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 tumour marker prognostic study.

Abstract

Background: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making 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<0.0001). Moreover, integrated model scores exhibited associations with sequential resection among patients who achieved DCB and pathological complete response (pCR) among those who underwent sequential resection procedures. Notably, higher radiomics model was correlated with MHC I expression, angiogenesis-related processes, CD8 T cell-related gene sets, as well as a higher frequency of TP53 mutations along with increased levels of mutation burden and neoantigen.

Conclusion: The deep learning-based clinical-radiomics model exhibited satisfactory predictive capability in forecasting the DCB derived from ICIs-based conversion therapy in potentially convertible HCC, and was associated with a diverse range of immune-related mechanisms.