Nomogram based on the neutrophil-to-lymphocyte ratio and MR diffusion quantitative parameters for predicting Ki67 expression in hepatocellular carcinoma from a prospective study

This study aimed to establish and validate a multiparameter prediction model for Ki67 expression in hepatocellular carcinoma (HCC) patients while also exploring its potential to predict the one-year recurrence risk. The clinical, pathological, and imaging data of 83 patients with HCC confirmed by postoperative pathology were analyzed, and the patients were randomly divided into a training set (n = 58) and a validation set (n = 25) at a ratio of 7:3. All patients underwent a magnetic resonance imaging (MRI) scan that included multi-b value diffusion-weighted scanning before surgery, and quantitative parameters were obtained via intravoxel incoherent motion (IVIM) and diffusion kurtosis (DKI) models. Univariate and multivariate logistic regression analyses were conducted using the training set data to construct a model, which was internally validated. The area under the curve (AUC) of the receiver operating characteristics (ROC), a decision curve analysis (DCA), and a calibration analysis were used to evaluate the model's performance. Additionally, for patients with available follow-up data, the combined model was evaluated for its potential utility in predicting the one-year recurrence risk by analyzing the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.The combined model outperformed the clinicaland parametric models in predicting high Ki67 expression. The nomograms based on the combined model included the neutrophil-to-lymphocyte ratio (NLR), ADCslow_Aver. The model showed strong discrimination in the training set, with an AUC of 0.836 (95% CI: 0.729-0.942) and acceptable calibration (Hosmer-Lemeshow p = 0.109). In the validation set, the model maintained moderate discrimination (AUC 0.806, 95% CI: 0.621-0.990) with good calibration (p = 0.663). DCA revealed that the combined model provided good clinical value and correction effects. Additionally, when used to predict the one-year recurrence risk, the combined model achieved moderate accuracy (AUC = 0.747), highlighting its potential utility in identifying patients at a higher risk of recurrence. A nomogram incorporating the NLR and quantitative MR diffusion parameters effectively predicts Ki67 expression in HCC patients before surgery. The model also shows promise in predicting recurrence risk, which may aid in postoperative risk stratification and patient management. Clinical Relevance Statement We established a model that incorporated the NLR and quantitative magnetic resonance diffusion parameters, which demonstrated robust performance in predicting both high Ki67 expression and the one-year recurrence risk in HCC patients. This model shows potential clinical value in guiding postoperative risk stratification and personalized treatment planning.