Tomography · Published 2026-07-01 · DOI 10.3390/tomography12070100
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–mask 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–mask 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 0.678–0.755), accuracy 0.545 (95% CI 0.496–0.592) and macro-F1 0.530 (95% CI 0.481–0.577). The paired T1/T2 complete-case random forest sensitivity model achieved macro-AUC 0.735 (95% CI 0.691–0.777), accuracy 0.575 (95% CI 0.520–0.632) and macro-F1 0.554 (95% CI 0.494–0.605). 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.
Abstract from DOAJ. Public domain (CC0 1.0).
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Sgourakis, G. (2026). A Reproducible Multicentre MRI Radiomics Workflow for Pancreatic Cyst Risk Stratification Using Paired T1- and T2-Weighted Imaging. Tomography. https://doi.org/10.3390/tomography12070100