Multi-cancer early detection (MCED) tests offer to screen for multiple types of cancer with a single blood sample. Despite their promising diagnostic performance, evidence regarding their population benefit is not yet available. Expecting that benefit will derive from detecting cancer before it progresses to an advanced stage, we develop a general two-stage model to project the reduction in advanced-stage diagnoses given stage-specific test sensitivities and testing ages. The model can be estimated using cancer registry data and values for the mean overall and advanced-stage preclinical sojourn times. We first estimate the model for lung cancer and validate it against the stage shift observed in the National Lung Screening Trial. We then estimate the model for liver, pancreas, and bladder cancer, which have no recommended screening tests, and we project stage shifts under a shared MCED testing protocol. Our framework transparently integrates available data to project reductions in advanced-stage diagnoses due to MCED testing.

翻译：多癌早期检测（MCED）测试可通过单次血液样本筛查多种癌症类型。尽管其诊断性能令人期待，但关于其人群获益的证据尚未获得。鉴于预期效益将源于在癌症进展至晚期前检出，我们开发了一个通用两阶段模型，用于根据分期特异性检测灵敏度和检测年龄预测晚期诊断的减少。该模型可利用癌症登记数据及总平均和晚期临床前期停留时间值进行估计。我们首先对肺癌进行模型估计，并依据国家肺部筛查试验中观察到的分期迁移进行验证。随后针对尚无推荐筛查手段的肝癌、胰腺癌和膀胱癌进行模型估计，并基于共享MCED检测方案预测分期迁移。该框架可透明整合现有数据，以预测MCED检测带来的晚期诊断人数下降。