Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in
Leyao Wang1, Bing Feng1, Meng Liang1
1Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Abdominal Radiology (New York)
|April 20, 2024
Summary
A new prognostic model using MRI LI-RADS and clinical factors accurately predicts outcomes for alpha-fetoprotein-negative hepatocellular carcinoma patients, outperforming traditional systems.
Area of Science:
- Hepatocellular Carcinoma Research
- Medical Imaging Diagnostics
- Prognostic Modeling
Background:
- Alpha-fetoprotein (AFP) is a common biomarker for hepatocellular carcinoma (HCC), but a significant subset of patients present with normal AFP levels.
- Accurate prognosis prediction for AFP-negative HCC is crucial for effective treatment planning and patient management.
- Existing staging systems may not fully capture the prognostic nuances in AFP-negative HCC patients.
Purpose of the Study:
- To assess the predictive value of magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinicopathological factors for prognosis in AFP-negative HCC.
- To develop and validate a novel prognostic model for AFP-negative HCC.
- To compare the performance of the developed model against traditional staging systems.
Main Methods:
- Retrospective analysis of 169 patients with AFP-negative HCC who underwent preoperative MRI and hepatectomy.
- Development of a prognostic model using Cox regression analysis on identified risk factors.
- Validation of the model's predictive performance and discrimination capability against established staging systems.
Main Results:
- Six factors—LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade—significantly correlated with recurrence-free survival.
- The developed prognostic model demonstrated good predictive performance (C-index 0.705 in derivation, 0.674 in validation datasets).
- The model outperformed traditional staging systems and effectively stratified patients into high- and low-risk groups.
Conclusions:
- A prognostic model integrating LI-RADS, imaging features, and clinicopathological factors offers improved risk stratification for AFP-negative HCC.
- This model can serve as a valuable tool for refining treatment strategies and predicting outcomes in this specific patient cohort.
- Further research may explore the integration of more advanced imaging biomarkers for enhanced prognostic accuracy.
