Radiomics in glioma: emerging trends and challenges
Abstract Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics compon...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Annals of Clinical and Translational Neurology |
| Online Access: | https://doi.org/10.1002/acn3.52306 |
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| _version_ | 1849344486890536960 |
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| author | Zihan Wang Lei Wang Yinyan Wang |
| author_facet | Zihan Wang Lei Wang Yinyan Wang |
| author_sort | Zihan Wang |
| collection | DOAJ |
| description | Abstract Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. Moreover, investigating specific tumor habitats of glioblastomas aids in a better understanding of glioblastoma aggressiveness and the development of effective treatment strategies. Additionally, advanced imaging techniques, such as diffusion‐weighted imaging, perfusion‐weighted imaging, magnetic resonance spectroscopy, magnetic resonance fingerprinting, functional MRI, and positron emission tomography, can provide supplementary information for tumor characterization and classification. Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune‐related biomarkers and characterizing immune responses within tumors. Integrating multi‐omics data, such as genomics, transcriptomics, proteomics, and pathomics, with radiomics, aids the understanding of the biological significance of the underlying radiomics features and improves the prediction of genetic mutations, prognosis, and treatment response in patients with glioma. Addressing challenges, such as model reproducibility, model generalizability, model interpretability, and multi‐omics data integration, is crucial for the clinical translation of radiomics in glioma. |
| format | Article |
| id | doaj-art-4523a6d7be9d4f3ba0ca9949b45e2666 |
| institution | Kabale University |
| issn | 2328-9503 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Annals of Clinical and Translational Neurology |
| spelling | doaj-art-4523a6d7be9d4f3ba0ca9949b45e26662025-08-20T03:42:39ZengWileyAnnals of Clinical and Translational Neurology2328-95032025-03-0112346047710.1002/acn3.52306Radiomics in glioma: emerging trends and challengesZihan Wang0Lei Wang1Yinyan Wang2Department of Neurosurgery, Beijing Tiantan Hospital Capital Medical University Beijing ChinaDepartment of Neurosurgery Guiqian International General Hospital Guiyang Guizhou ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital Capital Medical University Beijing ChinaAbstract Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. Moreover, investigating specific tumor habitats of glioblastomas aids in a better understanding of glioblastoma aggressiveness and the development of effective treatment strategies. Additionally, advanced imaging techniques, such as diffusion‐weighted imaging, perfusion‐weighted imaging, magnetic resonance spectroscopy, magnetic resonance fingerprinting, functional MRI, and positron emission tomography, can provide supplementary information for tumor characterization and classification. Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune‐related biomarkers and characterizing immune responses within tumors. Integrating multi‐omics data, such as genomics, transcriptomics, proteomics, and pathomics, with radiomics, aids the understanding of the biological significance of the underlying radiomics features and improves the prediction of genetic mutations, prognosis, and treatment response in patients with glioma. Addressing challenges, such as model reproducibility, model generalizability, model interpretability, and multi‐omics data integration, is crucial for the clinical translation of radiomics in glioma.https://doi.org/10.1002/acn3.52306 |
| spellingShingle | Zihan Wang Lei Wang Yinyan Wang Radiomics in glioma: emerging trends and challenges Annals of Clinical and Translational Neurology |
| title | Radiomics in glioma: emerging trends and challenges |
| title_full | Radiomics in glioma: emerging trends and challenges |
| title_fullStr | Radiomics in glioma: emerging trends and challenges |
| title_full_unstemmed | Radiomics in glioma: emerging trends and challenges |
| title_short | Radiomics in glioma: emerging trends and challenges |
| title_sort | radiomics in glioma emerging trends and challenges |
| url | https://doi.org/10.1002/acn3.52306 |
| work_keys_str_mv | AT zihanwang radiomicsingliomaemergingtrendsandchallenges AT leiwang radiomicsingliomaemergingtrendsandchallenges AT yinyanwang radiomicsingliomaemergingtrendsandchallenges |