A Nomogram Based on MRI Visual Decision Tree to Evaluate Vascular Endothelial Growth Factor in Hepatocellular Carcinoma

Abstract

BackgroundsAnti‐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.PurposeTo establish a visual noninvasive model based on clinical indicators and MRI features to evaluate VEGF expression in HCC.Study TypeRetrospective.PopulationOne hundred forty HCC patients were randomly divided into a training (N = 98) and a test cohort (N = 42).Field Strength/Sequence3.0 T, T2WI, T1WI including pre‐contrast, dynamic, and hepatobiliary phases.AssessmentThe fusion model constructed by history of smoking, albumin‐to‐globulin ratio (AGR) and the Radio‐Tree model was visualized by a nomogram.Statistical TestsPerformances 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.ResultsHistory 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 ConclusionThe fusion model combining history of smoking, AGR and Radio‐Tree model established with ML algorithm showed the highest AUC value than others.Evidence Level4Technical EfficacyStage 2