Radiomics-based prediction of HCC response to atezolizumab/bevacizumab

Rodriguez,Isaac; Vellala,Abhinay; Itzel,Timo; Daza,Jimmy; Vácha,Michael; Chang,De-Hua; Debic,Manuel; Dill,Michael T.; Seidensticker,Max; Mayerle,Julia; Munker,Stefan; Schoenberg,Stefan O.; Müller,Lukas; Galle,Peter R.; Weinmann,Arndt; Tamandl,Dietmar; Pinter,Matthias; Scheiner,Bernhard; Weiss,Christel; Pech,Maciej; Sinner,Friedrich; Keitel,Verena; Venerito,Marino; Ebert,Matthias Philip; Teufel,Andreas; Froelich,Matthias F.

doi:10.3892/ol.2025.15229

Radiomics-based prediction of HCC response to atezolizumab/bevacizumab

Authors:
- Isaac Rodriguez
- Abhinay Vellala
- Timo Itzel
- Jimmy Daza
- Michael Vácha
- De-Hua Chang
- Manuel Debic
- Michael T. Dill
- Max Seidensticker
- Julia Mayerle
- Stefan Munker
- Stefan O. Schoenberg
- Lukas Müller
- Peter R. Galle
- Arndt Weinmann
- Dietmar Tamandl
- Matthias Pinter
- Bernhard Scheiner
- Christel Weiss
- Maciej Pech
- Friedrich Sinner
- Verena Keitel
- Marino Venerito
- Matthias Philip Ebert
- Andreas Teufel
- Matthias F. Froelich
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Affiliations: Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, D-68167 Mannheim, Germany, Department of Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, D-68167 Mannheim, Germany, Department of Radiology, Cantonal Hospital of Lucerne, 6000 Lucerne, Switzerland, Department of Radiology, Medical Faculty Heidelberg, Heidelberg University, D-69120 Heidelberg, Germany, Department of Gastroenterology, Hepatology, Infectious Diseases and Intoxications, Heidelberg University Hospital, D-69120 Heidelberg, Germany, Department of Radiology, University Hospital, Ludwig-Maximilians-Universität Munich, D-80336 Munich, Germany, Department of Medicine II, University Hospital, Ludwig-Maximilians-Universität Munich, D-80336 Munich, Germany, Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz University, D-55131 Mainz, Germany, University Medical Center I, Department of Medicine, Mainz University, D-55131 Mainz, Germany, Department of Radiology, Medical Faculty Vienna, Vienna University, A-1090 Vienna, Austria, Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, A-1090 Vienna, Austria, Department of Medical Statistics, Biomathematics and Information Processing, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, Department of Radiology and Nuclear Medicine, Magdeburg University, D-39120 Magdeburg, Germany, Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty Magdeburg, Magdeburg University, D-39120 Magdeburg, Germany, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, D-68167 Mannheim, Germany
Published online on: August 14, 2025 https://doi.org/10.3892/ol.2025.15229
Article Number: 484
Copyright: © Rodriguez et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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. The present study aimed to develop and evaluate multimodal prediction models combining quantitative imaging and clinical markers to predict the treatment response in patients with HCC. Between March 2020 and 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 the 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. 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 the DC and PD groups. The radiomics model, using 14 selected features, achieved 73.1% accuracy and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.635 for the test set. The clinical model, with 4 selected features, achieved 73% accuracy and a ROC AUC of 0.649 for the test set. The combined model showed improved performance, with 69% accuracy and a ROC AUC of 0.753 for the test set. Hyperparameter tuning further enhanced the accuracy of the combined model to 80.1% and the ROC AUC to 0.771 for the test set. In conclusion, the hybrid model combining clinical and radiological data outperformed individual models, providing improved predictions of response to atezolizumab/bevacizumab in patients with HCC.

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