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Insights Imaging2026;17(1)May 22, 2026Journal Article

A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.

Yumeng Zhang1, Shruti Atul Mali1, Danial Khan1, Sina Amirrajab1, Eduardo Ibor-Crespo2, Ana Jimenez-Pastor2, Gloria Ribas3, Silvia Flor-Arnal3, Marta Zerunian4, Christophe Aubé5,6, Luis Martí-Bonmatí3,7, Zohaib Salahuddin1, Philippe Lambin8,9
1The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
2Research & Frontiers in AI Department, Quantitative Imaging Biomarkers in Medicine, Quibim SL, Valencia, Spain.
3Biomedical Imaging Research Group, La Fe Health Research Institute, Valencia, Spain.
4Radiology Unit, Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome, Sant'Andrea Hospital, Rome, Italy.
5Laboratoire HIFIH, Université d'Angers, SFR ICAT 4208, Angers, France.
6Department of Radiology, CHU Angers, Angers, France.
7Medical Imaging Department, La Fe University and Polytechnic Hospital, Valencia, Spain.
8The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands. philippe.lambin@maastrichtuniversity.nl.
9Department of Radiology and Nuclear Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands. philippe.lambin@maastrichtuniversity.nl.
PubMed PMC DOI PDF

Abstract

OBJECTIVES: Accurate MRI-based identification of extramural vascular invasion (EVI) and mesorectal fascia invasion (MFI) is crucial for risk-stratified rectal cancer treatment. However, subjective visual assessment and inter-institutional variability limit diagnostic consistency. This study developed and evaluated a multi-center, foundation model-driven framework that automatically classifies EVI…

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