PO.BCS02.06 · 生物信息与计算
利用结肠镜检查前特征预测高风险结直肠息肉：机器学习模型的开发与验证
Predicting high-risk colorectal polyps using pre-colonoscopy features: Machine learning model development and validation
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：晚期结直肠息肉的风险分层通常依赖于结肠镜检查和/或病理结果，但人们关注在结肠镜检查前是否存在可见的非侵入性特征，能够识别哪些患者风险较高。此类工具有助于临床决策，使结肠镜监测能够留给最可能患有高风险息肉的患者，并避免对低风险患者进行不必要的操作。
方法：我们开发了机器学习模型，利用人口统计学、生活方式和合并症来预测高风险息肉。患有绒毛状/管状绒毛状腺瘤、高级别异型增生、尺寸≥10 mm，和/或每次操作≥3枚息肉的患者被视为高风险息肉（HRP），其余则被视为低风险息肉（LRP）。数据集包括2014-2022年的4,681例患者（内部验证；2,018例HRP，2,658例LRP）以及2023-2024年的1,562例患者（外部验证；769例HRP，793例LRP）。所使用的模型有神经网络、随机森林、SVM、朴素贝叶斯、逻辑回归、决策树、KNN和XGBoost。
结果：神经网络取得了最佳的内部性能（ROC-AUC 0.7764，PR-AUC 0.75，准确率0.72）。然而，外部队列的性能有所下降（ROC-AUC 0.67，准确率0.66），提示存在过拟合或特征漂移。较不复杂的模型如朴素贝叶斯、SVM和XGBoost，虽然内部性能较弱（ROC-AUC 0.54-0.59），但表现出更强的外部性能（ROC-AUC 0.52-0.63，准确率约0.53-0.60）。这表明结肠镜检查前特征中存在预测信号，但信号中等且对时间和队列变异非常敏感。使用SHAP值的模型可解释性分析显示，驱动预测的主要变量为年龄、吸烟状况、性别、职业、种族和结肠镜检查指征。其他贡献因素包括一级亲属结直肠癌家族史、BMI，以及若干临床/生活方式因素，如ASA使用、NSAID使用和饮酒。这些结果凸显出，尽管传统临床风险因素在预测中占主导地位，社会人口学变量也携带重要信号。
结论：基于非侵入性结肠镜检查前特征的HRP预测是可行的，但具有挑战性。外部验证时的性能下降凸显了现实世界泛化能力以及实践或人口统计学变化影响的重要性。这些发现既凸显了结肠镜检查前风险预测的临床应用潜力，也揭示了其局限性，并提示可能需要多模态数据来源（例如基因组学、微生物组学、影像学、社会决定因素）才能达到具有临床意义的性能。
查看英文原文 English abstract
Background: Advanced colorectal polyp risk stratification typically relies on colonoscopy and/or pathology findings, but there is interest in whether there are non-invasive features visible prior to colonoscopy that can identify which patients are at higher risk. Such a tool could help in clinical decision-making, enabling colonoscopy surveillance to be reserved for those most likely to have high-risk polyps and avoiding unnecessary procedures in those at lower risk.
Methods: We developed machine learning models to predict high-risk polyps using demographic, lifestyle, and comorbidities. Patients with villous/tubulovillous adenoma, high-grade dysplasia, ≥10 mm in size, and/or ≥3 polyps per procedure were considered as having High-risk polyps (HRP), while all others were considered to be Low-risk polyps (LRP). The data set consisted of 4,681 patients from 2014 - 2022 (internal validation; 2018 HRP, 2,658 LRP) and 1,562 patients from 2023-2024 (external validation; 769 HRP, 793 LRP). Models utilized were neural networks, random forest, SVM, Naive Bayes, logistic regression, decision trees, KNN, and XGBoost.
Results: The neural network achieved the best internal performance (ROC-AUC 0.7764, PR-AUC 0.75, accuracy 0.72). However, external cohort performance reduced (ROC-AUC 0.67, accuracy 0.66), suggesting overfitting or feature drift. Less complex models such as Naive Bayes, SVM, and XGBoost, while weaker internally (ROC-AUC 0.54-0.59), demonstrated stronger external performance (ROC-AUC 0.52-0.63, accuracy ~0.53-0.60). This suggests that predictive signal in pre-colonoscopy features exists but is moderate and very sensitive to temporal and cohort variation. Model interpretability analysis using SHAP values revealed that the main variables driving predictions were age, smoking status, sex, occupation, race, and indication for colonoscopy. Additional contributors included family history of colorectal cancer in first-degree relatives, BMI, and several clinical/lifestyle factors such as ASA use, NSAID use, and alcohol use. These results highlight that while traditional clinical risk factors dominate prediction, sociodemographic variables also carry important signal.
Conclusions: HRP prediction based on non-invasive pre-colonoscopy features is feasible but challenging. Performance degradation upon external validation highlights the importance of real-world generalizability and practice or demographic change effects. These findings highlight both clinical utility potential and limitations of pre-colonoscopy risk prediction, and suggest that multimodal data sources (e.g., genomics, microbiomics, imaging, social determinants) may be required to achieve clinically meaningful performance.
利益披露 Disclosure
B. Qolomany, None..
M. Deverapall, None..
A. O. Laiyemo, None..
Z. A. Sherif, None..
H. Brim, None..
H. Ashktorab, None.