A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects.

Autor: Ghaderi S; Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran., Mohammadi S; Department of Medical Sciences, School of Medicine, Iran University of Medical Sciences, Tehran, Iran., Mohammadi M; Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran., Pashaki ZNA; Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran., Heidari M; Department of Medical Science, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., Khatyal R; Department of Radiology, Faculty of Allied Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran., Zafari R; School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Jazyk: angličtina
Zdroj: Journal of medical radiation sciences [J Med Radiat Sci] 2024 Jun; Vol. 71 (2), pp. 269-289. Date of Electronic Publication: 2024 Jan 18.
DOI: 10.1002/jmrs.756
Abstrakt: Introduction: Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC.
Methods: This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies.
Results: Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival.
Conclusion: Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.
(© 2024 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.)
Databáze: MEDLINE
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