Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necrosis

Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis

PURPOSE

In this study we gathered and analyzed the available evidence regarding 17 different imaging modalities and performed network meta-analysis to find the most effective modality for the differentiation between brain tumor recurrence and post-treatment radiation effects.

METHODS

We conducted a comprehensive systematic search on PubMed and Embase. The quality of eligible studies was assessed using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument. For each meta-analysis, we recalculated the effect size, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio from the individual study data provided in the original meta-analysis using a random-effects model. Imaging technique comparisons were then assessed using NMA. Ranking was assessed using the multidimensional scaling approach and by visually assessing surface under the cumulative ranking curves.

RESULTS

We identified 32 eligible studies. High confidence in the results was found in only one of them, with a substantial heterogeneity and small study effect in 21% and 9% of included meta-analysis respectively. Comparisons between MRS Cho/NAA, Cho/Cr, DWI, and DSC were most studied. Our analysis showed MRS (Cho/NAA) and 18F-DOPA PET displayed the highest sensitivity and negative likelihood ratios. 18-FET PET was ranked highest among the 17 studied techniques with statistical significance. APT MRI was the only non-nuclear imaging modality to rank higher than DSC, with statistical insignificance, however.

CONCLUSION

The evidence regarding which imaging modality is best for the differentiation between radiation necrosis and post-treatment radiation effects is still inconclusive. Using NMA, our analysis ranked FET PET to be the best for such a task based on the available evidence. APT MRI showed promising results as a non-nuclear alternative.

Apparent Diffusion Coefficient Metrics to Differentiate between Treatment-Related Abnormalities and Tumor Progression in Post-Treatment Glioblastoma Patients: A Retrospective Study

Distinguishing treatment-related abnormalities (TRA) from tumor progression (TP) in glioblastoma patients is a diagnostic imaging challenge due to the identical morphology of conventional MR imaging sequences. Diffusion-weighted imaging (DWI) and its derived images of the apparent diffusion coefficient (ADC) have been suggested as diagnostic tools for this problem. The aim of this study is to determine the diagnostic accuracy of different cut-off values of the ADC to differentiate between TP and TRA. In total, 76 post-treatment glioblastoma patients with new contrast-enhancing lesions were selected. Lesions were segmented using a T1-weighted, contrast-enhanced scan. The mean ADC values of the segmentations were compared between TRA and TP groups. Diagnostic accuracy was compared by use of the area under the curve (AUC) and the derived sensitivity and specificity values from cutoff points. Although ADC values in TP (mean = 1.32 × 10-3 mm2/s; SD = 0.31 × 10-3 mm2/s) were significantly different compared to TRA (mean = 1.53 × 10-3 mm2/s; SD = 0.28 × 10-3 mm2/s) (p = 0.003), considerable overlap in their distributions exists. The AUC of ADC values to distinguish TP from TRA was 0.71, with a sensitivity and specificity of 65% and 70%, respectively, at an ADC value of 1.47 × 10-3 mm2/s. These findings therefore indicate that ADC maps should not be used in discerning between TP and TRA at a certain timepoint without information on temporal evolution.

Effect of Bevacizumab Treatment in Cerebral Radiation Necrosis : Investigation of Response Predictors in a Single-Center Experience

OBJECTIVE

Bevacizumab is a feasible option for treating cerebral radiation necrosis (RN). We investigated the clinical outcome of RN after treatment with bevacizumab and factors related to the initial response and the sustained effect.

METHODS

Clinical data of 45 patients treated for symptomatic RN between September 2019 and February 2021 were retrospectively collected. Bevacizumab (7.5 mg/kg) was administered at 3-week intervals with a maximum four-cycle schedule. Changes in the lesions magnetic resonance image (MRI) scans were examined for the response evaluation. The subgroup analysis was performed based on the initial response and the long-term maintenance of the effect.

