Dai H, Yan C, Huang W, Pan Y, Pan F, Liu Y, Wang S, Wang H, Ye R, Li Y. A Nomogram Based on MRI Visual Decision Tree to Evaluate Vascular Endothelial Growth Factor in Hepatocellular Carcinoma.
J Magn Reson Imaging 2025;
61:970-982. [PMID:
39777758 PMCID:
PMC11706310 DOI:
10.1002/jmri.29491]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 06/01/2024] [Accepted: 06/04/2024] [Indexed: 01/11/2025] Open
Abstract
BACKGROUNDS
Anti-vascular endothelial growth factor (VEGF) therapy has been developed and recognized as an effective treatment for hepatocellular carcinoma (HCC). However, there remains a lack of noninvasive methods in precisely evaluating VEGF expression in HCC.
PURPOSE
To establish a visual noninvasive model based on clinical indicators and MRI features to evaluate VEGF expression in HCC.
STUDY TYPE
Retrospective.
POPULATION
One hundred forty HCC patients were randomly divided into a training (N = 98) and a test cohort (N = 42).
FIELD STRENGTH/SEQUENCE
3.0 T, T2WI, T1WI including pre-contrast, dynamic, and hepatobiliary phases.
ASSESSMENT
The fusion model constructed by history of smoking, albumin-to-globulin ratio (AGR) and the Radio-Tree model was visualized by a nomogram.
STATISTICAL TESTS
Performances of models were assessed by receiver operating characteristic (ROC) curves. Student's t-test, Mann-Whitney U-test, chi-square test, Fisher's exact test, univariable and multivariable logistic regression analysis, DeLong's test, integrated discrimination improvement (IDI), Hosmer-Lemeshow test, and decision curve analysis were performed. P < 0.05 was considered statistically significant.
RESULTS
History of smoking and AGR ≤1.5 were clinical independent risk factors of the VEGF expression. In training cohorts, values of area under the curve (AUCs) of Radio-Tree model, Clinical-Radiological (C-R) model, fusion model which combined history of smoking and AGR with Radio-Tree model were 0.821, 0.748, and 0.871. In test cohort, the fusion model showed highest AUC (0.844) than Radio-Tree and C-R models (0.819, 0.616, respectively). DeLong's test indicated that the fusion model significantly differed in performance from the C-R model in training cohort (P = 0.015) and test cohort (P = 0.007).
DATA CONCLUSION
The fusion model combining history of smoking, AGR and Radio-Tree model established with ML algorithm showed the highest AUC value than others.
EVIDENCE LEVEL
4 TECHNICAL EFFICACY: Stage 2.
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