用图像和病历数据融合预测结直肠癌风险，提升筛查精准度。
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
- 用Transformer模型分析病理图像，结合临床记录做多模态融合。
- 融合影像与非影像数据后5年风险预测AUC达0.674，优于传统方法。
- 无需人工阅片，适合临床风险评估系统快速部署。
结肠镜筛查能有效发现并切除息肉以预防结直肠癌（CRC），但当前随访指南主要依赖组织病理特征，忽略了其他重要风险因素。不同病理科医生对息肉的判断差异也影响了随访决策的一致性。数字病理学与深度学习的发展使得整合病理切片与医疗记录成为可能。本研究基于新罕布什尔结肠镜注册数据库，采用基于Transformer的模型分析组织病理图像，预测5年CRC风险。通过探索多模态融合策略，将临床记录与深度学习提取的图像特征结合。训练模型预测中间临床变量后，5年风险预测性能提升（AUC = 0.630），优于直接预测（AUC = 0.615，p = 0.013）。进一步融合影像与非影像数据后，性能达到AUC = 0.674，显著优于传统特征（如结肠镜与显微镜报告特征，AUC = 0.655，p = 0.001）。结果表明，结合多源数据与计算方法可显著提升CRC风险分层能力。
原文摘要 · Abstract (English)
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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