A review of deep learning for brain tumor analysis in MRI
Abstract Recent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-024-00789-2 |
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| _version_ | 1841559908452925440 |
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| author | Felix J. Dorfner Jay B. Patel Jayashree Kalpathy-Cramer Elizabeth R. Gerstner Christopher P. Bridge |
| author_facet | Felix J. Dorfner Jay B. Patel Jayashree Kalpathy-Cramer Elizabeth R. Gerstner Christopher P. Bridge |
| author_sort | Felix J. Dorfner |
| collection | DOAJ |
| description | Abstract Recent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective and reproducible measurements crucial for diagnosis, treatment planning, and disease monitoring. Furthermore, it holds the potential to pave the way for personalized medicine through the prediction of tumor type, grade, genetic mutations, and patient survival outcomes. In this review, we explore the transformative potential of DL for brain tumor care and discuss existing applications, limitations, and future directions and opportunities. |
| format | Article |
| id | doaj-art-b539f443dfde447a8088bf95a8126a9d |
| institution | Kabale University |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-b539f443dfde447a8088bf95a8126a9d2025-01-05T12:07:22ZengNature Portfolionpj Precision Oncology2397-768X2025-01-019111310.1038/s41698-024-00789-2A review of deep learning for brain tumor analysis in MRIFelix J. Dorfner0Jay B. Patel1Jayashree Kalpathy-Cramer2Elizabeth R. Gerstner3Christopher P. Bridge4Athinoula A. Martinos Center for Biomedical ImagingAthinoula A. Martinos Center for Biomedical ImagingUniversity of Colorado School of MedicineAthinoula A. Martinos Center for Biomedical ImagingAthinoula A. Martinos Center for Biomedical ImagingAbstract Recent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective and reproducible measurements crucial for diagnosis, treatment planning, and disease monitoring. Furthermore, it holds the potential to pave the way for personalized medicine through the prediction of tumor type, grade, genetic mutations, and patient survival outcomes. In this review, we explore the transformative potential of DL for brain tumor care and discuss existing applications, limitations, and future directions and opportunities.https://doi.org/10.1038/s41698-024-00789-2 |
| spellingShingle | Felix J. Dorfner Jay B. Patel Jayashree Kalpathy-Cramer Elizabeth R. Gerstner Christopher P. Bridge A review of deep learning for brain tumor analysis in MRI npj Precision Oncology |
| title | A review of deep learning for brain tumor analysis in MRI |
| title_full | A review of deep learning for brain tumor analysis in MRI |
| title_fullStr | A review of deep learning for brain tumor analysis in MRI |
| title_full_unstemmed | A review of deep learning for brain tumor analysis in MRI |
| title_short | A review of deep learning for brain tumor analysis in MRI |
| title_sort | review of deep learning for brain tumor analysis in mri |
| url | https://doi.org/10.1038/s41698-024-00789-2 |
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