Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma

Abstract

Background & aims: Atezolizumab plus bevacizumab (A/B) is a first-line therapy for unresectable hepatocellular carcinoma (HCC). Only a small proportion of patients respond to treatment. This study integrated radiomic and clinical data derived from routine pre-treatment imaging to predict outcomes after immunotherapy.

Methods: A total of 152 patients from two international centres receiving A/B were retrospectively reviewed. Deep learning autosegmentation generated whole liver masks from pre-treatment CTs. Radiomic features combined with clinical variables were used to predict 12-month mortality post A/B. Radiomic and integrated radiomic-clinical models were developed using seven machine learning models in combination with 13 feature selection techniques in the Imperial College London (ICL) cohort. K-means clustering identified high- and low-risk groups and predicted overall survival (OS), progression-free survival (PFS) and response. Model performance was assessed in the independent Assistance Publique-Hôpitaux de Paris (AP-HP) cohort.

Results: The integrated radiomic-clinical model outperformed BCLC stage (AUC 0.61, p <0.001) and ALBI grade (AUC 0.48, p <0.001) in the ICL (AUC 0.89, 95% CI 0.75-0.99) and AP-HP (AUC 0.75, 95% CI 0.64-0.85) cohorts. Integrated model-stratified high-risk patients had significantly shorter median OS (ICL: 5.6 months vs. 28.2 months; p <0.001; AP-HP: 5.8 months vs. 15.7 months; p <0.001) and PFS (ICL: 2.4 months vs. 14.6 months; p <0.001; AP-HP: 2.1 months vs. 6.1 months; p = 0.046). Low-risk patients had significantly higher immune checkpoint inhibitor response rates compared to high-risk patients (35.6% vs. 21.4%; p = 0.038). In multivariable analysis, radiomic group was the strongest predictor of OS (hazard ratio 3.22, 95% CI 1.99-5.20; p <0.001) and PFS (hazard ratio 1.82, 95% CI 1.18-2.80; p = 0.010).

Conclusion: Radiomic-based models predict survival outcomes and response to immunotherapy in patients with advanced HCC. Deep learning in combination with machine learning can stratify patients and allows for precision treatment strategies.

Impact and implications: Prognostic markers predicting survival and response to immunotherapy in hepatocellular carcinoma are lacking. This study used deep learning and machine learning to develop and validate an integrated radiomic-clinical model which can predict survival and response to atezolizumab plus bevacizumab from pre-treatment imaging. Radiomic-based machine learning models can risk-stratify patients with advanced HCC receiving atezolizumab plus bevacizumab.

Keywords: atezolizumab; bevacizumab; hepatocellular carcinoma; immunotherapy; machine learning; radiomics.