通过医学图像和记录的预测建模改进结直肠癌筛查与风险评估
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
AI总结：
针对当前结直肠癌（CRC）随访指南依赖组织病理学特征、病理学家息肉描述存在差异的问题，提出整合病理切片与医疗记录的预测模型，通过调整Transformer模型并结合多模态融合策略，提升了5年CRC风险预测准确性。
摘要：
结肠镜筛查能有效在息肉进展为结直肠癌（CRC）前识别并切除，但当前随访指南主要依赖组织病理学特征，忽略了其他重要CRC风险因素。病理学家之间息肉特征描述的差异也阻碍了一致的监测决策。数字病理学和深度学习的进展使整合病理切片与医疗记录以实现更准确的CRC风险预测成为可能。利用新罕布什尔州结肠镜检查登记处的纵向随访数据，我们调整了基于Transformer的组织病理学图像分析模型以预测5年CRC风险，并进一步探索了多模态融合策略，将临床记录与深度学习提取的图像特征相结合。训练模型预测中间临床变量后，5年CRC风险预测性能（AUC=0.630）优于直接预测（AUC=0.615，p=0.013）；整合影像与非影像数据且无需人工阅片时，性能（AUC=0.674）优于基于结肠镜和显微镜报告的传统特征（AUC=0.655，p=0.001）。这些结果凸显了通过计算方法整合多模态数据以增强CRC风险分层的价值。
英文摘要：
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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