Abstract 5060: A deep learning based pancreatic adenocarcinoma survival prediction model applicable to adenocarcinoma of other organs

Abstract

Abstract Introduction: To analyze how the histomorphological features of adenocarcinoma lesions are related to the survival of patients, we developed a deep learning based pancreatic adenocarcinoma survival model that predicts death risk through the general adenocarcinoma feature extractor (GAFE). Our pancreatic adenocarcinoma survival model and GAFE was trained with hematoxylin and eosin (H&E) stained whole slide images (WSIs). Also, to measure the generalization performance, we predict the death risk of rectum adenocarcinoma and breast adenocarcinoma using the pancreatic adenocarcinoma survival model. Design: The whole data analyzed in this study was from The Cancer Genome Atlas (TCGA). We used a self-supervised contrastive learning method to train GAFE with randomly chosen 328 adenocarcinoma H&E stained WSIs. These WSIs are consisted of 92 lung (TCGA-LUAD), 100 colon (TCGA-COAD), 97 prostate (TCGA-PRAD) and 39 stomach (TCGA-STAD) adenocarcinoma WSIs. For the pancreatic adenocarcinoma survival model, we used 179 WSIs of TCGA pancreatic adenocarcinoma (TCGA-PAAD) dataset labeled with survival events and periods. There existed 66 uncensored data, and we used 5-fold cross validation. Each WSI was divided into 256*256 pixel-wise patches. Feature vectors were extracted from the patches using the GAFE. Then the attention-based multi-instance learning was applied to train the survival model. The trained model returned a death risk for each patient. We used 122 adenocarcinoma H&E stained WSIs of rectum (TCGA-READ) and 999 of breast (TCGA-BRCA) from TCGA to test the generalization performance for different organs. Result: We used 5-fold cross validation for testing the pancreatic adenocarcinoma survival model. The mean value of C-index from the TCGA-PAAD test datasets was 0.7258 where the highest and the lowest C-index was 0.7784 and 0.6598. Using the model with median performance (C-index: 0.7216), we measured death risk and divided patients into 50% of high-risk and 50% of low-risk groups. From this, the p-value was 0.01986 for the log-rank test.To analyze the performance of the generalization, we ensembled 5 models from the above. We tested our ensembled model on TCGA-READ and TCGA-BRCA. The C-index for TCGA-Read was 0.6941 where the C-index for TCGA-BRCA was 0.5711. Conclusion: We found that adenocarcinoma’s histomorphological features had some correlation with pancreatic adenocarcinoma survival prediction. Moreover, we could see the possibility of transferability of adenocarcinoma histomorphological knowledge and survival analysis to other organs such as rectum adenocarcinoma and breast adenocarcinoma. Citation Format: Joonho Lee, Geongyu Lee, Tae-Yeong Kwak, Sun Woo Kim, Hyeyoon Chang. A deep learning based pancreatic adenocarcinoma survival prediction model applicable to adenocarcinoma of other organs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5060.