Differentiating Radiation-Induced Necrosis from Recurrent Brain Tumor Using MR Perfusion and Spectroscopy: A Meta-Analysis

Chuang, Ming Tsung; Liu, Yi Sheng; Tsai, Yi Shan; Chen, Ying Chen; Wang, Chien-Kuo

doi:10.1371/journal.pone.0141438

Cited by 132 publications

(141 citation statements)

References 43 publications

Supporting

Mentioning

136

Contrasting

Unclassified

Order By: Relevance

“…However, to date none of these scan types can reliably distinguish radiation necrosis from tumor progression based on lesion appearance (Figure 1). Other special imaging approaches [6; 7] may be useful to distinguish radiation necrosis from tumor progression, but they are not generally available.…”

Section: Introductionmentioning

confidence: 99%

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

et al. 2017

View full text Add to dashboard Cite

Objectives To develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumor progression in brain metastases after Gamma knife radiosurgery. Methods We retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions) or progression (73 lesions), and calculated 285 radiomic features from 4 MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at 2 follow-up time points per lesion per patient. Reproducibility of each feature between the two time points was calculated within each group to identify a subset of features with distinct reproducible values between two groups. Changes in radiomic features from one time point to the next (delta radiomics) were used to build a model to classify necrosis and progression lesions. Results A combination of 5 radiomic features from both T1 post-contrast and T2 MR images were found to be useful in distinguishing necrosis from progression lesions. Delta radiomic features with a RUSBoost ensemble classifier had an overall predictive accuracy of 73.2% and an area under the curve value of 0.73 in leave-one-out cross-validation. Conclusions Delta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumor progression after radiosurgery for brain metastases.

show abstract

Section: Introductionmentioning

confidence: 99%

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

et al. 2017

View full text Add to dashboard Cite

show abstract

“…As techniques develop and more research is conducted, multimodal imaging may become more widely used. Chuang et al [38] previously performed a meta-analysis to differentiate radiation-induced necrosis from recurrent brain tumors by magnetic resonance perfusion and spectroscopy. They included patients with primary brain tumor as well as those with brain metastases.…”

Section: Discussionmentioning

confidence: 99%

The diagnostic performance of perfusion MRI for differentiating glioma recurrence from pseudoprogression

Wan

Wang²,

Tu³

et al. 2017

Medicine

View full text Add to dashboard Cite

Background:The purpose of this meta-analysis was to evaluate the diagnostic accuracy of perfusion magnetic resonance imaging (MRI) as a method for differentiating glioma recurrence from pseudoprogression.Methods:The PubMed, Embase, Cochrane Library, and Chinese Biomedical databases were searched comprehensively for relevant studies up to August 3, 2016 according to specific inclusion and exclusion criteria. The quality of the included studies was assessed according to the quality assessment of diagnostic accuracy studies (QUADAS-2). After performing heterogeneity and threshold effect tests, pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Publication bias was evaluated visually by a funnel plot and quantitatively using Deek funnel plot asymmetry test. The area under the summary receiver operating characteristic curve was calculated to demonstrate the diagnostic performance of perfusion MRI.Results:Eleven studies covering 416 patients and 418 lesions were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.88 (95% confidence interval [CI] 0.84–0.92), 0.77 (95% CI 0.69–0.84), 3.93 (95% CI 2.83–5.46), 0.16 (95% CI 0.11–0.22), and 27.17 (95% CI 14.96–49.35), respectively. The area under the summary receiver operating characteristic curve was 0.8899. There was no notable publication bias. Sensitivity analysis showed that the meta-analysis results were stable and credible.Conclusion:While perfusion MRI is not the ideal diagnostic method for differentiating glioma recurrence from pseudoprogression, it could improve diagnostic accuracy. Therefore, further research on combining perfusion MRI with other imaging modalities is warranted.

show abstract

“…A prospective study using DCE perfusion data found that a plasma–volume ratio ≥ 2.6 identified progression versus radiation treatment effect with sensitivity of 91% and specificity of 80% (Hatzoglou et al, 2015). Although perfusion MRI-based rCBV appears more accurate than MRS (Huang et al, 2011), a meta-analysis has shown that both rCBV and Cho/Cr help differentiate true progression from radiation necrosis (Chuang et al, 2016). …”

Section: Caveats To Interpreting Posttreatment Imaging Changesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

154

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

Differentiating Radiation-Induced Necrosis from Recurrent Brain Tumor Using MR Perfusion and Spectroscopy: A Meta-Analysis

Cited by 132 publications

References 43 publications

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

The diagnostic performance of perfusion MRI for differentiating glioma recurrence from pseudoprogression

Brain metastases: neuroimaging

Contact Info

Product

Resources

About