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<p>Supplementary methods, figures and tables.</p> Haitao Huang (182705) Lili Feng (154280) Min-Er Zhong (6758177) Huifen Ye (11271543) Zhenhui Li (282794) Su Yao (3627038) Yunrui Ye (13909343) Yulin Liu (457083) Minning Zhao (13909337) Weixiong Xu (25157608) Lifen Yan (1404280) ChuanMiao Xie (25157611) Changhong Liang (215520) Zaiyi Liu (611024) Tong Tong (532210) Yanfen Cui (2146873) Xin-Juan Fan (329948) Ke Zhao (248186) Medicine Cell Biology Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Cancer sup >+</ sup observer variability inherent multivariable cox regression magnetic resonance imaging evaluated using kaplan dice similarity coefficient 9 %, hr evaluate segmentation performance dilated vessel segmentation observer agreement analysis meier survival analysis important prognostic biomarker achieved substantial agreement significantly lower 3 75 – 3 reflecting tumor invasiveness intravascular tumor signal year overall survival div >< p 7 %– 88 7 %– 85 mremvi assessment based manual mremvi assessment based segmentation model 1 %, hr mremvi status identified comprehensive prognostic validation 713 – 0 95 – 3 level segmentation reader agreement prognostic value free survival year disease work establishes vs .</ treatment individualization training cohort study trained senior radiologists risk stratification rectal cancer patients identified p </ objective framework metastatic potential level localization internal five fold cross <p>Text A–D, Fig A–O, Table A–F. Text A. Detailed information on the training and external test cohorts. Text B. Data standards. Text C. Detailed training procedure. Text D. Detailed inference procedure. Fig A. Patient enrollment flowchart for the training and external testing of the AI-mrEMVI model. Fig B. Flowchart of MRI marker evaluation. Fig C. Construction of the segmentation training dataset and five-fold cross-validation workflow for the AI-mrEMVI model. Fig D. The performance of the AI-mrEMVI model across different Dice scores. Fig E. Representative comparisons of AI-mrEMVI model predictions and radiologist annotations in typical, ambiguous, and misclassified cases. Fig F. Site-stratified confusion matrices and performance metrics of the AI-mrEMVI model in external test cohort 2. Fig G. The prognostic value of AI-mrEMVI status. Fig H. Bootstrap-derived distributions of C-index for DFS and OS across three models. Fig I. Kaplan-Meier survival curves for discordant cases between AI-mrEMVI and radiologist ground truth assessment. Fig J. The prognostic value of AI-mrEMVI lesion volume in external test cohort 2. Fig K. Correlation and agreement between AI-derived and manually annotated mrEMVI lesion volumes in true-positive cases. Fig L. Kaplan-Meier curves for DFS according to AI-mrEMVI status in different mrT and mrN staging subgroups. Fig M. Kaplan-Meier curves for DFS according to AI-mrEMVI status in different tumor location subgroups. Fig N. Kaplan-Meier curves for DFS according to AI-mrEMVI status in different subgroups of MR T2WI images with varying slice thicknesses and spacing between slices. Fig O. Kaplan-Meier curves for DFS according to AI-mrEMVI status in different subgroups of MR T2WI images with varying pixel spacing. Table A. MRI acquisition parameters across the participating centers. Table B. Segmentation performance comparison across deep learning architectures in internal five-fold cross-validation. Table C. Classification performance comparison across deep learning architectures on the combined external test cohorts. Table D. Delta C-index and 95% confidence intervals for model comparisons in disease-free survival (DFS) and overall survival (OS). Table E. Univariable and multivariable analyses of OS. Table F. Schoenfeld residual test results for proportional hazards assumption in multivariable Cox regression models of DFS and OS in two external test cohorts.</p> <p>(PDF)</p> 2026-10-01T17:40:25Z Text Journal contribution 10.1371/journal.pdig.0001763.s001 https://figshare.com/articles/journal_contribution/_p_Supplementary_methods_figures_and_tables_p_/34047490 CC BY 4.0