{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T11:53:04Z","timestamp":1782906784465,"version":"3.54.5"},"reference-count":16,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Purpose: To develop and technically validate a reproducible multicentre MRI radiomics workflow for pancreatic cyst risk stratification using paired T1- and T2-weighted imaging from public datasets. Methods: Public datasets were screened and Cyst-X was selected as the primary cohort because it contained pancreatic MRI, risk labels, masks and metadata. A linked Cyst-X subset was enriched with metadata, filtered to an exact paired T1\/T2 cohort, and processed through image\u2013mask quality control, 1.0 mm isotropic resampling, intensity normalisation, PyRadiomics feature extraction, feature reduction and patient-level centre-held-out validation. The revised modelling strategy used a T2 + clinical all-patient primary analysis (n = 409) and a complete-case paired T1\/T2 sensitivity analysis (n = 299). Results: The final cohort comprised 409 patients and 818 image-level rows across EMC, IU, MCF and NYU. All 818 image\u2013mask pairs passed post-preprocessing QC. T2 radiomics were complete for all 409 patients; however, 110 T1 feature sets were missing, all from MCF. In the all-patient T2 + clinical model comparison, logistic regression achieved the highest macro-AUC (0.737). The T2 + clinical random forest comparator achieved macro-AUC 0.716 (95% CI 0.678\u20130.755), accuracy 0.545 (95% CI 0.496\u20130.592) and macro-F1 0.530 (95% CI 0.481\u20130.577). The paired T1\/T2 complete-case random forest sensitivity model achieved macro-AUC 0.735 (95% CI 0.691\u20130.777), accuracy 0.575 (95% CI 0.520\u20130.632) and macro-F1 0.554 (95% CI 0.494\u20130.605). Conclusion: This study demonstrates the feasibility of constructing a reproducible public data MRI radiomics workflow for pancreatic cyst risk stratification. Model performance was modest, and independent external validation is required before clinical application.<\/jats:p>","DOI":"10.3390\/tomography12070100","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T08:19:02Z","timestamp":1782893942000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Reproducible Multicentre MRI Radiomics Workflow for Pancreatic Cyst Risk Stratification Using Paired T1- and T2-Weighted Imaging"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7900-2003","authenticated-orcid":false,"given":"George","family":"Sgourakis","sequence":"first","affiliation":[{"name":"HepatoPancreaticBiliary Unit, East Lancashire Hospitals NHS Trust, Blackburn BB2 3HH, UK"},{"name":"School of Medicine, University of Lancashire, Preston PR1 2HE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.pan.2017.07.007","article-title":"Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas","volume":"17","author":"Tanaka","year":"2017","journal-title":"Pancreatology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1136\/gutjnl-2018-316027","article-title":"European evidence-based guidelines on pancreatic cystic neoplasms","volume":"67","author":"Besselink","year":"2018","journal-title":"Gut"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. 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