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Article Open Access

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
  • View Affiliations / Copyright

    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
    Copyright: © Rodriguez et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 484
    |
    Published online on: August 14, 2025
       https://doi.org/10.3892/ol.2025.15229
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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.
View Figures

Figure 1

Overall and progression-free
survival. Survival curves: (A and C) Overall and (B and D)
progression-free survival according to (A and B) control vs.
progression and (C and D) modified Response Evaluation Criteria on
Solid Tumors category. CR, complete response; PD, disease
progression; PR, partial response; SD, stable disease.

Figure 2

Radiomics features represented as a
heatmap in the ROI. Example of a radiomics heatmap in (A) a
62-year-old male patient who responded to therapy and survived for
at least 484 days after the beginning of atezolizumab and
bevacizumab treatment and (B) a 70-year-old male patient who
progressed in spite of therapy but still survived at least 266 days
after the beginning of immunotherapy. The ROI corresponds to a
hepatocellular carcinoma tumor and the radiomics features depicted
are: Large area low gray level emphasis, which highlights regions
of large, low-intensity areas, with high values indicating
homogeneous, low-contrast zones; and zone variance, which measures
the variability in zone sizes, where high values suggest greater
heterogeneity in texture, while low values reflect more uniform
zone sizes. ROI, region of interest.

Figure 3

Feature importance in the different
models. (A) Radiomics model. 1, GLSZM large area low gray level
emphasis; 2, GLCM cluster shade; 3, GLSZM zone variance; 4, GLSZM
large area high gray level emphasis; 5, GLCM joint average; 6,
first order skewness; 7, GLSZM large area emphasis; 8, GLRLM short
run high gray level emphasis; 9, GLRLM long run low gray level
emphasis; 10, GLCM autocorrelation; 11, GLCM sum average; 12, GLSZM
low gray level zone emphasis; 13, GLRLM high gray level run
emphasis; and 14, GLDM high gray level emphasis. (B) Clinical
model. 1, C-reactive protein; 2, metastasis status; 3, previous
systemic therapy; and 4, previous TACE. (C) Combined model. 1,
GLSZM zone variance; 2, GLSZM large area high gray level emphasis;
3, GLRLM high gray level run emphasis; 4, GLDM high gray level
emphasis; 5, GLRLM long run low gray level emphasis; 6, GLCM joint
average; 7, GLCM autocorrelation; 8, first order skewness; 9, GLRLM
short run high gray level emphasis; 10, GLSZM low gray level zone
emphasis; 11, previous systemic therapy; 12, metastasis status; 13,
GLCM sum average; 14, C-reactive protein; 15, GLSZM large area
emphasis; 16, GLSZM large area low gray level emphasis; 17, GLCM
cluster shade; and 18, previous TACE. GLCM, gray level
co-occurrence matrix; GLDM, gray level dependence matrix; GLRLM,
gray level run length matrix; GLSZM, gray level size zone matrix;
TACE, transarterial chemoembolization.

Figure 4

ROC curves for each of the different
models. ROC curves for the (A) radiomics, (B) clinical and (C)
combined models. ROC, receiver operating characteristic.

Figure 5

ROC curves after hyperparameter
tuning. ROC curves of the (A) training set and (B) testing set
after hyperparameter tuning. For the combined model, the AUC was
0.77 in the testing set, with an accuracy of 80.1%, sensitivity of
62.5% and specificity of 89%. AUC, area under the curve; ROC,
receiver operating characteristic.
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Copy and paste a formatted citation
Spandidos Publications style
Rodriguez I, Vellala A, Itzel T, Daza J, Vácha M, Chang D, Debic M, Dill MT, Seidensticker M, Mayerle J, Mayerle J, et al: Radiomics-based prediction of HCC response to atezolizumab/bevacizumab. Oncol Lett 30: 484, 2025.
APA
Rodriguez, I., Vellala, A., Itzel, T., Daza, J., Vácha, M., Chang, D. ... Froelich, M.F. (2025). Radiomics-based prediction of HCC response to atezolizumab/bevacizumab. Oncology Letters, 30, 484. https://doi.org/10.3892/ol.2025.15229
MLA
Rodriguez, I., Vellala, A., Itzel, T., Daza, J., Vácha, M., Chang, D., Debic, M., Dill, M. T., Seidensticker, M., Mayerle, J., Munker, S., Schoenberg, S. O., Müller, L., Galle, P. R., Weinmann, A., Tamandl, D., Pinter, M., Scheiner, B., Weiss, C., Pech, M., Sinner, F., Keitel, V., Venerito, M., Ebert, M. P., Teufel, A., Froelich, M. F."Radiomics-based prediction of HCC response to atezolizumab/bevacizumab". Oncology Letters 30.4 (2025): 484.
Chicago
Rodriguez, I., Vellala, A., Itzel, T., Daza, J., Vácha, M., Chang, D., Debic, M., Dill, M. T., Seidensticker, M., Mayerle, J., Munker, S., Schoenberg, S. O., Müller, L., Galle, P. R., Weinmann, A., Tamandl, D., Pinter, M., Scheiner, B., Weiss, C., Pech, M., Sinner, F., Keitel, V., Venerito, M., Ebert, M. P., Teufel, A., Froelich, M. F."Radiomics-based prediction of HCC response to atezolizumab/bevacizumab". Oncology Letters 30, no. 4 (2025): 484. https://doi.org/10.3892/ol.2025.15229
Copy and paste a formatted citation
x
Spandidos Publications style
Rodriguez I, Vellala A, Itzel T, Daza J, Vácha M, Chang D, Debic M, Dill MT, Seidensticker M, Mayerle J, Mayerle J, et al: Radiomics-based prediction of HCC response to atezolizumab/bevacizumab. Oncol Lett 30: 484, 2025.
APA
Rodriguez, I., Vellala, A., Itzel, T., Daza, J., Vácha, M., Chang, D. ... Froelich, M.F. (2025). Radiomics-based prediction of HCC response to atezolizumab/bevacizumab. Oncology Letters, 30, 484. https://doi.org/10.3892/ol.2025.15229
MLA
Rodriguez, I., Vellala, A., Itzel, T., Daza, J., Vácha, M., Chang, D., Debic, M., Dill, M. T., Seidensticker, M., Mayerle, J., Munker, S., Schoenberg, S. O., Müller, L., Galle, P. R., Weinmann, A., Tamandl, D., Pinter, M., Scheiner, B., Weiss, C., Pech, M., Sinner, F., Keitel, V., Venerito, M., Ebert, M. P., Teufel, A., Froelich, M. F."Radiomics-based prediction of HCC response to atezolizumab/bevacizumab". Oncology Letters 30.4 (2025): 484.
Chicago
Rodriguez, I., Vellala, A., Itzel, T., Daza, J., Vácha, M., Chang, D., Debic, M., Dill, M. T., Seidensticker, M., Mayerle, J., Munker, S., Schoenberg, S. O., Müller, L., Galle, P. R., Weinmann, A., Tamandl, D., Pinter, M., Scheiner, B., Weiss, C., Pech, M., Sinner, F., Keitel, V., Venerito, M., Ebert, M. P., Teufel, A., Froelich, M. F."Radiomics-based prediction of HCC response to atezolizumab/bevacizumab". Oncology Letters 30, no. 4 (2025): 484. https://doi.org/10.3892/ol.2025.15229
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