# Integration of multiparametric MRI and clinical indicators to predict response to immune-targeted therapy in patients with advanced hepatocellular carcinoma

## TL;DR

This study combines MRI scans and clinical data to predict how patients with advanced liver cancer will respond to immune therapy.

## Contribution

A novel MRI-clinical nomogram model is proposed to predict treatment response in advanced hepatocellular carcinoma.

## Key findings

- The combined MRI and clinical model achieved an AUC of 0.811 for predicting treatment response.
- Post-treatment T2 signal intensity ratio and ADC mean value were significant imaging predictors.
- High-risk patients had shorter progression-free survival compared to low-risk patients.

## Abstract

The aim of this investigation is to evaluate the efficacy of a predictive model integrating multiparametric MRI and clinical indicators for forecasting the therapeutic response to immune-targeted therapy in patients with advanced hepatocellular carcinoma (HCC).

This retrospective analysis included 78 patients with HCC who received immune-targeted therapy between January 2021 and October 2024. Abdominal MRI scans were conducted within 2 weeks prior to treatment initiation and again at 8 weeks post-treatment. Complete pre-treatment laboratory data were available for all patients. Based on the Modified Response Evaluation Criteria in Solid Tumors (mRECIST), the patients were categorized into either a disease control group (n = 32) or a progression group (n = 46). The most discriminative features were selected via LASSO regression, and the optimal predictive factors were constructed based on the λ.1se criterion determined through 10-fold cross-validation. Subsequently, independent predictors were identified using multivariate logistic regression analysis. Prediction models based on imaging, clinical, and combined variables were constructed and evaluated using receiver operating characteristic (ROC) curves. In addition, decision curve analysis and calibration curves were employed to assess the predictive accuracy and discriminative ability of the nomogram. Progression-free survival (PFS) was estimated with Kaplan–Meier analysis.

Independent predictors for response to therapy in advanced HCC included the post-treatment T2 signal intensity ratio (T2 SIR) (p = 0.003), post-treatment apparent diffusion coefficient (ADC) mean value (p = 0.004), and neutrophil to lymphocyte ratio (NLR) (p = 0.013). The areas under the ROC curves for the imaging, clinical, and combined nomogram models were 0.751 (95% CI: 0.639–0.863), 0.614 (95% CI: 0.482–0.744), and 0.811 (95% CI: 0.713–0.910), respectively. Moreover, patients in the high-risk group experienced a significantly shorter median PFS compared to those in the low-risk group (5.0 vs. 7.0 months; p < 0.05).

The MRI–clinical nomogram provided effective discrimination of treatment responses to immune-targeted therapy in advanced HCC, thereby enhancing predictive efficiency.