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| Format: | Preprint |
| Published: |
2024
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| Online Access: | https://arxiv.org/abs/2409.16329 |
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| _version_ | 1866909325203603456 |
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| author | Kozák, Stanislav |
| author_facet | Kozák, Stanislav |
| contents | Radiomics is a relatively new field which utilises automatically identified features from radiological scans. It has found a widespread application, particularly in oncology because many of the important oncological biomarkers are not visible to the naked eye. The recent advent of big data, including in medical imaging, and the development of new ML techniques brought the possibility of faster and more accurate oncological diagnosis. Furthermore, standardised mathematical feature extraction based on radiomics helps to eliminate possible radiologist bias. This paper reviews the recent development in the oncological use of MRI radiomic features. It focuses on the identification of the isocitrate dehydrogenase (IDH) mutation status, which is an important biomarker for the diagnosis of glioblastoma and grade IV astrocytoma. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16329 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MRI Radiomics for IDH Genotype Prediction in Glioblastoma Diagnosis Kozák, Stanislav Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning I.2; I.4; J.3 Radiomics is a relatively new field which utilises automatically identified features from radiological scans. It has found a widespread application, particularly in oncology because many of the important oncological biomarkers are not visible to the naked eye. The recent advent of big data, including in medical imaging, and the development of new ML techniques brought the possibility of faster and more accurate oncological diagnosis. Furthermore, standardised mathematical feature extraction based on radiomics helps to eliminate possible radiologist bias. This paper reviews the recent development in the oncological use of MRI radiomic features. It focuses on the identification of the isocitrate dehydrogenase (IDH) mutation status, which is an important biomarker for the diagnosis of glioblastoma and grade IV astrocytoma. |
| title | MRI Radiomics for IDH Genotype Prediction in Glioblastoma Diagnosis |
| topic | Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning I.2; I.4; J.3 |
| url | https://arxiv.org/abs/2409.16329 |