用体检前数据预测非裔患者高危肠息肉，提升筛查效率与公平性。
Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation
- 基于年龄、病史等非侵入性特征训练机器学习模型。
- 外部验证准确率达78.6%，可识别70%以上高危患者。
- 适合资源有限地区用于优先筛查，促进医疗公平。
针对高级别结直肠息肉的风险分层通常依赖结肠镜检查和病理结果。然而，越来越多研究关注是否可在结肠镜前利用非侵入性临床特征识别高风险患者。此类方法有助于在资源有限时优化筛查优先级，减少低风险人群的不必要的检查。尤其对结肠镜资源匮乏或分布不均的群体，利用术前信息可推动更公平的风险评估。本研究在以非裔为主的都市人群队列中，基于霍华德大学医院2015-2022年4,681名患者的匿名、生活方式及共病数据，开发并外部验证多个机器学习模型（包括神经网络、随机森林、支持向量机、朴素贝叶斯、逻辑回归、决策树、K近邻和XGBoost），预测高危结直肠息肉（HRP）。HRP定义为绒毛状或管状绒毛状腺瘤、高级别异型增生、直径≥10 mm的息肉，或单次检查发现≥3个息肉；其余归为低风险息肉（LRP）。内部验证使用2015-2022年数据，外部验证采用2023-2024年1,562名患者数据。
原文摘要 · Abstract (English)
Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whether non-invasive features available prior to colonoscopy can help identify patients at higher risk. Such approaches may enhance clinical decision-making by prioritizing surveillance for individuals most likely to harbor high-risk polyps, when colonoscopy resources are limited while potentially reducing unnecessary procedures in lower-risk patients. Importantly, the use of non-invasive, pre-procedural information may also help promote more equitable access to risk stratification, particularly in settings where colonoscopy resources are limited or unevenly distributed. We aimed to develop and externally validate machine learning models to predict high-risk colorectal polyps using only non-invasive, pre-colonoscopy demographic, clinical, and behavioral features in a diverse, predominantly African American, urban cohort. We conducted a retrospective cohort study using demographic, lifestyle, and comorbidity data from patients who underwent colonoscopy at Howard University Hospital to develop and validate several machine learning models, including neural networks, random forest, support vector machines (SVM), Naive Bayes, logistic regression, decision trees, k-nearest neighbors (KNN), and XGBoost, for predicting high-risk colorectal polyps. High-risk polyps (HRP) were defined as villous or tubullovillous adenomas, high-grade dysplasia, polyps >= 10 mm in size, and/or the presence of >= 3 polyps per procedure; all other cases were classified as low-risk polyps (LRP). The dataset included 4,681 patients from 2015-2022 used for internal validation and 1,562 patients from 2023-2024 used for external validation.
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