MRI perfusion in determining pseudoprogression in patients with glioblastoma

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

Multiparametric Analysis Combining DSC-MR Perfusion and [18F]FET-PET is Superior to a Single Parameter Approach for Differentiation of Progressive Glioma from Radiation Necrosis

PURPOSE

Perfusion-weighted (PWI) magnetic resonance imaging (MRI) and O‑(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) positron emission tomography (PET) are both useful for discrimination of progressive disease (PD) from radiation necrosis (RN) in patients with gliomas. Previous literature showed that the combined use of FET-PET and MRI-PWI is advantageous; hhowever the increased diagnostic performances were only modest compared to the use of a single modality. Hence, the goal of this study was to further explore the benefit of combining MRI-PWI and [18F]FET-PET for differentiation of PD from RN. Secondarily, we evaluated the usefulness of cerebral blood flow (CBF), mean transit time (MTT) and time to peak (TTP) as previous studies mainly examined cerebral blood volume (CBV).

METHODS

In this single center study, we retrospectively identified patients with WHO grades II-IV gliomas with suspected tumor recurrence, presenting with ambiguous findings on structural MRI. For differentiation of PD from RN we used both MRI-PWI and [18F]FET-PET. Dynamic susceptibility contrast MRI-PWI provided normalized parameters derived from perfusion maps (r(relative)CBV, rCBF, rMTT, rTTP). Static [18F]FET-PET parameters including mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated. Based on histopathology and radioclinical follow-up we diagnosed PD in 27 and RN in 10 cases. Using the receiver operating characteristic (ROC) analysis, area under the curve (AUC) values were calculated for single and multiparametric models. The performances of single and multiparametric approaches were assessed with analysis of variance and cross-validation.

RESULTS

After application of inclusion and exclusion criteria, we included 37 patients in this study. Regarding the in-sample based approach, in single parameter analysis rTBRmean (AUC = 0.91, p < 0.001), rTBRmax (AUC = 0.89, p < 0.001), rTTP (AUC = 0.87, p < 0.001) and rCBVmean (AUC = 0.84, p < 0.001) were efficacious for discrimination of PD from RN. The rCBFmean and rMTT did not reach statistical significance. A classification model consisting of TBRmean, rCBVmean and rTTP achieved an AUC of 0.98 (p < 0.001), outperforming the use of rTBRmean alone, which was the single parametric approach with the highest AUC. Analysis of variance confirmed the superiority of the multiparametric approach over the single parameter one (p = 0.002). While cross-validation attributed the highest AUC value to the model consisting of TBRmean and rCBVmean, it also suggested that the addition of rTTP resulted in the highest accuracy. Overall, multiparametric models performed better than single parameter ones.

CONCLUSION

A multiparametric MRI-PWI and [18F]FET-PET model consisting of TBRmean, rCBVmean and PWI rTTP significantly outperformed the use of rTBRmean alone, which was the best single parameter approach. Secondarily, we firstly report the potential usefulness of PWI rTTP for discrimination of PD from RN in patients with glioma; however, for validation of our findings the prospective studies with larger patient samples are necessary.

Evaluating the Efficacy of Perfusion MRI and Conventional MRI in Distinguishing Recurrent Cerebral Metastasis from Brain Radiation Necrosis

Differentiating recurrent cerebral metastasis (CM) from brain radiation necrosis (BRN) is pivotal for guiding appropriate treatment and prognostication. Despite advances in imaging techniques, however, accurately distinguishing these conditions non-invasively is still challenging. This single-center retrospective study reviewed 32 cases (28 patients) with confirmed cerebral metastases who underwent surgical excision of lesions initially diagnosed by MRI and/or MR perfusion scans from 1 January 2015 to 30 September 2020. Diagnostic accuracy was assessed by comparing imaging findings with postoperative histopathology. Conventional MRI accurately identified recurrent CM in 75% of cases. MR perfusion scans showed significantly higher mean maximum relative cerebral blood volume (max. rCBV) in metastasis cases, indicating its potential as a discriminative biomarker. No single imaging modality could definitively distinguish CM from BRN. Survival analysis revealed gender as the only significant factor affecting overall survival, with no significant survival difference observed between patients with CM and BRN after controlling for confounding factors. This study underscores the limitations of both conventional MRI and MR perfusion scans in differentiating recurrent CM from BRN. Histopathological examination remains essential for accurate diagnosis. Further research is needed to improve the reliability of non-invasive imaging and to guide the management of patients with these post-radiation events.

