Pancreatic cancer is projected to be the second-deadliest cancer by 2030, making early detection critical. Intraductal papillary mucinous neoplasms (IPMNs), key cancer precursors, present a clinical dilemma, as current guidelines struggle to stratify malignancy risk, leading to unnecessary surgeries or missed diagnoses. Here, we developed Cyst-X, an AI framework for IPMN risk prediction trained on a unique, multi-center dataset of 1,461 MRI scans from 764 patients. Cyst-X achieves significantly higher accuracy (AUC = 0.82) than both the established Kyoto guidelines (AUC = 0.75) and expert radiologists, particularly in correct identification of high-risk lesions. Clinically, this translates to a 20% increase in cancer detection sensitivity (87.8% vs. 64.1%) for high-risk lesions. We demonstrate that this performance is maintained in a federated learning setting, allowing for collaborative model training without compromising patient privacy. To accelerate research in early pancreatic cancer detection, we publicly release the Cyst-X dataset and models, providing the first large-scale, multi-center MRI resource for pancreatic cyst analysis.

翻译：预计到2030年，胰腺癌将成为第二大致命癌症，因此早期检测至关重要。导管内乳头状黏液性肿瘤（IPMNs）作为关键的癌前病变，构成了临床困境，因为现有指南难以有效分层恶性风险，导致不必要的手术或漏诊。本研究开发了Cyst-X，一种基于独特多中心数据集训练的IPMN风险预测人工智能框架，该数据集包含764名患者的1,461次MRI扫描。Cyst-X实现了显著高于现有京都指南（AUC = 0.75）和放射科专家的准确率（AUC = 0.82），尤其在高风险病变的正确识别方面表现突出。临床意义上，这使高风险病变的癌症检测灵敏度提升了20%（87.8%对比64.1%）。我们证明该性能在联邦学习环境中得以保持，允许在不损害患者隐私的前提下进行协作模型训练。为加速早期胰腺癌检测研究，我们公开发布了Cyst-X数据集和模型，提供了首个用于胰腺囊肿分析的大规模多中心MRI资源。