Deep Learning-Based Prediction of Microvascular Invasion and Survival Outcomes in Hepatocellular Carcinoma Using Dual-phase CT Imaging of Tumors and Lesser Omental Adipose: A Multicenter Study

Shidi Miao, Mengzhuo Sun, Xuemeng Li, Mingxuan Wang, Yuyang Jiang, Zengyao Liu, Qiujun Wang, Xuemei Ding, Ruitao Wang,
Deep Learning-Based Prediction of Microvascular Invasion and Survival Outcomes in Hepatocellular Carcinoma Using Dual-phase CT Imaging of Tumors and Lesser Omental Adipose: A Multicenter Study,
Academic Radiology,
Volume 32, Issue 10,
2025,
Pages 5789-5801,
ISSN 1076-6332,
https://doi.org/10.1016/j.acra.2025.07.015.
(https://www.sciencedirect.com/science/article/pii/S1076633225006555)
Abstract: Background
Accurate preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) remains challenging. Current imaging biomarkers show limited predictive performance.
Purpose
To develop a deep learning model based on preoperative multiphase CT images of tumors and lesser omental adipose tissue (LOAT) for predicting MVI status and to analyze associated survival outcomes.
Materials and Methods
This retrospective study included pathologically confirmed HCC patients from two medical centers between 2016 and 2023. A dual-branch feature fusion model based on ResNet18 was constructed, which extracted fused features from dual-phase CT images of both tumors and LOAT. The model's performance was evaluated on both internal and external test sets. Logistic regression was used to identify independent predictors of MVI. Based on MVI status, patients in the training, internal test, and external test cohorts were stratified into high- and low-risk groups, and overall survival differences were analyzed.
Results
The model incorporating LOAT features outperformed the tumor-only modality, achieving an AUC of 0.889 (95% CI: [0.882, 0.962], P=0.004) in the internal test set and 0.826 (95% CI: [0.793, 0.872], P=0.006) in the external test set. Both results surpassed the independent diagnoses of three radiologists (average AUC=0.772). Multivariate logistic regression confirmed that maximum tumor diameter and LOAT area were independent predictors of MVI. Further Cox regression analysis showed that MVI-positive patients had significantly increased mortality risks in both the internal test set (Hazard Ratio [HR]=2.246, 95% CI: [1.088, 4.637], P=0.029) and external test set (HR=3.797, 95% CI: [1.262, 11.422], P=0.018).
Conclusion
This study is the first to use a deep learning framework integrating LOAT and tumor imaging features, improving preoperative MVI risk stratification accuracy. Independent prognostic value of LOAT has been validated in multicenter cohorts, highlighting its potential to guide personalized surgical planning.
Keywords: Hepatocellular carcinoma; Microvascular invasion; Deep learning; Omental fat; Multimodal