Low rCBV values in glioblastoma tumor progression under chemoradiotherapy

PURPOSE

After standard treatment for glioblastoma, perfusion MRI remains challenging for differentiating tumor progression from post-treatment changes. Our objectives were (1) to correlate rCBV values at diagnosis and at first tumor progression and (2) to analyze the relationship of rCBV values at tumor recurrence with enhancing volume, localization of tumor progression, and time elapsed since the end of radiotherapy in tumor recurrence.

METHODS

Inclusion criteria were (1) age > 18 years, (2) histologically confirmed glioblastoma treated with STUPP regimen, and (3) tumor progression according to RANO criteria > 12 weeks after radiotherapy. Co-registration of segmented enhancing tumor VOIs with dynamic susceptibility contrast perfusion MRI was performed using Olea Sphere software. For tumor recurrence, we correlated rCBV values with enhancing tumor volume, with recurrence localization, and with time elapsed from the end of radiotherapy to progression. Analyses were performed with SPSS software.

RESULTS

Sixty-four patients with glioblastoma were included in the study. Changes in rCBV values between diagnosis and first tumor progression were significant (p < 0.001), with a mean and median decreases of 32% and 46%, respectively. Mean rCBV values were also different (p < 0.01) when tumors progressed distally (radiation field rCBV values of 1.679 versus 3.409 distally). However, changes and, therefore, low rCBV values after radiotherapy in tumor recurrence were independent of time.

CONCLUSION

Chemoradiation alters tumor perfusion and rCBV values may be decreased in the setting of tumor progression. Changes in rCBV values with respect to diagnosis, with low rCBV in tumor progression, are independent of time but related to the site of recurrence.

Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging in Differentiation Between Recurrence and Pseudoprogression in High-Grade Glioma: A Meta-analysis

INTRODUCTION

In glioma patients that have undergone surgical tumor resection, the ability to reliably distinguish between pseudoprogression (PsP) and a recurrent tumor (RT) is of key clinical importance. Accordingly, this meta-analysis evaluated the utility of dynamic susceptibility contrast-enhanced perfusion-weighted imaging as a means of distinguishing between PsP and RT when analyzing patients with high-grade glioma.

MATERIALS AND METHODS

The PubMed, Web of Science, and Wanfang databases were searched for relevant studies. Pooled analyses of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) values were conducted, after which the area under the curve (AUC) for summary receiver operating characteristic curves was computed.

RESULTS

This meta-analysis ultimately included 21 studies enrolling 879 patients with 888 lesions. Cerebral blood volume-associated diagnostic results were reported in 20 of the analyzed studies, and the respective pooled sensitivity, specificity, PLR, and NLR values were 86% (95% confidence interval [CI], 0.81-0.89), 83% (95% CI, 0.77-0.87), 4.94 (95% CI, 3.61-6.75), and 0.18 (95% CI, 0.13-0.23) for these 20 studies. The corresponding AUC value was 0.91 (95% CI, 0.88-0.93), and the publication bias risk was low ( P = 0.976). Cerebral blood flow-related diagnostic results were additionally reported in 6 of the analyzed studies, with respective pooled sensitivity, specificity, PLR, and NLR values of 85% (95% CI, 0.78-0.90), 85% (95% CI, 0.76-0.91), 5.54 (95% CI, 3.40-9.01), and 0.18 (95% CI, 0.12-0.26). The corresponding AUC value was 0.92 (95% CI, 0.89-0.94), and the publication bias risk was low ( P = 0.373).

