What type of study is this?

This is a Quantitative Study study (also classified as: Experimental Study).

September 20, 2025Open Access

Automating classification of treatment responses to combined targeted therapy and immunotherapy in HCC

BQBing QuanFudan University MDMin DaiNanfang Hospital PZPeihong ZhangFujian Medical University

Key Points

The multimodal CNN–Transformer model significantly improved predictions of tumor response to TKI–immunotherapy in patients with HCC.
Validation results showed that the model's area under the curve reached 0.785, outperforming single-modality models and providing reliable insights.
A two-stage 3D U-Net framework was employed for precise liver tumor segmentation, reducing bias from manual input and enhancing model accuracy.
Integration of imaging data and clinical parameters through the multimodal approach supports personalized treatment planning, indicating its clinical value.

Abstract

Abstract Tyrosine kinase inhibitors (TKIs) combined with immunotherapy regimens are now widely used for treating advanced hepatocellular carcinoma (HCC), but their clinical efficacy is limited to a subset of patients. Considering that the vast majority of advanced HCC patients lose the opportunity for liver resection and thus cannot provide tumor tissue samples, we leveraged the clinical and image data to construct a multimodal convolutional neural network (CNN)–Transformer model for predicting and analyzing tumor response to TKI–immunotherapy. An automatic liver tumor segmentation system, based on a two-stage 3D U-Net framework, delineates lesions by first segmenting the liver parenchyma and then precisely localizing the tumor. This approach effectively addresses the variability in clinical data and significantly reduces bias introduced by manual intervention. Thus, we developed a clinical model using only pre-treatment clinical information, a CNN model using only pre-treatment magnetic resonance imaging data, and an advanced multimodal CNN–Transformer model that fused imaging and clinical parameters using a training cohort (n = 181) and then validated them using an independent cohort (n = 30). In the validation cohort, the area under the curve (95% confidence interval) values were 0.720 (0.710–0.731), 0.695 (0.683–0.707), and 0.785 (0.760–0.810), respectively, indicating that the multimodal model significantly outperformed the single-modality baseline models across validations. Finally, single-cell sequencing with the surgical tumor specimens reveals tumor ecosystem diversity associated with treatment response, providing a preliminary biological validation for the prediction model. In summary, this multimodal model effectively integrates imaging and clinical features of HCC patients, has a superior performance in predicting tumor response to TKI–immunotherapy, and provides a reliable tool for optimizing personalized treatment strategies.

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