2026-10-09T19:53:40Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34048961 2026-10-01T17:40:37Z category_7 category_12 category_21 category_734 category_811 category_64 portal_5 item_type_3 month_year_10_2026

Univariable and multivariable analyses of DFS. 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 <div>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.</div> 2026-10-01T17:40:25Z Dataset Dataset 10.1371/journal.pdig.0001763.t002 https://figshare.com/articles/dataset/_p_Univariable_and_multivariable_analyses_of_DFS_p_/34048961 CC BY 4.0