Bibliographic Details
| Title: |
A review of deep learning for brain tumor analysis in MRI |
| Authors: |
Felix J. Dorfner, Jay B. Patel, Jayashree Kalpathy-Cramer, Elizabeth R. Gerstner, Christopher P. Bridge |
| Source: |
npj Precision Oncology, Vol 9, Iss 1, Pp 1-13 (2025) |
| Publisher Information: |
Nature Portfolio, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| Subject Terms: |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2397-768X |
| Relation: |
https://doaj.org/toc/2397-768X |
| DOI: |
10.1038/s41698-024-00789-2 |
| Access URL: |
https://doaj.org/article/b539f443dfde447a8088bf95a8126a9d |
| Accession Number: |
edsdoj.b539f443dfde447a8088bf95a8126a9d |
| Database: |
Directory of Open Access Journals |