RESULTS

Of the 45 patients, 36 patients (80.0%) showed an initial response, and eight patients (17.8%) showed delayed worsening of the corresponding lesion. The non-responders showed a significantly higher incidence of diffusion restriction on MRI than the responders (100.0% vs. 25.0%, p<0.001). The delayed worsening group showed a significantly higher proportion of glioma pathology than the maintenance group (87.5% vs. 28.6%, p=0.005). Cumulative survival rates with sustained effect were significantly higher in the groups with non-glioma pathology (p=0.019) and the absence of diffusion restriction (p<0.001). Pathology of glioma and diffusion restriction in MRI were the independent risk factors for non-response or delayed worsening after initial response.

CONCLUSION

The initial response of RN to bevacizumab was favorable, with improvement in four-fifths of the patients. However, a certain proportion of patients showed non-responsiveness or delayed exacerbations. Bevacizumab may be more effective in treating RN in patients with non-glioma pathology and without diffusion restriction in the MRI.

Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis

BACKGROUND AND PURPOSE

To investigate the feasibility of using a multiapproach analysis combining clinical data, diffusion- and perfusion-weighted imaging, and 3D magnetic resonance spectroscopic imaging to distinguish true tumor progression (TP) from pseudoprogression (PSP) in patients with glioblastoma.

MATERIALS AND METHODS

Progression was suspected within 6 months of radiotherapy in 46 of the 180 patients included in the Phase-III SpectroGlio trial (NCT01507506). Choline/creatine (Cho/Cr), choline/N-acetyl aspartate (Cho/NAA) and lactate/N-acetyl aspartate (Lac/NAA) ratios were extracted. Apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were calculated. ADC, relative CBV values and tumor volume (TV) were collected at relapse. Differences between TP and PSP were evaluated using Mann-Whitney tests, and p values were adjusted with Bonferroni correction.

RESULTS

Patients with suspected progression underwent a new MRI scan 1 month after the first one. Of these, 28 were classified as PSP, and 18 as TP. After a median follow-up of 41 months, median overall survival was higher in PSP than in TP (25.2 vs 20.3 months; p = 0.0092). Lac/NAA and Cho/Cr ratios were higher in TP than in PSP (1.2 vs 0.5; p = 0.006; and 3 vs 2.2; p = 0.021). After multivariate regression analysis, TV was the most significant predictor of TP vs PSP, and the only one retained in the model (p = 0.028).

CONCLUSION

Three spectroscopic ratios could be used to differentiate PSP from TP. TV at relapse was the most predictive factor in the multivariate analysis, and overall survival was higher in PSP than in TP.

Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma

Simple Summary

Glioma is the most common primary malignant tumor of the adult central nervous system. Despite aggressive multimodal treatment, its prognosis remains poor. During follow-up, it remains challenging to distinguish treatment-related changes from tumor progression in treated patients with gliomas due to both share clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions). The early effective identification of tumor progression and treatment-related changes is of great significance for the prognosis and treatment of gliomas. We believe that advanced neuroimaging techniques can provide additional information for distinguishing both at an early stage. In this article, we focus on the research of magnetic resonance imaging technology and artificial intelligence in tumor progression and treatment-related changes. Finally, it provides new ideas and insights for clinical diagnosis.

Abstract

As the most common neuro-epithelial tumors of the central nervous system in adults, gliomas are highly malignant and easy to recurrence, with a dismal prognosis. Imaging studies are indispensable for tracking tumor progression (TP) or treatment-related changes (TRCs). During follow-up, distinguishing TRCs from TP in treated patients with gliomas remains challenging as both share similar clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions) and fulfill criteria for progression. Thus, the early identification of TP and TRCs is of great significance for determining the prognosis and treatment. Histopathological biopsy is currently the gold standard for TP and TRC diagnosis. However, the invasive nature of this technique limits its clinical application. Advanced imaging methods (e.g., diffusion magnetic resonance imaging (MRI), perfusion MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), amide proton transfer (APT) and artificial intelligence (AI)) provide a non-invasive and feasible technical means for identifying of TP and TRCs at an early stage, which have recently become research hotspots. This paper reviews the current research on using the abovementioned advanced imaging methods to identify TP and TRCs of gliomas. First, the review focuses on the pathological changes of the two entities to establish a theoretical basis for imaging identification. Then, it elaborates on the application of different imaging techniques and AI in identifying the two entities. Finally, the current challenges and future prospects of these techniques and methods are discussed.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

Objective

To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments.

