Differentiation of Glioblastoma from Brain Metastasis: Qualitative and Quantitative Analysis Using Arterial Spin Labeling MR Imaging

Sunwoo, Leonard; Tie, Yun; You, Sung Hyun; Yoo, Roh Eul; Kang, Koung Mi; Choi, Seung Hong; Kim, Ji Hoon; Sohn, Chul‐Ho; Park, Sun Won; Jung, Cheolkyu; Park, Chul‐Kee

doi:10.1371/journal.pone.0166662

Cited by 60 publications

(42 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Glioblastoma-normalized CBF values were higher both in the enhancing tumors and in the peritumoral area of T2 signal change than those of metastatic tumors. Peritumoral CBF data provided the best discrimination between glioblastoma and metastases with an AUC of 0.54 ( p < 0.001) (Sunwoo et al, 2016). …”

Section: Differentiating Metastases From Similar-appearing Massesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

155

130

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.

show abstract

Section: Differentiating Metastases From Similar-appearing Massesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

155

130

View full text Add to dashboard Cite

show abstract

“…Both qualitatively and quantitatively, glioblastomas demonstrated higher intratumoral and peritumoral rCBF than metastases. They report an area under the curve (AUC) of 0.835 for differentiating the two with peritumoral rCBF (55).…”

Section: Differentiation Of Malignancy Typesmentioning

confidence: 99%

Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response

2020

View full text Add to dashboard Cite

Early detection of brain metastases and differentiation from other neuropathologies is crucial. Although biopsy is often required for definitive diagnosis, imaging can provide useful information. After treatment commences, imaging is also performed to assess the efficacy of treatment. Contrast-enhanced magnetic resonance imaging (MRI) is the traditional imaging method for the evaluation of brain metastases, as it provides information about lesion size, morphology, and macroscopic properties. Newer MRI sequences have been developed to increase the conspicuity of detecting enhancing metastases. Other advanced MRI techniques, that have the capability to probe beyond the anatomic structure, are available to characterize microstructures, cellularity, physiology, perfusion, and metabolism. Artificial intelligence provides powerful computational tools for detection, segmentation, classification, prediction, and prognosis. We highlight and review a few advanced MRI techniques for the assessment of brain metastases-specifically for (1) diagnosis, including differentiating between malignancy types and (2) evaluation of treatment response, including the differentiation between radiation necrosis and disease progression.

show abstract

“…Several studies have reported that related cerebral blood flow (rCBF) in HGG was significantly higher than in metastasis ( P < .05), [13–15] While previous reports demonstrated that rCBF values may not be sufficient for discriminating metastatic tumors from HGG. [16,17] Meta-analysis has been recognized as an effective method to answer a wide variety of clinical questions by summarizing and reviewing previously published quantitative research.…”

Section: Introductionmentioning

confidence: 99%

Based on arterial spin labeling helps to differentiate high-grade gliomas from brain solitary metastasis

Han

Feng

et al. 2019

Medicine

View full text Add to dashboard Cite

Background: We first introduced this meta-analysis was to assess the accuracy of arterial spin labeling (ASL) in the differentiating high-grade gliomas (HGG) from brain solitary metastases (BSM). Methods: The PubMed, Web of Knowledge, and Cochrane Libraries and China National Knowledge Infrastructure databases were searched up to August 31, 2018. The pooled weighted sensitivity and specificity, summary receiver operating characteristic curve (SROC), sensitivity analysis, and threshold effect analysis were performed on Stata version 12.0 and Meta-Disc version 1.4. Deeks’ funnel plot asymmetry test was performed to assess publication bias. Results: Of 5 eligible articles, of the 346 lesions from 346 patients, 274 were HGG, and 72 were BSM. The forest pooled sensitivity of 0.88 (95% confidence interval [CI]: 0.65, 0.96) and specificity of 0.85 (95% CI: 0.74, 0.92) of ASL were reported in this meta-analysis. The pooled area under the curve of SROC was 0.92 (95% CI: 0.89, 0.94). Sensitivity analysis demonstrated that the pooled estimates were reliable. No evident publication bias was obtained ( P = .38). Conclusion: The parameters derived from ASL with high accuracy in differentiating HGG from BSM. However, results must be interpreted with caution due to the small sample size considered. Large sample prospective studies were necessary to assess and confirm its clinical value.

show abstract

Differentiation of Glioblastoma from Brain Metastasis: Qualitative and Quantitative Analysis Using Arterial Spin Labeling MR Imaging

Cited by 60 publications

References 25 publications

Brain metastases: neuroimaging

Brain metastases: neuroimaging

Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response

Based on arterial spin labeling helps to differentiate high-grade gliomas from brain solitary metastasis

Contact Info

Product

Resources

About