Recurrent glioblastoma multiforme versus radiation injury: a multiparametric 3-T MR approach

Costanzo, Alfonso Di; Scarabino, Tommaso; Trojsi, Francesca; Popolizio, Teresa; Bonavita, Simona; Cristofaro, M. De; Conforti, Renata; Cristofano, Adriana; Colonnese, Claudio; Salvolini, U.; Tedeschi, Gioacchino

doi:10.1007/s11547-013-0371-y

Cited by 57 publications

(54 citation statements)

References 33 publications

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“…The majority focuses mainly on overall survival, true progression vs. pseudo-progression or true progression vs. radiation injury. Some recent examples are the following Elson et al (2015) show using DKI data from 52 patients that Apparent Diffusion Coefficient (ADC)–values strongly correlate to overall survival Smets et al (2013) conclude on 24 patients that absence of contrast enhancement on immediately post-operative T1pc correlates to an increase in overall survival Zhang et al (2016) developed a new feature selection method using DKI data from 79 patients which gives an area under the receiver operating characteristic (ROC) curve (AUC) of 0.86 for separating true from pseudo-progression, without any manual segmentation; Bulik et al (2015) found significant differences in ADC and spectroscopic metabolites values between patients with true and pseudo-progression Di Costanzo et al (2014) show, using data from 29 patients, that LDA trained on ADC, CBV, and normalized Choline gives a 96.6% accuracy in differentiating patients with true progression vs. radiation injury Khalifa et al (2016) show that the fraction of hypoperfused tumor volume gives a 79.2% accuracy in anticipating tumor relapse at the next follow-up point.…”

Section: Discussionmentioning

confidence: 99%

Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features

et al. 2017

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Purpose: The purpose of this paper is discriminating between tumor progression and response to treatment based on follow-up multi-parametric magnetic resonance imaging (MRI) data retrieved from glioblastoma multiforme (GBM) patients.Materials and Methods: Multi-parametric MRI data consisting of conventional MRI (cMRI) and advanced MRI [i.e., perfusion weighted MRI (PWI) and diffusion kurtosis MRI (DKI)] were acquired from 29 GBM patients treated with adjuvant therapy after surgery. We propose an automatic pipeline for processing advanced MRI data and extracting intensity-based histogram features and 3-D texture features using manually and semi-manually delineated regions of interest (ROIs). Classifiers are trained using a leave-one-patient-out cross validation scheme on complete MRI data. Balanced accuracy rate (BAR)–values are computed and compared between different ROIs, MR modalities, and classifiers, using non-parametric multiple comparison tests.Results: Maximum BAR–values using manual delineations are 0.956, 0.85, 0.879, and 0.932, for cMRI, PWI, DKI, and all three MRI modalities combined, respectively. Maximum BAR–values using semi-manual delineations are 0.932, 0.894, 0.885, and 0.947, for cMRI, PWI, DKI, and all three MR modalities combined, respectively. After statistical testing using Kruskal-Wallis and post-hoc Dunn-Šidák analysis we conclude that training a RUSBoost classifier on features extracted using semi-manual delineations on cMRI or on all MRI modalities combined performs best.Conclusions: We present two main conclusions: (1) using T1 post-contrast (T1pc) features extracted from manual total delineations, AdaBoost achieves the highest BAR–value, 0.956; (2) using T1pc-average, T1pc-90th percentile, and Cerebral Blood Volume (CBV) 90th percentile extracted from semi-manually delineated contrast enhancing ROIs, SVM-rbf, and RUSBoost achieve BAR–values of 0.947 and 0.932, respectively. Our findings show that AdaBoost, SVM-rbf, and RUSBoost trained on T1pc and CBV features can differentiate progressive from responsive GBM patients with very high accuracy.

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Section: Discussionmentioning

confidence: 99%

Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features

et al. 2017

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“…Recent developments in imaging technology have made it possible to monitor tumors at the metabolite and microvascular levels. Single-photon emission computed tomography (SPECT), [6] positron emission tomography (PET), [7,8] perfusion CT, [9] diffusion MRI, [10,11] perfusion MRI, and magnetic resonance spectroscopy (MRS) [12,13] are the imaging modalities in the clinical setting.…”

Section: Introductionmentioning

confidence: 99%

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

Wan

Wang²,

Tu³

et al. 2017

Medicine

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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.

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“…The contemporaneous use of MRP in conjunction with MR for evaluation of tumor progression in high-grade gliomas has been well characterized in the literature3,8,10,11,16,21,23). The sensitivity and specificity of MRP to predict histological tumor progression in conjunction with MR has been approximately 80% and 77% respectively24).…”

Section: Discussionmentioning

confidence: 99%

The Use of MR Perfusion Imaging in the Evaluation of Tumor Progression in Gliomas

Snelling

Shah

Buttrick

et al. 2017

J Korean Neurosurg Soc

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ObjectiveDiagnosing tumor progression and pseudoprogression remains challenging for many clinicians. Accurate recognition of these findings remains paramount given necessity of prompt treatment. However, no consensus has been reached on the optimal technique to discriminate tumor progression. We sought to investigate the role of magnetic resonance perfusion (MRP) to evaluate tumor progression in glioma patients.MethodsAn institutional retrospective review of glioma patients undergoing MRP with concurrent clinical follow up visit was performed. MRP was evaluated in its ability to predict tumor progression, defined clinically or radiographically, at concurrent clinical visit and at follow up visit. The data was then analyzed based on glioma grade and subtype.ResustsA total of 337 scans and associated clinical visits were reviewed from 64 patients. Sensitivity, specificity, positive and negative predictive value were reported for each tumor subtype and grade. The sensitivity and specificity for high-grade glioma were 60.8% and 87.8% respectively, compared to low-grade glioma which were 85.7% and 89.0% respectively. The value of MRP to assess future tumor progression within 90 days was 46.9% (sensitivity) and 85.0% (specificity).ConclusionBased on our retrospective review, we concluded that adjunct imaging modalities such as MRP are necessary to help diagnose clinical disease progression. However, there is no clear role for stand-alone surveillance MRP imaging in glioma patients especially to predict future tumor progression. It is best used as an adjunctive measure in patients in whom progression is suspected either clinically or radiographically.

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Recurrent glioblastoma multiforme versus radiation injury: a multiparametric 3-T MR approach

Abstract: The multiparametric 3-T MR assessment based on (1)H-MRSI, DWI and PWI in addition to MRI is a useful tool to discriminate tumour recurrence/progression from radiation effects.

Cited by 57 publications

References 33 publications

Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features

Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features

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

The Use of MR Perfusion Imaging in the Evaluation of Tumor Progression in Gliomas

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