A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.

Résumé

BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibitors (ICIs)-based conversion therapy a primary option. However, challenges persist in predicting response and identifying the optimal patient subset. The objective is to develop a CT-based clinical-radiomics model to predict durable clinical benefit (DCB) of ICIs-based treatment in potentially convertible HCC patients.METHODS: The radiomics features were extracted by pyradiomics in training set, and machine learning models was generated based on the selected radiomics features. Deep learning models were created using two different protocols. Integrated models were constructed by incorporating radiomics scores, deep learning scores, and clinical variables selected through multivariate analysis. Furthermore, we analyzed the relationship between integrated model scores and clinical outcomes related to conversion therapy in the entire cohort. Finally, radiogenomic analysis was conducted on bulk RNA and DNA sequencing data.RESULTS: The top-performing integrated model demonstrated excellent predictive accuracy with an area under the curve (AUC) of 0.96 (95% CI: 0.94-0.99) in the training set and 0.88 (95% CI: 0.77-0.99) in the test set, effectively stratifying survival risk across the entire cohort and revealing significant disparity in overall survival (OS), as evidenced by Kaplan-Meier survival curves ( P