The construction and risk stratification study of a hepatocellular carcinoma prognosis model based on automatic segmentation and radiomics of gadoxetate disodium-enhanced MRI
10.3760/cma.j.cn112149-20241218-00742
- VernacularTitle:基于钆塞酸二钠增强MRI自动分割与影像组学的肝癌预后模型构建及风险分层研究
- Author:
Can YU
1
;
Qi ZHANG
;
Yueqi WANG
;
Tiantian FAN
;
Huiying LI
;
Shan CONG
;
Yang ZHOU
Author Information
1. 哈尔滨医科大学附属肿瘤医院影像中心，哈尔滨 150001
- Publication Type:Journal Article
- Keywords:
Carcinoma, hepatocellular;
Magnetic Resonance Imaging;
Gadoxetic acid disodium;
Artificial intelligence;
Deep learning;
Radiomics;
Prognostic model
- From:
Chinese Journal of Radiology
2025;59(6):681-687
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.
