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Ghaderi S, Mohammadi S, Mohammadi M, Pashaki ZNA, Heidari M, Khatyal R, Zafari R. A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects. J Med Radiat Sci 2024; 71:269-289. [PMID: 38234262 PMCID: PMC11177032 DOI: 10.1002/jmrs.756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/06/2024] [Indexed: 01/19/2024] Open
Abstract
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.
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Affiliation(s)
- Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
| | - Sana Mohammadi
- Department of Medical Sciences, School of MedicineIran University of Medical SciencesTehranIran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical SciencesTehranIran
| | | | - Mehrsa Heidari
- Department of Medical Science, School of MedicineAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Rahim Khatyal
- Department of Radiology, Faculty of Allied Medical SciencesTabriz University of Medical SciencesTabrizIran
| | - Rasa Zafari
- School of MedicineTehran University of Medical SciencesTehranIran
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Han T, Liu X, Jing M, Zhang Y, Zhang B, Deng L, Zhou J. ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index. Acta Radiol 2023; 64:3032-3041. [PMID: 37822165 DOI: 10.1177/02841851231205151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
BACKGROUND Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment. PURPOSE To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI). METHODS Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann-Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed. RESULTS All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = -0.012 to -0.293). CONCLUSION ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, PR China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, PR China
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