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While radiomics and deep learning (DL) have been explored for pancreatic cancer, cyst-level malignancy risk stratification of IPMNs remains untapped.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our multi-institutional assessed the feasibility of AI for predicting IPMN dysplasia grade using cyst-level image features using 359 T2-weighted (T2W) MRI images from seven centers. We developed and compared 2D and 3D radiomics-only, DL-only, and radiomics-DL fusion models using expert radiologist scoring as a baseline reference. Model performance was evaluated using held-out test data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The radiomics-DL fusion model showed the highest discriminatory ability on the test set AUC of 69.2%, outperforming the radiomics-only model, AUC of 66.5%. Expert accuracy varied widely from 37.4% to 66.7%, and the inter-rater agreement varied as well with weighted Cohen\u2019s kappa coefficients of 0.33\u20130.67.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The fusion model, which combines DL with radiomics features from routine T2W MRI, shows promise for objective, cyst-level risk stratification of IPMNs in a multi-center cohort, outperforming radiomics-only models and nearly matching expert radiologists using only T2W and T1-weighted (T1W) sequences. While performance requires improvement for standalone clinical use, this approach offers a scalable, non-invasive method to potentially improve diagnostic accuracy and reduce unnecessary surgical interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00261-025-05371-3","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T06:27:18Z","timestamp":1768199238000},"page":"3805-3820","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs"],"prefix":"10.1007","volume":"51","author":[{"given":"Andrea M","family":"Bejar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Jaramillo 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Keswani acknowledges: Boston Scientific - consultant; Olympus - consultant; Medtronic - consultant and research support.Dr. Michael B. Wallace acknowledges: Boston Scientific, ClearNote Health, Cosmo Pharmaceuticals, Endostart, Endiatix, Fujifilm, Medtronic, Surgical Automations, Ohelio Ltd, Venn Bioscience, Virgo Inc., Surgical Automation, and Microtek.Dr. Marco J. Bruno acknowledges: Boston Scientific - consultant, support for industry and investigator-initiated studies; Cook Medical - consultant, support for industry and investigator-initiated studies; Pentax Medical - consultant, support for investigator-initiated studies; Mylan - support for investigator initiated studies; AMBU - consultant, support for investigator initiated studies; ChiRoStim - support for investigator-initiated studies.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}