Cyst-X: A Federated AI System Outperforms Clinical Guidelines to Detect Pancreatic Cancer Precursors and Reduce Unnecessary Surgery
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915603503120384 |
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| author | Pan, Hongyi Durak, Gorkem Keles, Elif Seyithanoglu, Deniz Zhang, Zheyuan Medetalibeyoglu, Alpay Aktas, Halil Ertugrul Bejar, Andrea Mia Hong, Ziliang Taktak, Yavuz Kartal, Gulbiz Dagoglu Erturk, Mehmet Sukru Cebeci, Timurhan Gonzalez, Maria Jaramillo Velichko, Yury Zhao, Lili Agarunov, Emil Salanitri, Federica Proietto Spampinato, Concetto Tiwari, Pallavi Xu, Ziyue Jambawalikar, Sachin Schoots, Ivo G. Bruno, Marco J. Huang, Chenchan Bolan, Candice W. Gonda, Tamas Miller, Frank H. Keswani, Rajesh N. Wallace, Michael B. Bagci, Ulas |
| author_facet | Pan, Hongyi Durak, Gorkem Keles, Elif Seyithanoglu, Deniz Zhang, Zheyuan Medetalibeyoglu, Alpay Aktas, Halil Ertugrul Bejar, Andrea Mia Hong, Ziliang Taktak, Yavuz Kartal, Gulbiz Dagoglu Erturk, Mehmet Sukru Cebeci, Timurhan Gonzalez, Maria Jaramillo Velichko, Yury Zhao, Lili Agarunov, Emil Salanitri, Federica Proietto Spampinato, Concetto Tiwari, Pallavi Xu, Ziyue Jambawalikar, Sachin Schoots, Ivo G. Bruno, Marco J. Huang, Chenchan Bolan, Candice W. Gonda, Tamas Miller, Frank H. Keswani, Rajesh N. Wallace, Michael B. Bagci, Ulas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cyst-X: A Federated AI System Outperforms Clinical Guidelines to Detect Pancreatic Cancer Precursors and Reduce Unnecessary Surgery Pan, Hongyi Durak, Gorkem Keles, Elif Seyithanoglu, Deniz Zhang, Zheyuan Medetalibeyoglu, Alpay Aktas, Halil Ertugrul Bejar, Andrea Mia Hong, Ziliang Taktak, Yavuz Kartal, Gulbiz Dagoglu Erturk, Mehmet Sukru Cebeci, Timurhan Gonzalez, Maria Jaramillo Velichko, Yury Zhao, Lili Agarunov, Emil Salanitri, Federica Proietto Spampinato, Concetto Tiwari, Pallavi Xu, Ziyue Jambawalikar, Sachin Schoots, Ivo G. Bruno, Marco J. Huang, Chenchan Bolan, Candice W. Gonda, Tamas Miller, Frank H. Keswani, Rajesh N. Wallace, Michael B. Bagci, Ulas Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Cyst-X: A Federated AI System Outperforms Clinical Guidelines to Detect Pancreatic Cancer Precursors and Reduce Unnecessary Surgery |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.22017 |