用常规血检数据，机器学习预测三种癌症高风险人群。
Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers
- 结合血常规和代谢面板，用深度学习识别癌症风险。
- 对结直肠、肝、肺癌的预测AUC分别达0.76、0.85、0.78。
- 适合用于健康人群初筛和特定群体癌症风险评估。
早期癌症筛查能显著提升生存率，并避免因晚诊带来的高强度、高成本治疗。健康人群的癌症筛查通常先进行风险分层，以确定筛查方式与频率，主要目的是通过将资源聚焦于最可能获益者来优化配置。目前多数筛查程序基于年龄和临床风险因素（如家族史）进行分层。本文提出一种基于血液标志物的风险分层方法，可识别出癌症高风险个体，推动其接受诊断检测或参与筛查。我们证明，结合常规且广泛可用的血液检查（如全血细胞计数和全面代谢面板），可用于识别结直肠癌、肝癌和肺癌高风险者，其受试者工作特征曲线下面积（ROC AUC）分别为0.76、0.85和0.78。此外，我们假设该方法不仅可用于个体层面的筛查前风险评估，还可作为人群健康管理工具，例如更精准地分析特定亚群的癌症风险。
原文摘要 · Abstract (English)
Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations.
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