Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records

• URL: http://arxiv.org/abs/2410.09880v2
• Date: Sun, 13 Apr 2025 19:21:04 GMT
• Title: Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
• Authors: Shuai Jiang, Christina Robinson, Joseph Anderson, William Hisey, Lynn Butterly, Arief Suriawinata, Saeed Hassanpour,
• Abstract summary: Colonoscopy screening effectively identifies polyps before they progress to colorectal cancer (CRC)<n>Current follow-up guidelines rely primarily on features overlooking other important risk factors.<n>We adapted a transformer-based model for histopathology image analysis to predict 5-year CRC risk.
• Score: 2.675031613330622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Colonoscopy screening effectively identifies and removes polyps before they progress to colorectal cancer (CRC), but current follow-up guidelines rely primarily on histopathological features, overlooking other important CRC risk factors. Variability in polyp characterization among pathologists also hinders consistent surveillance decisions. Advances in digital pathology and deep learning enable the integration of pathology slides and medical records for more accurate CRC risk prediction. Using data from the New Hampshire Colonoscopy Registry, including longitudinal follow-up, we adapted a transformer-based model for histopathology image analysis to predict 5-year CRC risk. We further explored multi-modal fusion strategies to combine clinical records with deep learning-derived image features. Training the model to predict intermediate clinical variables improved 5-year CRC risk prediction (AUC = 0.630) compared to direct prediction (AUC = 0.615, p = 0.013). Incorporating both imaging and non-imaging data, without requiring manual slide review, further improved performance (AUC = 0.674) compared to traditional features from colonoscopy and microscopy reports (AUC = 0.655, p = 0.001). These results highlight the value of integrating diverse data modalities with computational methods to enhance CRC risk stratification.

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