Methods

The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging).

Results

The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application.

Conclusion

Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.

Diagnostic accuracy of diffusion-weighted imaging in differentiating glioma recurrence from posttreatment-related changes: a meta-analysis

BACKGROUND

Magnetic resonance imaging (MRI) is the most commonly used imaging method to evaluate glioma recurrence. However, conventional MRI has difficulty distinguishing glioma accurately. This study aimed to explore the value of diffusion weighted imaging (DWI) in evaluating glioma recurrence and post-treatment-related changes.

RESEARCH DESIGN AND METHODS

PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Database and China Science and Technology Journal Database were extensively searched in accordance with inclusion criteria and exclusion criteria to obtain appropriate included studies. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Combined sensitivity and specificity and the area under the summary receiver operating characteristic curve (SROC) with the 95% confidence interval (CI) were calculated.

RESULTS

Seventeen high-quality studies were included. The combined sensitivity was 0.82 (95% CI: 0.76-0.87), the specificity was 0.83 (95% CI: 0.76-0.89), the positive likelihood ratio was 4.9 (95% CI: 3.2-7.5), the negative likelihood ratio was 0.21 (95% CI: 0.15-0.30), the diagnostic odds ratio was 23 (95%: CI 11-48), and the area under the SROC was 0.90 (95% CI: 0.87-0.92).

CONCLUSIONS

This meta-analysis suggests that DWI has high sensitivity, specificity and accuracy in differentiating glioma recurrence.

Qualitative Assessment of Advanced MRI in Post-Treatment High Grade Gliomas Follow Up: Do We Agree?

PURPOSE

MRI is commonly used in follow up of high grade glioma. Our purpose is to assess the interrater agreement on the increasingly used visual qualitative assessment of various conventional and advanced MR techniques in the setting of treated high grade glioma in comparison to the well established quantitative measurements.

METHODS

We prospectively enrolled HGG patients who underwent reresection of a new enhancing lesion on post-treatment 3T MR examination including DWI, DCE and DSC sequences. Two neuroradiologists objectively assessed the diffusion and perfusion maps by placing ROI on representative post-processed maps. They subjectively assessed the post-contrast, perfusion and diffusion sequences. Interrater agreement and concordance correlation coefficient were calculated.

RESULTS

Twenty-eight lesions were included. The interrater agreement on the qualitative assessment was good for k-trans (k = 0.73), moderate for Vp (k = 0.52), fair for AUC and Ve maps (k = 0.37 and 0.21), fair for corrected CBV (k = 0.39) and poor for the enhancement pattern and presence of diffusion restriction (k = 0.02 and 0.07). The concordance between the quantitative measurements was substantial for AUC and Vp (ρc = 0.98 and 0.97), moderate for k-trans and corrected CBV (ρc = 0.94) and poor for Ve and ADC (ρc = 0.86 and 0.24).

CONCLUSION

While the quantitative measurements of DSC and DCE perfusion maps show satisfactory inter-rater agreement, the qualitative assessment has lower interobserver agreement and should not be relied upon solely in the interpretation. Similarly, the suboptimal inter-rater agreement on the interpretation of enhancement pattern and diffusion restriction potentially limits their usefulness in differentiating glioma recurrence from treatment related changes.

Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors

PURPOSE OF REVIEW

This review will explore the latest in advanced imaging techniques, with a focus on the complementary nature of multiparametric, multimodality imaging using magnetic resonance imaging (MRI) and positron emission tomography (PET).

