Schuurmans Megan, Saha Anindo, Alves Natália, Vendittelli Pierpaolo, Yakar Derya, Sabroso-Lasa Sergio, Xue Nannan, Malats Núria, Huisman Henkjan, Hermans John, Litjens Geert
Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Medical Imaging, University Medical Center Groningen, Groningen, The Netherlands.
Eur Radiol. 2025 May 23. doi: 10.1007/s00330-025-11694-y.
Pancreatic cancer treatment plans involving surgery and/or chemotherapy are highly dependent on disease stage. However, current staging systems are ineffective and poorly correlated with survival outcomes. We investigate how artificial intelligence (AI) can enhance prognostic accuracy in pancreatic cancer by integrating multiple data sources.
Patients with histopathology and/or radiology/follow-up confirmed pancreatic ductal adenocarcinoma (PDAC) from a Dutch center (2004-2023) were included in the development cohort. Two additional PDAC cohorts from a Dutch and Spanish center were used for external validation. Prognostic models including clinical variables, contrast-enhanced CT images, and a combination of both were developed to predict high-risk short-term survival. All models were trained using five-fold cross-validation and assessed by the area under the time-dependent receiver operating characteristic curve (AUC).
The models were developed on 401 patients (203 females, 198 males, median survival (OS) = 347 days, IQR: 171-585), with 98 (24.4%) short-term survivors (OS < 230 days) and 303 (75.6%) long-term survivors. The external validation cohorts included 361 patients (165 females, 138 males, median OS = 404 days, IQR: 173-736), with 110 (30.5%) short-term survivors and 251 (69.5%) longer survivors. The best AUC for predicting short vs. long-term survival was achieved with the multi-modal model (AUC = 0.637 (95% CI: 0.500-0.774)) in the internal validation set. External validation showed AUCs of 0.571 (95% CI: 0.453-0.689) and 0.675 (95% CI: 0.593-0.757).
Multimodal AI can predict long vs. short-term survival in PDAC patients, showing potential as a prognostic tool in clinical decision-making.
Question Prognostic tools for pancreatic ductal adenocarcinoma (PDAC) remain limited, with TNM staging offering suboptimal accuracy in predicting patient survival outcomes. Findings The multimodal AI model demonstrated improved prognostic performance over TNM and unimodal models for predicting short- and long-term survival in PDAC patients. Clinical relevance Multimodal AI provides enhanced prognostic accuracy compared to current staging systems, potentially improving clinical decision-making and personalized management strategies for PDAC patients.
涉及手术和/或化疗的胰腺癌治疗方案高度依赖于疾病分期。然而，目前的分期系统效果不佳，与生存结果的相关性较差。我们研究人工智能（AI）如何通过整合多个数据源来提高胰腺癌的预后准确性。
来自荷兰一个中心（2004 - 2023年）的组织病理学和/或放射学/随访确诊为胰腺导管腺癌（PDAC）的患者被纳入开发队列。另外两个来自荷兰和西班牙中心的PDAC队列用于外部验证。开发了包括临床变量、增强CT图像以及两者结合的预后模型，以预测高风险短期生存。所有模型均使用五折交叉验证进行训练，并通过时间依赖性受试者操作特征曲线（AUC）下的面积进行评估。
模型基于401例患者开发（203例女性，198例男性，中位生存期（OS）= 347天，IQR：171 - 585），其中98例（24.4%）为短期生存者（OS < 230天），303例（75.6%）为长期生存者。外部验证队列包括361例患者（165例女性，138例男性，中位OS = 404天，IQR：173 - 736），其中110例（30.5%）为短期生存者，251例（69.5%）为长期生存者。在内部验证集中，多模态模型预测短期与长期生存的最佳AUC为0.637（95% CI：0.500 - 0.774）。外部验证显示AUC分别为0.571（95% CI：0.453 - 0.689）和0.675（95% CI：0.593 - 0.757）。
多模态AI可以预测PDAC患者的长期与短期生存，显示出作为临床决策中预后工具的潜力。
问题 胰腺导管腺癌（PDAC）的预后工具仍然有限，TNM分期在预测患者生存结果方面准确性欠佳。发现 多模态AI模型在预测PDAC患者短期和长期生存方面比TNM和单模态模型具有更好的预后性能。临床意义 与当前分期系统相比，多模态AI提供了更高的预后准确性，可能改善PDAC患者的临床决策和个性化管理策略。
