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- Alexander Nowakowski
- Rosalind and Morris Goodman Cancer Institute, McGill University , Montreal, Québec , Canada
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- Zubin Lahijanian
- McGill University Health Centre, Department of Diagnostic Radiology, McGill University , Montreal, Québec , Canada
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- Valerie Panet-Raymond
- McGill University Health Centre, Department of Diagnostic Radiology, McGill University , Montreal, Québec , Canada
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- Peter M Siegel
- Rosalind and Morris Goodman Cancer Institute, McGill University , Montreal, Québec , Canada
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- Kevin Petrecca
- Montreal Neurological Institute-Hospital, McGill University , Montreal, Québec , Canada
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- Farhad Maleki
- Department of Computer Science, University of Calgary , Calgary, Alberta , Canada
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- Matthew Dankner
- Rosalind and Morris Goodman Cancer Institute, McGill University , Montreal, Québec , Canada
書誌事項
- 公開日
- 2022-01-01
- 権利情報
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- https://creativecommons.org/licenses/by/4.0/
- DOI
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- 10.1093/noajnl/vdac141
- 公開者
- Oxford University Press (OUP)
説明
<jats:title>Abstract</jats:title><jats:p>Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than 1 year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.</jats:p>
収録刊行物
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- Neuro-Oncology Advances
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Neuro-Oncology Advances 4 (1), 2022-01-01
Oxford University Press (OUP)
