Radiomics-based prediction of HCC response to atezolizumab/bevacizumab.

Abstract

636 Background: Advanced hepatocellular carcinoma (HCC) treatment has evolved with the introduction of atezolizumab/bevacizumab, showing improved outcomes over sorafenib. However, the response varies among patients, particularly between viral and non-viral etiologies. This study aimed to develop and evaluate multimodal prediction models combining quantitative imaging and clinical markers to predict treatment response in HCC patients. Methods: From March 2020 to May 2023, patients with advanced HCC treated with atezolizumab/bevacizumab were retrospectively identified from six centers in Germany and Austria. Patients underwent baseline contrast-enhanced liver MRI and follow-up imaging to assess therapy response. Machine learning models, including RandomForestClassifier, were developed for radiomics, clinical, and combined datasets. Hyperparameter tuning was performed using RandomizedSearchCV, followed by cross-validation to evaluate model performance. Results: The study included 103 patients, with 70 achieving disease control (DC) and 33 experiencing disease progression (PD). Key findings included significant differences in treatment response and progression-free survival between DC and PD groups. The radiomics model, using 14 selected features, achieved 73.1% accuracy and a ROC AUC of 0.635 on the test set. The clinical model, with 4 selected features, achieved 73% accuracy and a ROC AUC of 0.649 on the test set. The combined model showed improved performance with 69% accuracy and a ROC AUC of 0.753 on the test set. Hyperparameter tuning further enhanced the combined model's accuracy to 80.1% and ROC AUC to 0.771 on the test set. Conclusions: The hybrid model combining clinical and radiological data outperformed individual models, providing better predictions of response to atezolizumab/bevacizumab in HCC patients.