Artificial intelligence for evaluation of magnetic resonance imaging-detected extramural vascular invasion in rectal cancer.
Authors
Affiliations (20)
Affiliations (20)
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- School of Medicine, South China University of Technology, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China.
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Department of Pathology, Heyuan People's Hospital, Heyuan, China.
- Heyuan Key Laboratory of Molecular Diagnosis and Disease Prevention and Treatment, Heyuan People's Hospital, Heyuan, China.
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, China.
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Henan Provincial Key Laboratory of Radiation Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou University, Zhengzhou, Henan, China.
- Chinese Medicine Guangdong Laboratory, Hengqin, China.
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.