RECENT FINDINGS

Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. PET performed in addition to advanced MRI provides complementary information regarding tumor metabolic properties, particularly when performed simultaneously. 18F-fluoroethyltyrosine (FET) PET improves the specificity of tumor diagnosis and evaluation of post-treatment changes. Incorporation of radiogenomics and machine learning methods further improve advanced imaging. The complementary nature of combining advanced imaging techniques across modalities for brain tumor imaging and incorporating technologies such as radiogenomics has the potential to reshape the landscape in neuro-oncology.

Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN..

Centrally Reduced Diffusion Sign for Differentiation between Treatment-Related Lesions and Glioma Progression: A Validation Study

BACKGROUND AND PURPOSE

Differentiating between treatment-related lesions and tumor progression remains one of the greatest dilemmas in neuro-oncology. Diffusion MR imaging characteristics may provide useful information to help make this distinction. The aim of the study was to assess the diagnostic accuracy of the centrally reduced diffusion sign for differentiation of treatment-related lesions and true tumor progression in patients with suspected glioma recurrence.

MATERIALS AND METHODS

The images of 231 patients who underwent an operation for suspected glioma recurrence were reviewed. Patients with susceptibility artifacts or without central necrosis were excluded. The final diagnosis was established according to histopathology reports. Two neuroradiologists classified the diffusion patterns on preoperative MR imaging as the following: 1) reduced diffusion in the solid component only, 2) reduced diffusion mainly in the solid component, 3) no reduced diffusion, 4) reduced diffusion mainly in the central necrosis, and 5) reduced diffusion in the central necrosis only. Diagnostic accuracy metrics and the area under the receiver operating characteristic curve were estimated for the diffusion patterns.

RESULTS

One hundred three patients were included (22 with treatment-related lesions and 81 with tumor progression). The diagnostic accuracy results for the centrally reduced diffusion pattern as a predictor of treatment-related lesions ("mainly central" and "exclusively central" patterns versus all other patterns) were as follows: 64% sensitivity (95% CI, 41%-83%), 84% specificity (95% CI, 74%-91%), 52% positive predictive value (95% CI, 37%-66%), and 89% negative predictive value (95% CI, 83%-94%).

CONCLUSIONS

The centrally reduced diffusion sign is associated with the presence of treatment effect. The probability of a histologic diagnosis of a treatment-related lesion is low (11%) in the absence of centrally reduced diffusion.

Molecular and Cellular Complexity of Glioma. Focus on Tumour Microenvironment and the Use of Molecular and Imaging Biomarkers to Overcome Treatment Resistance

This review highlights the importance and the complexity of tumour biology and microenvironment in the progression and therapy resistance of glioma. Specific gene mutations, the possible functions of several non-coding microRNAs and the intra-tumour and inter-tumour heterogeneity of cell types contribute to limit the efficacy of the actual therapeutic options. In this scenario, identification of molecular biomarkers of response and the use of multimodal in vivo imaging and in particular the Positron Emission Tomography (PET) based molecular approach, can help identifying glioma features and the modifications occurring during therapy at a regional level. Indeed, a better understanding of tumor heterogeneity and the development of diagnostic procedures can favor the identification of a cluster of patients for personalized medicine in order to improve the survival and their quality of life.

Meta-analysis of the diagnostic performance of diffusion magnetic resonance imaging with apparent diffusion coefficient measurements for differentiating glioma recurrence from pseudoprogression

OBJECTIVE

The accurate differentiation of glioma recurrence from pseudoprogression (PSP) after therapy remains a considerable clinical challenge. Several studies have shown that diffusion magnetic resonance imaging (MRI) has potential value in distinguishing these 2 outcomes. The current meta-analysis examined the diagnostic accuracy of diffusion MRI with the apparent diffusion coefficient (ADC) in the differentiation of glioma recurrence from PSP.