CONCLUSIONS

The present meta-analysis results suggest that dynamic susceptibility contrast-enhanced perfusion-weighted imaging represents an effective diagnostic approach to distinguishing between PsP and RT in high-grade glioma patients.

Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors

This study delineates the pivotal role of imaging within the field of neurology, emphasizing its significance in the diagnosis, prognostication, and evaluation of treatment responses for central nervous system (CNS) tumors. A comprehensive understanding of both the capabilities and limitations inherent in emerging imaging technologies is imperative for delivering a heightened level of personalized care to individuals with neuro-oncological conditions. Ongoing research in neuro-oncological imaging endeavors to rectify some limitations of radiological modalities, aiming to augment accuracy and efficacy in the management of brain tumors. This review is dedicated to the comparison and critical examination of the latest advancements in diverse imaging modalities employed in neuro-oncology. The objective is to investigate their respective impacts on diagnosis, cancer staging, prognosis, and post-treatment monitoring. By providing a comprehensive analysis of these modalities, this review aims to contribute to the collective knowledge in the field, fostering an informed approach to neuro-oncological care. In conclusion, the outlook for neuro-oncological imaging appears promising, and sustained exploration in this domain is anticipated to yield further breakthroughs, ultimately enhancing outcomes for individuals grappling with CNS tumors.

Highlighted Advances in Therapies for Difficult-To-Treat Brain Tumours Such as Glioblastoma

Glioblastoma multiforme (GBM) remains a challenging disease, as it is the most common and deadly brain tumour in adults and has no curative solution and an overall short survival time. This incurability and short survival time means that, despite its rarity (average incidence of 3.2 per 100,000 persons), there has been an increased effort to try to treat this disease. Standard of care in newly diagnosed glioblastoma is maximal tumour resection followed by initial concomitant radiotherapy and temozolomide (TMZ) and then further chemotherapy with TMZ. Imaging techniques are key not only to diagnose the extent of the affected tissue but also for surgery planning and even for intraoperative use. Eligible patients may combine TMZ with tumour treating fields (TTF) therapy, which delivers low-intensity and intermediate-frequency electric fields to arrest tumour growth. Nonetheless, the blood–brain barrier (BBB) and systemic side effects are obstacles to successful chemotherapy in GBM; thus, more targeted, custom therapies such as immunotherapy and nanotechnological drug delivery systems have been undergoing research with varying degrees of success. This review proposes an overview of the pathophysiology, possible treatments, and the most (not all) representative examples of the latest advancements.

The efficacy and adverse effects of anlotinib in the treatment of high-grade glioma: A retrospective analysis

Introduction

Anlotinib, a novel multi-kinase inhibitor, was found to improve progression-free survival (PFS) in brain metastases.

Methods

This paper retrospectively analyzed 26 newly diagnosed or recurrent high-grade gliomas from 2017 to 2022, and the patients received oral anlotinib during concurrent postoperative chemoradiotherapy or after recurrence. Efficacy was evaluated according to the Response Assessment in Neuro-Oncology (RANO) criteria, and the main study endpoints were PFS at 6 months and overall survival (OS) at 1 year.

Results

After the follow-up, until May 2022, 13 patients survived and 13 patients died, with a median follow-up time of 25.6 months. The disease control rate (DCR) was 96.2% (25/26), and the overall response rate (ORR) rate was 73.1% (19/26). The median PFS after oral anlotinib was 8.9 months (0.8-15.1), and the PFS at 6 months was 72.5%. The median OS after oral anlotinib was 12 months (1.6-24.4), and the OS at 12 months was 42.6%. Anlotinib-related toxicities were observed in 11 patients, mostly grades 1-2. In the multivariate analysis, patients with Karnofsky Performance Scale (KPS) above 80 had a highermedian PFS of 9.9months (p = 0.02), and their sex, age, IDH mutation, MGMTmethylation, and whether anlotinib was combined with chemoradiotherapy or maintenance treatment had no effect on PFS.

