Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.

胰腺癌是一种侵袭性疾病，通常出现较晚且预后不佳，这表明迫切需要早期检测。在这项研究中，我们将人工智能方法应用于丹麦 600 万患者（24,000 例胰腺癌病例）（丹麦国家患者登记处 (DNPR)）和美国 300 万名患者（3,900 例）（美国退伍军人事务部）的临床数据。 （美国-弗吉尼亚州））。我们根据临床病史中的疾病代码序列训练机器学习模型，并测试增量时间窗口内癌症发生的预测 (CancerRiskNet)。对于 36 个月内发生的癌症，最佳 DNPR 模型的表现是受试者工作特征曲线下面积 (AUROC) = 0.88，当癌症诊断前 3 个月内的疾病事件被排除在训练之外时，会降低到 AUROC (3m) = 0.83，对于 1,000 名 50 岁以上风险最高的患者，估计相对风险为 59。丹麦模型与 US-VA 数据的交叉应用性能较低（AUROC = 0.71），需要重新训练来提高性能（AUROC = 0.78，AUROC (3m) = 0.76）。这些结果提高了为高风险患者设计实际监测计划的能力，通过及早发现这种侵袭性癌症，可能有利于延长寿命和生活质量。