METHOD

PubMed, Embase, Cochrane Library, and Chinese Biomedical databases were reviewed to identify studies that fulfilled our inclusion/exclusion criteria and were published on or before May 5, 2019. Threshold effects; heterogeneity; pooled sensitivity (SENS), specificity, positive likelihood ratio, and negative likelihood ratio; and diagnostic odds ratio were calculated. The overall diagnostic usefulness of diffusion MRI-derived ADC values was assessed by calculating the area under the curve (AUC) following summary receiver operating characteristic (SROC) analysis.

RESULTS

Six eligible studies examined a total of 214 patients. Calculation of pooled values indicated the SENS was 0.95 (95% confidence interval [CI] = 0.89-0.98), specificity was 0.83 (95% CI = 0.72-0.91), positive likelihood ratio was 4.82 (95% CI = 2.93-7.93), negative likelihood ratio was 0.08 (95% CI = 0.04-0.17), and diagnostic odds ratio was 59.63 (95% CI = 22.63-157.37). The SROC AUC was 0.9322. Publication bias was not significant, and SENS analysis indicated the results were relatively stable.

CONCLUSIONS

Our meta-analysis indicated that diffusion MRI with quantitative ADC is an effective approach for differentiation of glioma recurrence from PSP, and can be used as an auxiliary tool to diagnose glioma progression.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study

Purpose

We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.

Materials and Methods

Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results

In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370–1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962–1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778–1.0000)/100%/90%/83.33%/100%, respectively.

Conclusion

Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

82Rubidium chloride PET discrimination of recurrent intracranial malignancy from radiation necrosis

BACKGROUND

Accurate identification and discrimination of post treatment changes from recurrent disease remains a challenge for patients with intracranial malignancies despite advances in molecular and magnetic resonance imaging. We have explored the ability of readily available Rubidium-82 chloride (82RbCl) PET to identify and distinguish progressive intracranial disease from radiation necrosis in patients previously treated with radiation therapy.

METHODS

Six patients with a total of 9 lesions of either primary (n=3) or metastatic (n=6) intracranial malignancies previously treated with stereotactic radiation surgery (SRS) and persistent contrast enhancement on MRI underwent brain 82RbCl PET imaging. Two patients with arteriovenous malformations previously treated with SRS, also had brain 82RbCl PET imaging for a total of 11 lesions studied. Histological confirmation via stereotactic biopsy/excisional resection was obtained for 9 lesions with the remaining 2 classified as either recurrent tumor or radiation necrosis based on subsequent MRI examinations. 82RbCl PET time activity curve analysis was performed which comprised lesion SUVmax, contralateral normal brain SUVmax, and tumor to background ratios (TBmax).

RESULTS

82RbCl demonstrates uptake greater than normal brain parenchyma in all lesions studied. Time activity curves demonstrated progressive uptake of 82RbCl in all lesions without evidence of washout. While recurrent disease demonstrated a greater mean SUVmax compared to radiation necrosis, no statistically significant difference between lesion SUVmax nor TBmax was found (p>0.05).

CONCLUSIONS

82RbCl PET produces high-contrast uptake of both recurrent disease and radiation necrosis compared to normal brain. However, no statistically significant difference was found between recurrent tumor and radiation necrosis.

Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI

BACKGROUND

Following brain cancer treatment, the capacity of anatomical MRI to differentiate neoplastic tissue from treatment-related changes (e.g., pseudoprogression) is limited. This study compared apparent diffusion coefficients (ADC) obtained by diffusion-weighted MRI (DWI) with static and dynamic parameters of O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation of treatment-related changes from tumour progression.

PATIENTS AND METHODS

Forty-eight pretreated high-grade glioma patients with anatomical MRI findings suspicious for progression (median time elapsed since last treatment was 16 weeks) were investigated using DWI and dynamic FET PET. Maximum and mean tumour-to-brain ratios (TBR_max, TBR_mean) as well as dynamic parameters (time-to-peak and slope values) of FET uptake were calculated. For mean ADC calculation, regions-of-interest analyses were performed on ADC maps calculated from DWI coregistered with the contrast-enhanced MR image. Diagnoses were confirmed neuropathologically (21%) or clinicoradiologically. Diagnostic performance was evaluated using receiver-operating-characteristic analyses or Fisher's exact test for a combinational approach.

