Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911142698287104 |
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| author | Tikhonov, Daniil Scatolin, Matheus Banerjee, Mohor Ji, Qiankun Jaheen, Ahmed Salem, Mostafa Elsayed, Abdelrahman Wang, Hu Hashmi, Sarim Yaqub, Mohammad |
| author_facet | Tikhonov, Daniil Scatolin, Matheus Banerjee, Mohor Ji, Qiankun Jaheen, Ahmed Salem, Mostafa Elsayed, Abdelrahman Wang, Hu Hashmi, Sarim Yaqub, Mohammad |
| contents | Accurate evaluation of the response of glioblastoma to therapy is crucial for clinical decision-making and patient management. The Response Assessment in Neuro-Oncology (RANO) criteria provide a standardized framework to assess patients' clinical response, but their application can be complex and subject to observer variability. This paper presents an automated method for classifying the intervention response from longitudinal MRI scans, developed to predict tumor response during therapy as part of the BraTS 2025 challenge. We propose a novel hybrid framework that combines deep learning derived feature extraction and an extensive set of radiomics and clinically chosen features. Our approach utilizes a fine-tuned ResNet-18 model to extract features from 2D regions of interest across four MRI modalities. These deep features are then fused with a rich set of more than 4800 radiomic and clinically driven features, including 3D radiomics of tumor growth and shrinkage masks, volumetric changes relative to the nadir, and tumor centroid shift. Using the fused feature set, a CatBoost classifier achieves a mean ROC AUC of 0.81 and a Macro F1 score of 0.50 in the 4-class response prediction task (Complete Response, Partial Response, Stable Disease, Progressive Disease). Our results highlight that synergizing learned image representations with domain-targeted radiomic features provides a robust and effective solution for automated treatment response assessment in neuro-oncology. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_06511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach Tikhonov, Daniil Scatolin, Matheus Banerjee, Mohor Ji, Qiankun Jaheen, Ahmed Salem, Mostafa Elsayed, Abdelrahman Wang, Hu Hashmi, Sarim Yaqub, Mohammad Computer Vision and Pattern Recognition Accurate evaluation of the response of glioblastoma to therapy is crucial for clinical decision-making and patient management. The Response Assessment in Neuro-Oncology (RANO) criteria provide a standardized framework to assess patients' clinical response, but their application can be complex and subject to observer variability. This paper presents an automated method for classifying the intervention response from longitudinal MRI scans, developed to predict tumor response during therapy as part of the BraTS 2025 challenge. We propose a novel hybrid framework that combines deep learning derived feature extraction and an extensive set of radiomics and clinically chosen features. Our approach utilizes a fine-tuned ResNet-18 model to extract features from 2D regions of interest across four MRI modalities. These deep features are then fused with a rich set of more than 4800 radiomic and clinically driven features, including 3D radiomics of tumor growth and shrinkage masks, volumetric changes relative to the nadir, and tumor centroid shift. Using the fused feature set, a CatBoost classifier achieves a mean ROC AUC of 0.81 and a Macro F1 score of 0.50 in the 4-class response prediction task (Complete Response, Partial Response, Stable Disease, Progressive Disease). Our results highlight that synergizing learned image representations with domain-targeted radiomic features provides a robust and effective solution for automated treatment response assessment in neuro-oncology. |
| title | Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.06511 |