Conclusion

We found that anlotinib combined with chemoradiotherapy in treating high-grade central nervous system (CNS) tumors can prolong PFS and OS and that it was safe.

Challenges and opportunities for advanced neuroimaging of glioblastoma

Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.

Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging in Differentiating Recurrence From Radiation Injury in Postoperative Glioma: A Meta-analysis

PURPOSE

It is important to differentiate between radiation injury (RI) and tumor recurrence (TR) in patients with glioma after surgery and radiotherapy. Our objective was to evaluate the use of dynamic susceptibility contrast-enhanced perfusion-weighted imaging to distinguish between TR and RI in patients with glioma.

METHODS

Relevant studies published until October 2021 were identified in the PubMed, Embase, and Cochrane Library databases. Stata v12.0 and RevMan 5.3 were used for meta-analysis.

RESULTS

In total, the meta-analysis incorporated 13 retrospective studies that included 513 patients with 522 lesions. Among the 522 lesions, 329 lesions were TRs and 193 lesions were RIs. The pooled relative cerebral blood volume value was significantly greater in the TR group (P < 0.00001) with significant heterogeneity (I2 = 88%). The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 83% (95 confidence interval [CI], 77%-88%), 85% (95 CI, 77%-91%), 5.60 (95 CI, 3.61-8.70), and 0.20 (95% CI, 0.14-0.27), respectively. The heterogeneity of sensitivity (I2 = 33.18%), specificity (I2 = 24.01%), PLR (I2 = 0.00%), and NLR (I2 = 6.68%) is not significant. The area under the receiver operating characteristic curve was 0.91 (95% CI, 0.88-0.93). The 3.0 T magnetic resonance imaging, high-grade glioma, and Europe/America patient subgroups showed PLR greater than 5 and NLR less than 0.2. There was no significant indication of publication bias in the analysis (P = 0.496).

CONCLUSIONS

It is concluded that dynamic susceptibility contrast-enhanced perfusion-weighted imaging is effective for the diagnostic differentiation between TR and RI in patients with glioma.

Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis

High-grade gliomas remain the most common primary brain tumour with limited treatments options and early recurrence rates following adjuvant treatments. However, differentiating true tumour progression (TTP) from treatment-related effects or pseudoprogression (PsP), may critically influence subsequent management options. Structural MRI is routinely employed to evaluate treatment responses, but misdiagnosis of TTP or PsP may lead to continuation of ineffective or premature cessation of effective treatments, respectively. A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses method. Embase, MEDLINE, Web of Science and Google Scholar were searched for methods applied to differentiate PsP and TTP, and studies were selected using pre-specified eligibility criteria. The sensitivity and specificity of included studies were summarised. Three of the identified methods were compared in a separate subgroup meta-analysis. Thirty studies assessing seven distinct neuroimaging methods in 1372 patients were included in the systematic review. The highest performing methods in the subgroup analysis were DWI (AUC = 0.93 [0.91-0.95]) and DSC-MRI (AUC = 0.93 [0.90-0.95]), compared to DCE-MRI (AUC = 0.90 [0.87-0.93]). 18F-fluoroethyltyrosine PET (18F-FET PET) and amide proton transfer-weighted MRI (APTw-MRI) also showed high diagnostic accuracy, but results were based on few low-powered studies. Both DWI and DSC-MRI performed with high sensitivity and specificity for differentiating PsP from TTP. Considering the technical parameters and feasibility of each identified method, the authors suggested that, at present, DSC-MRI technique holds the most clinical potential.

Reliability of dynamic susceptibility contrast perfusion metrics in pre- and post-treatment glioma

Background

In neuro-oncology, dynamic susceptibility contrast magnetic resonance (DSC-MR) perfusion imaging emerged as a tool to aid in the diagnostic work-up and to surveil effectiveness of treatment. However, it is believed that a significant variability exists with regard to the measured in DSC-MR perfusion parameters. The aim of this study was to assess the observer variability in measured DSC-MR perfusion parameters in patients before and after treatment. In addition, we investigated whether region-of-interest (ROI) shape impacted the observer variability.