RESULTS

Ten of 48 patients had treatment-related changes (21%). The diagnostic performance of FET PET was significantly higher (threshold for both TBR_max and TBR_mean, 1.95; accuracy, 83%; AUC, 0.89 ± 0.05; P < 0.001) than that of ADC values (threshold ADC, 1.09 × 10^-3 mm²/s; accuracy, 69%; AUC, 0.73 ± 0.09; P = 0.13). The addition of static FET PET parameters to ADC values increased the latter's accuracy to 89%. The highest accuracy was achieved by combining static and dynamic FET PET parameters (93%). Moreover, in contrast to ADC values, TBRs <1.95 at suspected progression predicted a significantly longer survival (P = 0.01).

CONCLUSIONS

Data suggest that static and dynamic FET PET provide valuable information concerning the differentiation of early treatment-related changes from tumour progression and outperform ADC measurement for this highly relevant clinical question.

Pseudoprogression of brain tumors

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Diagnosis of glioma recurrence using multiparametric dynamic 18F-fluoroethyl-tyrosine PET-MRI

OBJECTIVES

To investigate the value of combined 18F-fluorethyltyrosine-(FET)-PET/MRI for differentiation between recurrence and treatment-related changes in glioma patients.

METHODS

63 lesions suggestive of recurrence in 47 glioma patients were retrospectively identified. All patients had a dynamic FET scan, as well as morphologic MRI, PWI and DWI on a hybrid PET/MRI scanner. Lesions suggestive of recurrence were marked. ROC analysis was performed univariately and on parameter combination.

RESULTS

50 lesions were classified as recurrence, 13 as radiation necrosis. Diagnosis was based on histology in 23 and follow-up imaging in 40 cases. Sensitivities and specificities for static PET were 80 and 85%, 66% and 77% for PWI, 62 and 77% for DWI and 64 and 79% for PET time-to-peak. AUC was 0.86 (p < 0.001) for static PET, 0.73 (p = 0.013) for PWI, 0.70 (p = 0.030) for DWI and 0.73 (p < 0.001) for dynamic PET. Multiparametric analysis resulted in an AUC of 0.89, notably yielding sensitivity of 76% vs. 56% for PET alone at 100% specificity.

CONCLUSION

Simultaneous dynamic FET-PET/MRI was reliably feasible for imaging of recurrent glioma. While all modalities were able to discriminate between recurrence and treatment-related changes, multiparametric analysis added value especially when high specificity was demanded.

Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice

Objectives

At a European Society of Neuroradiology (ESNR) Annual Meeting 2015 workshop, commonalities in practice, current controversies and technical hurdles in glioma MRI were discussed. We aimed to formulate guidance on MRI of glioma and determine its feasibility, by seeking information on glioma imaging practices from the European Neuroradiology community.

Methods

Invitations to a structured survey were emailed to ESNR members (n=1,662) and associates (n=6,400), European national radiologists’ societies and distributed via social media.

Results

Responses were received from 220 institutions (59% academic). Conventional imaging protocols generally include T2w, T2-FLAIR, DWI, and pre- and post-contrast T1w. Perfusion MRI is used widely (85.5%), while spectroscopy seems reserved for specific indications. Reasons for omitting advanced imaging modalities include lack of facility/software, time constraints and no requests. Early postoperative MRI is routinely carried out by 74% within 24–72 h, but only 17% report a percent measure of resection. For follow-up, most sites (60%) issue qualitative reports, while 27% report an assessment according to the RANO criteria. A minority of sites use a reporting template (23%).

Conclusion

Clinical best practice recommendations for glioma imaging assessment are proposed and the current role of advanced MRI modalities in routine use is addressed.

Key Points

• We recommend the EORTC-NBTS protocol as the clinical standard glioma protocol.

