Development of an artificial intelligence-based automatic MRI scoring model for extramural vascular invasion in rectal cancer and its prognostic value
10.3760/cma.j.cn112149-20241125-00700
- VernacularTitle:基于人工智能的直肠癌MRI壁外血管侵犯自动评分模型的建立及其预测预后的价值
- Author:
Haitao HUANG
1
;
Yunrui YE
;
Lifen YAN
;
Yanfen CUI
;
Lili FENG
;
Huifen YE
;
Yulin LIU
;
Ying ZHU
;
Zhongwei CHEN
;
Zhenhui LI
;
Ke ZHAO
;
Zaiyi LIU
;
Changhong LIANG
Author Information
1. 南方医科大学附属广东省人民医院（广东省医学科学院）放射科 广东省医学影像智能分析与应用重点实验室，广州 510080
- Publication Type:Journal Article
- Keywords:
Rectal neoplasms;
Magnetic resonance imaging;
Extramural vascular invasion;
Artificial intelligence;
Prognostic analysis
- From:
Chinese Journal of Radiology
2025;59(11):1267-1274
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an artificial intelligence (AI)-based automatic scoring model for magnetic resonance imaging-detected extramural vascular invasion (AI-mrEMVI) and evaluate its performance and prognostic value in patients with rectal cancer.Methods:In this multicenter retrospective cohort study, a total of 2 501 rectal cancer patients from seven centers between November 2012 and December 2020 were included and divided into completely independent training ( n=1 830) and validation ( n=671) cohorts. A nnUNet-based AI-mrEMVI scoring model was constructed. Manual mrEMVI scores assigned by two radiologists served as the reference standard for accessing the accuracy of the AI-mrEMVI scoring. Kaplan-Meier survival analysis and Cox regression were used to evaluate the prognostic stratification ability of the AI-mrEMVI scores. The concordance index (C-index) was calculated to evaluate prognostic performance. Results:In the validation cohort, the manual mrEMVI scores were 0-2 in 425 patients (63.3%), 3 in 89 (13.4%), and 4 in 157 (23.4%). The AI-mrEMVI model identified 0-2 in 375 patients (55.9%), 3 in 95 (14.2%), and 4 in 201 (30.0%), with an overall accuracy of 81.1% (544/671, 95% CI 77.9%-84.0%). The 3-year disease-free survival (DFS) rates for patients with AI-mrEMVI scores of 0-2, 3, and 4 were 85.2%, 70.0%, and 58.2%, respectively, and the 5-year overall survival (OS) rates were 87.2%, 81.6%, and 62.6%, respectively (DFS: χ2=48.74, P<0.001; OS: χ2=30.04, P<0.001). Multivariable Cox regression showed that for DFS, AI-mrEMVI scores of 3 and 4 were associated with hazard ratios ( HR) of 1.75 (95% CI 1.11-2.77, P=0.016) and 2.65 (95% CI 1.86-3.78, P<0.001), respectively. For OS, an AI-mrEMVI score of 4 was associated with an HR of 2.56 (95% CI 1.62-4.03, P<0.001). The C-index values of the AI-mrEMVI scoring model for predicting DFS and OS were 0.647 (95% CI 0.608-0.686) and 0.650 (95% CI 0.598-0.702), respectively. Conclusion:The proposed AI-mrEMVI automatic scoring model demonstrated high diagnostic accuracy and performed favorably in predicting DFS and OS prognostic risk in patients with rectal cancer.
