PLOS Digit Health. 2026 Oct 1;5(10):e0001763. doi: 10.1371/journal.pdig.0001763. eCollection 2026 Oct.
ABSTRACT
MRI-detected extramural vascular invasion (mrEMVI) is an important prognostic biomarker in rectal cancer, reflecting tumor invasiveness and metastatic potential. To address the subjectivity and inter-observer variability inherent in manual mrEMVI assessment, this study trained and validated an nnUNet-based segmentation model for automated voxel-level localization and visualization of mrEMVI. This multi-center retrospective study included a total of 2,501 rectal cancer patients, comprising 1,830 in the training cohort (with 5-fold cross-validation) and 671 in two independent external test cohorts. The Dice similarity coefficient was used to evaluate segmentation performance; the inter-reader agreement for mrEMVI identification was assessed using Cohen's kappa (κ). The prognostic value of mrEMVI status identified by the artificial intelligence (AI) model was evaluated using Kaplan-Meier survival analysis and multivariable Cox regression. In internal five-fold cross-validation, the model yielded Dice scores of 0.850, 0.442, and 0.335 for tumor, intravascular tumor signal, and dilated vessel segmentation, respectively. The model demonstrated strong classification performance, with accuracies of 81.5% (95% CI: 76.7%-85.8%) and 84.7% (95% CI: 80.7%-88.2%) in the two external test cohorts, and achieved substantial agreement with senior radiologists (κ = 0.713-0.736). Patients identified as AI-mrEMVI+ had significantly lower 3-year disease-free survival (DFS) and 5-year overall survival (OS) rates than AI-mrEMVI- patients (DFS: 62.3% vs. 84.9%, HR = 2.67, 95% CI: 1.95-3.66; OS: 68.7% vs. 87.1%, HR = 2.64, 95% CI: 1.75-3.97; both p < 0.001). This work establishes a scalable, objective framework for mrEMVI assessment based on voxel-level segmentation, inter-observer agreement analysis, and comprehensive prognostic validation, with direct implications for risk stratification and treatment individualization in rectal cancer.
PMID:42821531 | PMC:PMC13630187 | DOI:10.1371/journal.pdig.0001763