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

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.