• Perfusion MRI is recommended for diagnosis and follow-up of glioma.

• Use of advanced imaging could be promoted with increased education activities.

• Most response assessment is currently performed qualitatively.

• Reporting templates are not widely used, and could facilitate standardisation.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5314-5) contains supplementary material, which is available to authorized users.

Diagnostic Accuracy of Centrally Restricted Diffusion in the Differentiation of Treatment-Related Necrosis from Tumor Recurrence in High-Grade Gliomas

BACKGROUND AND PURPOSE

Centrally restricted diffusion has been demonstrated in recurrent high-grade gliomas treated with bevacizumab. Our purpose was to assess the accuracy of centrally restricted diffusion in the diagnosis of radiation necrosis in high-grade gliomas not treated with bevacizumab.

MATERIALS AND METHODS

In this prospective study, we enrolled patients with high-grade gliomas who developed a new ring-enhancing necrotic lesion and who underwent re-resection. The presence of a centrally restricted diffusion within the ring-enhancing lesion was assessed visually on diffusion trace images and by ADC measurements on 3T preoperative diffusion tensor examination. The percentage of tumor recurrence and radiation necrosis in each surgical specimen was defined histopathologically. The association between centrally restricted diffusion and radiation necrosis was assessed using the Fisher exact test. Differences in ADC and the ADC ratio between the groups were assessed via the Mann-Whitney U test, and receiver operating characteristic curve analysis was performed.

RESULTS

Seventeen patients had re-resected ring-enhancing lesions: 8 cases of radiation necrosis and 9 cases of tumor recurrence. There was significant association between centrally restricted diffusion by visual assessment and radiation necrosis (P = .015) with a sensitivity of 75% and a specificity of 88.9%, a positive predictive value 85.7%, and a negative predictive value of 80% for the diagnosis of radiation necrosis. There was a statistically significant difference in the ADC and ADC ratio between radiation necrosis and tumor recurrence (P = .027).

CONCLUSIONS

The presence of centrally restricted diffusion in a new ring-enhancing lesion might indicate radiation necrosis rather than tumor recurrence in high-grade gliomas previously treated with standard chemoradiation without bevacizumab.

Advanced MRI increases the diagnostic accuracy of recurrent glioblastoma: Single institution thresholds and validation of MR spectroscopy and diffusion weighted MR imaging

The accurate identification of glioblastoma progression remains an unmet clinical need. The aim of this prospective single-institutional study is to determine and validate thresholds for the main metabolite concentrations obtained by MR spectroscopy (MRS) and the values of the apparent diffusion coefficient (ADC) to enable distinguishing tumor recurrence from pseudoprogression. Thirty-nine patients after the standard treatment of a glioblastoma underwent advanced imaging by MRS and ADC at the time of suspected recurrence — median time to progression was 6.7 months. The highest significant sensitivity and specificity to call the glioblastoma recurrence was observed for the total choline (tCho) to total N-acetylaspartate (tNAA) concentration ratio with the threshold ≥ 1.3 (sensitivity 100.0% and specificity 94.7%). The ADCmean value higher than 1313 × 10^− 6 mm²/s was associated with the pseudoprogression (sensitivity 98.3%, specificity 100.0%). The combination of MRS focused on the tCho/tNAA concentration ratio and the ADCmean value represents imaging methods applicable to early non-invasive differentiation between a glioblastoma recurrence and a pseudoprogression. However, the institutional definition and validation of thresholds for differential diagnostics is needed for the elimination of setup errors before implementation of these multimodal imaging techniques into clinical practice, as well as into clinical trials.

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For an effective salvage treatment, an accurate diagnosis of GBM recurrence is essential.

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The standard structural MRI has limited sensitivity and specificity to distinguish GBM progression.

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GBM recurrence is characterized by the ADCmean value ≤ 1313 × 10^− 6 mm²/s and the tCho/tNAA ratio ≥ 1.3.

