JMIR Publications

ABSTRACT

Background:

Hepatocellular carcinoma (HCC) treatment selection demands nuanced integration of heterogeneous patient data, yet prevailing predictive models rely on restricted data modalities and oversimplified therapeutic frameworks, compromising clinical translation.

Objective:

We developed and validated a multimodal artificial intelligence framework to guide optimal treatment strategy selection across the full spectrum of HCC interventions.

Methods:

This retrospective study comprised 1,043 HCC patients (development cohort, January 2017–December 2023) and 55 external validation patients (2023) from Wuxi People’s Hospital. We engineered Embedding-Augmented Extra Trees (ET-Emb), a novel model fusing structured clinical variables with contextual text embeddings derived from medical histories and radiology reports. ET-Emb quantifies probabilities for five primary treatments: open/laparoscopic resection, transarterial chemoembolization, radiofrequency ablation (RFA), and chemotherapy. Model performance was rigorously assessed via 10-fold cross-validation and external validation using ROC-AUC and PR-AUC metrics.

Results:

ET-Emb demonstrated robust performance in the development cohort (ROC-AUC: 0.84 ± 0.04; PR-AUC: 0.55 ± 0.06), significantly outperforming established benchmarks. This generalizability was preserved in external validation (ROC-AUC: 0.77 ± 0.02; PR-AUC: 0.47 ± 0.03). SHAP analysis identified textual clinical narratives and socioeconomic determinants as critical predictive drivers.

Conclusions:

By unifying structured and unstructured data modalities, ET-Emb delivers accurate, multi-treatment strategy prediction for HCC. Its clinical validity and the demonstrated significance of textual features establish multimodal AI as an essential paradigm for simulating complex oncological decision-making, positioning ET-Emb as a transformative tool for precision HCC management.