Abstract: Objective To construct a nomogram model based on Gd-EOB-DTPA enhanced MRI and to investigate its clinical value in predicting post-hepatectomy liver failure (PHLF). Methods The clinical data of 117 hepatocellular carcinoma patients who underwent liver resection at the Department of Hepatobiliary Surgery, First Affiliated Hospital of Army Medical University from September 2019 to November 2020 were retrospectively analyzed. Liver-to-muscle ratio (LMR) was calculated by hepatobiliary phase images of preoperative Gd-EOB-DTPA enhanced MRI to quantitatively evaluate liver function. The independent predictors were screened by univariable and multivariable logistic regression analysis and the prediction model was constructed. Meanwhile, the receiver operating characteristic (ROC) curve and calibration curve were drawn. Results The univariable and multivariable logistic regression analysis showed that MELD score, surgical approach and LMR were independent factors in predicting PHLF (P<0.05). The area under the ROC curve of the model was 0.83, with sensitivity of 91.4% and specificity of 64.6%. The calibration curve showed a good consistency of the model. Conclusions The nomogram model constructed based on Gd-EOB-DTPA enhanced MRI and clinical predictors shows high accuracy in predicting liver failure after liver tumor surgery, and has potential clinical application value.

Key words: Primary liver cancer, Gd-EOB-DTPA enhanced MRI, Post-hepatectomy liver failure, Nomogram, Liver function, Prediction model