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Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
Published on: June 7, 2020
Magnetic Resonance Imaging Features for Predicting Brain Invasion in Meningiomas: A Systematic Review and
Huan Huang1, Yin Gao1, Lunxing Wu2
1Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China.
Objective:
To systematically assess magnetic resonance imaging (MRI) features of brain invasion (BI) in meningiomas and to evaluate diagnostic performance of MRI for prediction of BI in meningiomas.
Methods:
A comprehensive search was conducted on Web of Science, PubMed, and EMBASE from January 1, 2016, to August 1, 2024, to confirm eligible original articles. Data extracted from the articles included sample size, number of patients with BI or without BI, mean age, male/female ratio, authors, publication year, duration of patient care, study design, strength (tesla) of magnetic field, imaging sequences used, use of radiomics, and reference standard methodology.
Results:
This systematic review included 14 eligible articles investigating MRI characteristics of BI in meningiomas. Meningiomas with BI exhibited higher volumes of peritumoral edema, irregular tumor shape, incomplete cerebrospinal fluid cleft sign, heterogeneous contrast enhancement, larger sizes, unclear brain-to-tumor interface, and lower mean apparent diffusion coefficient value. The meta-analysis included 12 original studies. The summary area under the curve of MRI for predicting BI in meningiomas was 0.91 (95% confidence interval 0.88-0.93, SE = 0.0165, P = 0.0185), with summary sensitivity and specificity of 0.85 (95% confidence interval 0.81-0.89, P < 0.001) and 0.83 (95% confidence interval 0.76-0.89, P < 0.001), respectively. In subgroup analyses, studies incorporating ≥2 sequences demonstrated superior sensitivity (0.86 vs. 0.82) and specificity (0.85 vs. 0.68)compared with studies incorporating 1 sequence; the addition of apparent diffusion coefficient appeared to further increase diagnostic performance, with summary sensitivity and summary specificity of 0.89 and 0.88, respectively. Studies with a sample size >200 patients had higher sensitivity (0.86 vs. 0.79) and specificity (0.85 vs. 0.76). Studies including brain-to-tumor interface showed better sensitivity (0.89 vs. 0.81) and diagnostic odds ratio (44.3 vs. 20.1), but similar specificity (0.84 vs. 0.83). In addition, studies using radiomics showed better specificity (0.85 vs. 0.80) and diagnostic odds ratio (34.8 vs. 28.9), but exhibited a lower sensitivity (0.83 vs. 0.91).
Conclusions:
MRI demonstrated favorable diagnostic efficacy for prediction of BI in meningiomas. Diagnostic performance of MRI was notably influenced by the specific imaging sequences employed, sample size, and characteristics observed at the brain-to-tumor interface.
