MRI Features and Neutrophil-to-Lymphocyte Ratio (NLR)-Based Nomogram to Predict Prognosis of Microvascular...

This study aimed to develop a novel nomogram to predict recurrence-free survival (RFS) for microvascular invasion (MVI)-negative hepatocellular carcinoma (HCC) patients after curative resection.

A total of 143 pathologically confirmed MVI-negative HCC patients were analyzed retrospectively. Baseline MRI features and inflammatory markers were collected. We used univariable and multivariable Cox regression analysis to identify the independent risk factors for RFS. And we established a nomogram based on significant MRI features and inflammatory marker. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve were used to evaluate the predictive accuracy and discriminative ability of the nomogram. The decision curve analysis (DCA) was performed to validate the clinical utility of the nomogram.

In multivariate Cox regression analysis, neutrophil-to-lymphocyte ratio (NLR) (P = 0.018), tumor size (P = 0.002), and tumor capsule (P = 0.000) were independent significant variables associated with RFS. Nomogram with independent factors was developed and achieved a good C-index of 0.730 (95% confidence interval [CI]: 0.656-0.804) for predicting RFS. In ROC analysis, the areas under curve of the nomogram for 1-, 3- and 5-year RFS prediction were 0.725, 0.784 and 0.798, respectively. The risk score calculated by nomogram could divide MVI-negative HCC patients into high-risk group or low-risk group (P < 0.0001). DCA analysis revealed that the nomogram could increase net benefit and exhibited a wider range of threshold probabilities by the risk stratification than the independent risk factors in the prediction of MVI-negative HCC recurrence.

The nomogram prognostic model based on MRI features and NLR for predicting RFS showed high accuracy in MVI-negative HCC patients after curative resection. It can help clinicians make treatment decisions for MVI-negative HCC patients and identify high-risk patients for timely intervention.