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

[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.