Materials and methods

Twenty non-treated patients and a matched group of twenty patients post-treatment (neurosurgical resection and post-chemoradiotherapy) were included. Six ROIs were independently placed by three readers: circular ROIs and polygonal ROIs covering 1) the tumor hotspot; 2) the peritumoral region (T2/FLAIR-hyperintense region) and 3) the whole tumor region. A two-way random Intra-class coefficient (ICC) model was used to assess variability in measured DSC-MRI perfusion parameters. The perfusion metrics as assessed by the circular and the polygonal ROI were compared by use of the dependent T-test.

Results

In the non-treated group, circular ROIs showed good–excellent overlap (ICC-values ranging from 0.741–0.963) with the exception of those representing the tumor hotspot. Polygonal ROIs showed lower ICC-values, ranging from 0.113 till 0.856. ROI-placement in the posttreatment group showed to be highly variable with a significant deterioration of ICC-values. Furthermore, perfusion metric assessment in similar tumor regions was not impacted by ROI shape.

Discussion

This study shows that posttreatment quantitative interpretation of DSC-MR perfusion imaging is highly variable and should be carried out with precaution. Pretreatment assessment of DSC-MR images, however, could be carried out be a single reader in order to provide valid data for further analyses.

• DSC-MR perfusion imaging measurements in non-treated glioma is highly reliable between readers, even readers with little experience.

• DSC-MR perfusion imaging measurements in treated glioma is show to be inconsistent between readers.

• When using DSC-MR perfusion imaging as a quantitative surveillance tool for the recurrence of glioma after treatment, double-reading should be preferred.

Volumetric Measurement of Relative CBV Using T1-Perfusion-Weighted MRI with High Temporal Resolution Compared with Traditional T2*-Perfusion-Weighted MRI in Postoperative Patients with High-Grade Gliomas

BACKGROUND AND PURPOSE

T1-PWI with high temporal resolution may provide a reliable relative CBV value as a valid alternative to T2*-PWI under increased susceptibility. The purpose of this study was to assess the technical and clinical performance of T1-relative CBV in patients with postoperative high-grade gliomas.

MATERIALS AND METHODS

Forty-five MRIs of 34 patients with proved high-grade gliomas were included. In all MRIs, T1- and T2*-PWIs were both acquired and processed semiautomatically to generate relative CBV maps using a released commercial software. Lesion masks were overlaid on the relative CBV maps, followed by a histogram of the whole VOI. The intraclass correlation coefficient and Bland-Altman plots were used for quantitative and qualitative comparisons. Signal loss from both methods was compared using the Wilcoxon signed-rank test of zero voxel percentage. The MRIs were divided into a progression group (n = 20) and a nonprogression group (n = 14) for receiver operating characteristic curve analysis.

RESULTS

Fair intertechnique consistency was observed between the 90th percentiles of the T1- and T2*-relative CBV values (intraclass correlation coefficient = 0.558, P < .001). T2*-PWI revealed a significantly higher percentage of near-zero voxels than T1-PWI (17.7% versus 3.1%, P < .001). There was no statistically significant difference between the area under the curve of T1- and T2*-relative CBV (0.811 versus 0.793, P = .835). T1-relative CBV showed 100% sensitivity and 57.1% specificity for the detection of progressive lesions.

CONCLUSIONS

T1-relative CBV demonstrated exquisite diagnostic performance for detecting progressive lesions in postoperative patients with high-grade gliomas, suggesting the potential role of T1-PWI as a valid alternative to the traditional T2*-PWI.

Dynamic Susceptibility Contrast MRI May Contribute in Prediction of Stereotactic Radiosurgery Outcome in Brain Metastases

Background

Following stereotactic radiosurgery (SRS), predicting treatment response is not possible at an early stage using structural imaging alone. Hence, the current study aims at investigating whether dynamic susceptibility contrast (DSC)-MRI estimated prior to SRS can provide predictive biomarkers in response to SRS treatment and characterize vascular characteristics of pseudo-progression.

