Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification
IntroductionRadiomics-based glioblastoma classification demands feature extraction techniques that can effectively capture tumor heterogeneity while maintaining computational efficiency. Conventional tools such as PyRadiomics and CaPTk rely on extensive handcrafted feature sets, which often result i...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1583079/full |
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| author | V. L. Sowmya A. Bharathi Malakreddy Santhi Natarajan N. Prathik I. S. Rajesh |
| author_facet | V. L. Sowmya A. Bharathi Malakreddy Santhi Natarajan N. Prathik I. S. Rajesh |
| author_sort | V. L. Sowmya |
| collection | DOAJ |
| description | IntroductionRadiomics-based glioblastoma classification demands feature extraction techniques that can effectively capture tumor heterogeneity while maintaining computational efficiency. Conventional tools such as PyRadiomics and CaPTk rely on extensive handcrafted feature sets, which often result in redundancy and necessitate further optimization steps.MethodsThis study proposes a novel framework, Spectral Entropic Radiomics Feature Extraction (SERFE), which integrates spectral frequency decomposition, entropy-driven feature selection, and graph-based spatial encoding. SERFE decomposes voxel intensity fluctuations into spectral signatures, employs entropy-based weighting to prioritize informative features, and preserves spatial topology through graph-based modeling. The method was evaluated using the public TCIA glioblastoma dataset.ResultsSERFE generated a refined feature set of 350 radiomic features from an initial pool of 2,260, achieving a 92% stability score and 91.7% classification accuracy. This performance surpasses traditional radiomics methods in both predictive accuracy and feature compactness.DiscussionThe results demonstrate SERFE’s capacity to enhance tumor characterization and streamline radiomics pipelines without requiring post-extraction feature reduction. Its compatibility with existing clinical workflows makes it a promising tool for future neuro-oncology applications. |
| format | Article |
| id | doaj-art-e2c309602c0c4793985ae4a6e0fd93c6 |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-e2c309602c0c4793985ae4a6e0fd93c62025-08-20T03:30:23ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-07-01810.3389/frai.2025.15830791583079Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classificationV. L. Sowmya0A. Bharathi Malakreddy1Santhi Natarajan2N. Prathik3I. S. Rajesh4Department of Artificial Intelligence & Machine Learning, BMS Institute of Technology & Management, Bangalore, IndiaDepartment of Artificial Intelligence & Machine Learning, BMS Institute of Technology & Management, Bangalore, IndiaDepartment of CSE, Shiv Nadar University, Chennai, IndiaItron Inc., Bangalore, IndiaDepartment of Artificial Intelligence & Machine Learning, BMS Institute of Technology & Management, Bangalore, IndiaIntroductionRadiomics-based glioblastoma classification demands feature extraction techniques that can effectively capture tumor heterogeneity while maintaining computational efficiency. Conventional tools such as PyRadiomics and CaPTk rely on extensive handcrafted feature sets, which often result in redundancy and necessitate further optimization steps.MethodsThis study proposes a novel framework, Spectral Entropic Radiomics Feature Extraction (SERFE), which integrates spectral frequency decomposition, entropy-driven feature selection, and graph-based spatial encoding. SERFE decomposes voxel intensity fluctuations into spectral signatures, employs entropy-based weighting to prioritize informative features, and preserves spatial topology through graph-based modeling. The method was evaluated using the public TCIA glioblastoma dataset.ResultsSERFE generated a refined feature set of 350 radiomic features from an initial pool of 2,260, achieving a 92% stability score and 91.7% classification accuracy. This performance surpasses traditional radiomics methods in both predictive accuracy and feature compactness.DiscussionThe results demonstrate SERFE’s capacity to enhance tumor characterization and streamline radiomics pipelines without requiring post-extraction feature reduction. Its compatibility with existing clinical workflows makes it a promising tool for future neuro-oncology applications.https://www.frontiersin.org/articles/10.3389/frai.2025.1583079/fullspectral radiomicsentropy-weighted feature selectiongraph-theoretic encodingadaptive radiomics modellingglioblastoma classificationTCIA-based radiomics |
| spellingShingle | V. L. Sowmya A. Bharathi Malakreddy Santhi Natarajan N. Prathik I. S. Rajesh Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification Frontiers in Artificial Intelligence spectral radiomics entropy-weighted feature selection graph-theoretic encoding adaptive radiomics modelling glioblastoma classification TCIA-based radiomics |
| title | Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification |
| title_full | Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification |
| title_fullStr | Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification |
| title_full_unstemmed | Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification |
| title_short | Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification |
| title_sort | spectral entropic radiomics feature extraction serfe an adaptive approach for glioblastoma disease classification |
| topic | spectral radiomics entropy-weighted feature selection graph-theoretic encoding adaptive radiomics modelling glioblastoma classification TCIA-based radiomics |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1583079/full |
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