Abstract 2584: Decoding colon cancer recurrence: Unveiling accurate predictions with attention-guided deep neural networks on histopathological whole slide images

Abstract Background: Up to 40% of colon cancer patients are at high risk of cancer recurrence, yet accurate and timely prediction tools are lacking. Leveraging whole slide images (WSIs) and deep learning models, we aimed to develop precise algorithm for prediction of colon cancer recurrence. Thus, enables risk stratification for optimized therapeutic interventions and improved health outcomes. Design: We designed an attention-based deep learning model for predicting colon cancer recurrence on paraffin-embedded, hematoxylin and eosin-stained colon tissue biopsies of digital slides. The WSIs were downloaded from the TCGA dataset, and preprocessed for color normalization, tissue segmentation, tilling, and histopathological features extraction. The entire dataset was then labeled based on cancer recurrence status, and divided into training (70%), validation (15%), and testing sets (15%). The model's performance was then evaluated by standard evaluation metrics, including receiver operating characteristic Area Under the Curve (AUC) and accuracy, on the training, validation, and testing datasets, where interpretability attention heatmaps were applied to gain insights into specific histological features and patterns involved in the model's decision-making process. The study is supported by the NIH-NCI-T32 for Next Generation Pathologists Program at our institution. Results: A total of 350 WSIs were included and labeled into two classes: post-therapeutic colon cancer recurrence and no recurrence. The tissue segmentation process involved converting RGB images to HSV color space, applying a median blur filter (kernel size: 7), and performing thresholding using Otsu's method. The extracted features were utilized to train and construct a Clustering-constrained Attention Multiple Instance Learning (CLAM) model. The model demonstrated consistent performance across the validation and testing datasets, achieving an AUC of 0. 85 with an accuracy of 0. 83 on the validation set, and an identical AUC of 0. 85 with an accuracy of 0. 83 on the testing set, indicating the model's robust ability to identify patients at risk of cancer recurrence. Conclusion: The study demonstrates the strong performance of our deep learning model in accurately identifying patients at risk for colon cancer recurrence based on H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 2584.