Methods

In this retrospective study, perfusion-weighted DSC-MRI image data acquired with a temporal resolution of 1.45 seconds were collected from 41 patients suffering from brain metastases. Outcome was defined based on lesion volume changes in time (determined on structural images) or death. Motion correction and manual lesion delineation were performed prior to semi-automated, voxel-wise perfusion analysis. Statistical testing was performed using linear regression and a significance threshold at P = .05. Age, sex, primary cancers (pulmonary cancer and melanoma), lesion volume, and dichotomized survival time were added as covariates in the linear regression models (ANOVA).

Results

Relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) were found to be significantly lower prior to SRS treatment in patients with increasing lesion volume or early death post-SRS (P ≤ .01).

Conclusion

Unfavorable treatment outcome may be linked to low perfusion prior to SRS. Pseudo-progression may be preceded by a transient rCBF increase post-SRS. However, results should be verified in different or larger patient material.

Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions

In the follow-up treatment of high-grade gliomas (HGGs), differentiating true tumor progression from treatment-related effects, such as pseudoprogression and radiation necrosis, presents an ongoing clinical challenge. Conventional MRI with and without intravenous contrast serves as the clinical benchmark for the posttreatment surveillance imaging of HGG. However, many advanced imaging techniques have shown promise in helping better delineate the findings in indeterminate scenarios, as posttreatment effects can often mimic true tumor progression on conventional imaging. These challenges are further confounded by the histologic admixture that can commonly occur between tumor growth and treatment-related effects within the posttreatment bed. This review discusses the current practices in the surveillance imaging of HGG and the role of advanced imaging techniques, including perfusion MRI and metabolic MRI.

Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation-Coupled Multidimensional Support Vector Machine

PURPOSE

To provide early and localized glioblastoma (GBM) recurrence prediction, we introduce a novel postsurgery multiparametric magnetic resonance-based support vector machine (SVM) method coupling with stem cell niche (SCN) proximity estimation.

METHODS AND MATERIALS

This study used postsurgery magnetic resonance imaging (MRI) scans from 50 patients with recurrent GBM, obtained approximately 2 months before clinically diagnosed recurrence. The main prediction pipeline consisted of a proximity-based estimator to identify regions with high risk of recurrence (HRRs) and an SVM classifier to provide voxelwise prediction in HRRs. The HRRs were estimated using the weighted sum of inverse distances to 2 possible origins of recurrence-the SCN and the tumor cavity. Subsequently, multiparametric voxels (from T1, T1 contrast-enhanced, fluid-attenuated inversion recovery, T2, and apparent diffusion coefficient) within the HRR were grouped into recurrent (warped from the clinical diagnosis) and nonrecurrent subregions and fed into the proximity estimation-coupled SVM classifier (SVM_PE). The cohort was randomly divided into 40% and 60% for training and testing, respectively. The trained SVM_PE was then extrapolated to an earlier time point for earlier recurrence prediction. As an exploratory analysis, the SVM_PE predictive cluster sizes and the image intensities from the 5 magnetic resonance sequences were compared across time to assess the progressive subclinical traces.

RESULTS

On 2-month prerecurrence MRI scans from 30 test cohort patients, the SVM_PE classifier achieved a recall of 0.80, a precision of 0.69, an F1-score of 0.73, and a mean boundary distance of 7.49 mm. Exploratory analysis at early time points showed spatially consistent but significantly smaller subclinical clusters and significantly increased T1 contrast-enhanced and apparent diffusion coefficient values over time.

CONCLUSIONS

We demonstrated a novel voxelwise early prediction method, SVM_PE, for GBM recurrence based on clinical follow-up MR scans. The SVM_PE is promising in localizing subclinical traces of recurrence 2 months ahead of clinical diagnosis and may be used to guide more effective personalized early salvage therapy.

