Cyst-X: A Federated AI System Outperforms Clinical Guidelines to Detect Pancreatic Cancer Precursors and Reduce Unnecessary Surgery
Abstrak
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.
Penulis (31)
Hongyi Pan
Gorkem Durak
Elif Keles
Deniz Seyithanoglu
Zheyuan Zhang
Alpay Medetalibeyoglu
Halil Ertugrul Aktas
Andrea Mia Bejar
Ziliang Hong
Yavuz Taktak
Gulbiz Dagoglu Kartal
Mehmet Sukru Erturk
Timurhan Cebeci
Maria Jaramillo Gonzalez
Yury Velichko
Lili Zhao
Emil Agarunov
Federica Proietto Salanitri
Concetto Spampinato
Pallavi Tiwari
Ziyue Xu
Sachin Jambawalikar
Ivo G. Schoots
Marco J. Bruno
Chenchan Huang
Candice W. Bolan
Tamas Gonda
Frank H. Miller
Rajesh N. Keswani
Michael B. Wallace
Ulas Bagci
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