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An institutional definition of thresholds is needed, if advanced imaging should be used accurately in clinical practice.

Preoperative Grading of Astrocytic Supratentorial Brain Tumors with Diffusion-Weighted Magnetic Resonance Imaging and Apparent Diffusion Coefficient

Background

Diffusion-weighted imaging (DWI) is a form of magnetic resonance imaging (MRI) based on measuring the random Brownian motion of water molecules within the biological tissues and is particularly useful in tumor characterization.

Objectives

The purpose of this study was to evaluate the diagnostic value of DW MRI and the apparent diffusion coefficient (ADC) for preoperative grading of astrocytic supratentorial brain tumors.

Patients and Methods

Twenty-three patients (14 females, 9 males, mean age 43 years) with astrocytic supratentorial brain tumors underwent preoperative conventional MR imaging and DW MRI. The minimum, maximum and mean ADC values and the minimum, maximum and mean DWI signal intensities of each tumor were taken by placing several regions of interest in the tumor on DWI images and ADC maps. To assess the relationship between these values and the tumor grade, we used the Mann-Whitney U test and the Spearman correlation. Receiver operating characteristic (ROC) analysis was used to determine the cutoff value of the minimum, maximum and mean ADC values and the minimum, maximum and mean DWI signal intensities that had the best composition of sensitivity and specificity for differentiating low-grade and high-grade astrocytic brain tumors.

Results

According to the pathology reports, 10 patients had low-grade astrocytomas (grades I, II) and 13 patients had high-grade astrocytomas (grades III, IV). The minimum ADC value showed a significantly inverse correlation with astrocytic tumor grade (P = 0.006). The correlation between the maximum ADC value and the maximum DWI signal intensity with tumor grade was direct (P = 0.013, P = 0.035). According to the ROC analysis, the cutoff values of 0.843 × 10^-3 mm²/s, 2.117 × 10^-3 mm²/s and 165.2 for the minimum ADC, maximum ADC and maximum DWI respectively, obtained the best combination of sensitivity and specificity for distinguishing low-grade and high-grade astrocytomas.

Conclusion

Measuring minimum ADC, maximum ADC and maximum DWI signal intensity can provide valuable information for grading of astrocytic supratentorial brain tumors before surgery.

A Gamma-Knife-Enabled Mouse Model of Cerebral Single-Hemisphere Delayed Radiation Necrosis

Purpose

To develop a Gamma Knife-based mouse model of late time-to-onset, cerebral radiation necrosis (RN) with serial evaluation by magnetic resonance imaging (MRI) and histology.

Methods and Materials

Mice were irradiated with the Leksell Gamma Knife^® (GK) Perfexion^TM (Elekta AB; Stockholm, Sweden) with total single-hemispheric radiation doses (TRD) of 45- to 60-Gy, delivered in one to three fractions. RN was measured using T2-weighted MR images, while confirmation of tissue damage was assessed histologically by hematoxylin & eosin, trichrome, and PTAH staining.

Results

MRI measurements demonstrate that TRD is a more important determinant of both time-to-onset and progression of RN than fractionation. The development of RN is significantly slower in mice irradiated with 45-Gy than 50- or 60-Gy, where RN development is similar. Irradiated mouse brains demonstrate all of the pathologic features observed clinically in patients with confirmed RN. A semi-quantitative (0 to 3) histologic grading system, capturing both the extent and severity of injury, is described and illustrated. Tissue damage, as assessed by a histologic score, correlates well with total necrotic volume measured by MRI (correlation coefficient = 0.948, with p<0.0001), and with post-irradiation time (correlation coefficient = 0.508, with p<0.0001).

Conclusions

Following GK irradiation, mice develop late time-to-onset cerebral RN histology mirroring clinical observations. MR imaging provides reliable quantification of the necrotic volume that correlates well with histologic score. This mouse model of RN will provide a platform for mechanism of action studies, the identification of imaging biomarkers of RN, and the development of clinical studies for improved mitigation and neuroprotection.