Integrating lesser omentum adipose CT in dual-phase tumor imaging: A multi-label deep learning framework for preoperative microvascular invasion prediction and survival analysis in hepatocellular carcinoma.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
환자: MVI, surgical resection of satellite nodules may improve OS
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
For patients with MVI, surgical resection of satellite nodules may improve OS. [CONCLUSIONS] Our multi-label deep learning framework accurately predicts MVI in HCC patients and enables stratified analysis of OS, which could guide personalized treatment and improve outcomes through timely intervention.
[BACKGROUND AND OBJECTIVE] Accurate preoperative prediction of microvascular invasion (MVI) and survival risk is essential for personalized treatment in hepatocellular carcinoma (HCC).
- p-value p < 0.05
- 95% CI 0.815-0.961
APA
Miao S, Jiang Y, et al. (2026). Integrating lesser omentum adipose CT in dual-phase tumor imaging: A multi-label deep learning framework for preoperative microvascular invasion prediction and survival analysis in hepatocellular carcinoma.. Computer methods and programs in biomedicine, 275, 109197. https://doi.org/10.1016/j.cmpb.2025.109197
MLA
Miao S, et al.. "Integrating lesser omentum adipose CT in dual-phase tumor imaging: A multi-label deep learning framework for preoperative microvascular invasion prediction and survival analysis in hepatocellular carcinoma.." Computer methods and programs in biomedicine, vol. 275, 2026, pp. 109197.
PMID
41349434
Abstract
[BACKGROUND AND OBJECTIVE] Accurate preoperative prediction of microvascular invasion (MVI) and survival risk is essential for personalized treatment in hepatocellular carcinoma (HCC). This study aimed to develop a multi-label deep learning framework to enhance prediction performance.
[METHODS] We developed CGAResNet18, an end-to-end dual-branch model, using retrospective data from two centers. Computed tomography (CT) of lesser omentum adipose (LOA) was channel-wise concatenated with arterial-phase tumor CT and, separately, with venous-phase tumor CT, resulting in two fused inputs that were fed into the model. Clinical data were analyzed via univariate and multivariate logistic regression to identify three independent MVI risk factors-gender, satellite nodules, and tumor size-which were used as auxiliary labels to guide training. Patients were stratified into high-risk and low-risk groups based on the model's predictions, and overall survival (OS) analysis was conducted.
[RESULTS] The model incorporating LOA features and trained with multi-label clinical data demonstrated superior performance in MVI prediction. In the internal and external test cohort, AUCs were 0.895 (95 % CI: 0.815-0.961) and 0.842 (95 % CI: 0.747-0.930). Compared with radiologists, our model significantly reduced both false-positive and false-negative rates. The Kaplan-Meier survival analysis demonstrated that patients predicted to have MVI exhibited significantly shorter OS compared to those predicted to be MVI-absent (log-rank test, p < 0.05). For patients with MVI, surgical resection of satellite nodules may improve OS.
[CONCLUSIONS] Our multi-label deep learning framework accurately predicts MVI in HCC patients and enables stratified analysis of OS, which could guide personalized treatment and improve outcomes through timely intervention.
[METHODS] We developed CGAResNet18, an end-to-end dual-branch model, using retrospective data from two centers. Computed tomography (CT) of lesser omentum adipose (LOA) was channel-wise concatenated with arterial-phase tumor CT and, separately, with venous-phase tumor CT, resulting in two fused inputs that were fed into the model. Clinical data were analyzed via univariate and multivariate logistic regression to identify three independent MVI risk factors-gender, satellite nodules, and tumor size-which were used as auxiliary labels to guide training. Patients were stratified into high-risk and low-risk groups based on the model's predictions, and overall survival (OS) analysis was conducted.
[RESULTS] The model incorporating LOA features and trained with multi-label clinical data demonstrated superior performance in MVI prediction. In the internal and external test cohort, AUCs were 0.895 (95 % CI: 0.815-0.961) and 0.842 (95 % CI: 0.747-0.930). Compared with radiologists, our model significantly reduced both false-positive and false-negative rates. The Kaplan-Meier survival analysis demonstrated that patients predicted to have MVI exhibited significantly shorter OS compared to those predicted to be MVI-absent (log-rank test, p < 0.05). For patients with MVI, surgical resection of satellite nodules may improve OS.
[CONCLUSIONS] Our multi-label deep learning framework accurately predicts MVI in HCC patients and enables stratified analysis of OS, which could guide personalized treatment and improve outcomes through timely intervention.
🏷️ 키워드 / MeSH
- Humans
- Carcinoma
- Hepatocellular
- Liver Neoplasms
- Male
- Female
- Deep Learning
- Tomography
- X-Ray Computed
- Omentum
- Middle Aged
- Retrospective Studies
- Survival Analysis
- Neoplasm Invasiveness
- Aged
- Adipose Tissue
- Microvessels
- Dual-phase hepatic tumor CT
- Lesser omentum adipose CT
- Microvascular invasion
- Multi-label deep learning
- Survival analysis
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