumor regions, and stromal adjacency to tumor regions. Ability to separate patients based on these features was evaluated using silhouette score, concordance index, and Cox proportional hazards ratios (HR). Results: Without using image-based features, exhaustive search of this cohort’s clinicopathological features found an optimal Cox proportional hazards model can yield a HR = 0.22 (p = 0.02) in 50 training examples and HR = 0.41 (p = 0.29) on 32 unseen test patients, ultimately utilizing just pathologically-determined T, and N information. The developed image-based risk predictor improved performance with HR = 0.51 (p = 0.06) on training data and HR = 0.52 (p = 0.09) on unseen test data. Combining the image-based risk models to selected clinicopathological features enhanced performance further to HR = 0.25 (p = 0.01) on the training set and HR = 0.37 (p = 0.07) on unseen test patients. Conclusions: Our interpretable image-based risk predictor shows high-risk pancreatic cancer patients have higher lymphocyte count overall but proportionally fewer tumor-infiltrating lymphocytes (TILs). In addition, this system shows high-risk patients have less stromal tissue within 100um from tumor compared to low risk patients. By aggregating both standard clinicopathological features with the proposed image-based risk assessment, superior separation in survival curves was achieved for both training and testing sets compared to either risk-model alone. Thus, our study demonstrates that image-based risk-associated features are independently prognostic of clinicopathological features. Despite the very limited sample-size of similarly-treated patients within the training dataset, these results trend towards significance and warrant further study within a larger cohort.

Citation Format: Mustafa I. Jaber, Liudmila Beziaeva, Robert J. Torphy, Stephen C. Benz, Shahrooz Rabizadeh, Patrick Soon-Shiong, Christopher W Szeto. Deep-learning image-based tumor, stroma, and lymphocytes spatial relationships and clinicopathological features that affect survival in pancreatic cancer patients [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PO-030.