用血液和病历数据预测胰腺癌，提前多年识别高危人群。
Digitally enriching a screening population for pancreatic cancer using routine blood-based measures and clinical histories
- 用Transformer模型分析长期病历与血检记录，预测胰腺癌风险。
- 提前1-3年预测准确率AUC达0.837至0.760，风险评估精准可靠。
- 适合临床筛查前的高危人群筛选，推动早期干预落地。
胰腺癌早期发现对实现可治愈治疗、降低死亡率至关重要，但当前筛查尚不可行。个体疾病史与血液检测轨迹中潜藏病理信号，可预测胰腺癌发生。研究利用患者临床交互中积累的编码诊断与血检值序列，训练基于多头注意力机制的定制Transformer神经网络，实现数年前瞻性的胰腺癌风险预测，并对人群进行分层筛选。研究队列包含6,017例胰腺癌患者与177,081例对照（平均年龄75岁，45%女性），平均有12年（四分位距6.9–16.2年）的病史数据。外部验证显示，提前1、2、3年预测的ROC曲线下面积分别为0.837（95%CI 0.827–0.848）、0.797（95%CI 0.782–0.813）和0.760（95%CI 0.745–0.776）。风险估计校准良好（校准斜率1.08，截距-0.077；Brier评分0.025），结合贝叶斯方法可实现跨场景风险迁移。在测试中，1年风险阈值>3.3%时，诊断优势比达18.2。本研究为首个面向人群的数字化高危人群增强工具，奠定了胰腺癌根治性管理普及的基础。
原文摘要 · Abstract (English)
Earlier detection of pancreatic cancer is key to enabling wider access to curative treatment and reducing cancer deaths; however, screening is presently not viable. Latent indicators of pathology are evident in an individual's disease and blood test trajectories and may predict the development of pancreatic cancer. Longitudinal sequences of coded diagnoses and blood test values accrued by patients throughout their clinical interactions were used to train a custom Transformer-based neural network with a multi-head attention mechanism to predict risk of pancreatic cancer with a multi-year lead time and risk-stratify populations for targeted screening. The cohort comprised 6,017 adults with pancreatic cancer and 177,081 controls (overall median age 75, 45% female) with median 12 years (interquartile range 6.9-16.2) of medical history prior to pancreatic cancer diagnosis. External validation via leave-one-site-out, out-of-sample testing predicting pancreatic cancer 1-, 2-, and 3-years prior to diagnosis demonstrated mean area under the receiver operating characteristic of 0.837 (95% confidence interval 0.827-0.848), 0.797 (95% confidence interval 0.782-0.813), and 0.760 (95% confidence interval 0.745-0.776), respectively. Estimated pancreatic cancer risks were well-calibrated (calibration plot slope 1.08, intercept of -0.077; Brier score 0.025), and a Bayesian population pancreatic cancer prevalence update allows estimated cancer risk outputs to be transportable across settings. At testing, a screening threshold of >3.3% risk of pancreatic cancer in 1-year offered a diagnostic odds ratio of 18.2. Our work therefore lays the foundation for a first population-level digital enrichment tool to widen access to curative-intent management of pancreatic cancer.
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