Combined Diagnostic Accuracy of Diffusion and Perfusion MR Imaging to Differentiate Radiation-Induced Necrosis from Recurrence in Glioblastoma

We aimed to use quantitative values derived from perfusion and diffusion-weighted MR imaging (PWI and DWI) to differentiate radiation-induced necrosis (RIN) from tumor recurrence in Glioblastoma (GBM) and investigate the best parameters for improved diagnostic accuracy and clinical decision-making. Methods: A retrospective analysis of follow-up MRI with new enhancing observations was performed in histopathologically confirmed subjects of post-treated GBM, who underwent re-surgical exploration. Quantitative estimation of rCBV (relative cerebral blood volume) from PWI and three methods of apparent diffusion coefficient (ADC) estimation were performed, namely ADC R1 (whole cross-sectional area of tumor), ADC R2 (only solid enhancing lesion), and ADC R3 (central necrosis). ROC curve and logistic regression analysis was completed. A confusion matrix table created using Excel provided the best combination parameters to ameliorate false-positive and false-negative results. Results: Forty-four subjects with a mean age of 46 years (range, 19−70 years) underwent re-surgical exploration with RIN in 28 (67%) and recurrent tumor in 16 (33%) on histopathology. rCBV threshold of >3.4 had the best diagnostic accuracy (AUC = 0.93, 81% sensitivity and 89% specificity). A multiple logistic regression model showed significant contributions from rCBV (p < 0.001) and ADC R3 (p = 0.001). After analysis of confusion matrix ADC R3 > 2032 × 10−6 mm2 achieved 100% specificity with gain in sensitivity (94% vs. 56%). Conclusions: A combination of parameters had better diagnostic performance, and a stepwise combination of rCBV and ADC R3 obviated unnecessary biopsies in 10% (3/28), leading to improved clinical decision-making.

The diagnostic accuracy of O-(2-18F-fluoroethyl)-L-tyrosine parameters for the differentiation of brain tumour progression from treatment-related changes

BACKGROUND

18F-fluoro-ethyl-tyrosine (18F-FET) is recommended to distinguish brain tumours post-therapeutic true progression (including recurrent and metastatic brain tumours) and treatment-related change (TRC). However, many parameters of 18F-FET can be used for this differential diagnosis. Our purpose was to investigate the diagnostic accuracy of various 18F-FET parameters to differentiate true progression from TRC.

METHODS

We performed a literature search using the following databases: the PubMed, Embase and Web of Science databases up to 29 November 2020. We included studies that reported the diagnostic test results of 18F-FET to distinguish true progression from TRC. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to evaluate the quality of the included studies. The diagnostic accuracy of various parameters was pooled using a random-effects model.

RESULTS

We included 17 eligible studies (nine parameters). For static parameters of 18F-FET, the maximum and mean tumour-to-brain ratios (TBRmax and TBRmean) showed similar pooled sensitivities of 82% [95% confidence interval (CI), 80-85%) and 82% (95% CI, 78-85%), respectively. Among the three kinetic parameters (slope, time to peak and kinetic pattern), the kinetic pattern presented the optimal diagnostic value with a pooled sensitivity of 81% (95% CI, 75-86%). When combining the static and kinetic parameters, the diagnostic performance of 18F-FET was significantly improved, with a pooled sensitivity of 90% (95% CI, 84-94%) in the combination of TBR and kinetic patterns.

CONCLUSIONS

18F-FET static parameters alone showed a comparably high sensitivity in the differentiation between brain tumour true progression and TRC. Combining static and kinetic parameters provided improved diagnostic performance.

Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review

Background: Glioblastoma is the most frequent malignant primitive brain tumor in adults. The treatment includes surgery, radiotherapy, and chemotherapy. During follow-up, combined chemoradiotherapy can induce treatment-related changes mimicking tumor progression on medical imaging, such as pseudoprogression (PsP). Differentiating PsP from true progression (TP) remains a challenge for radiologists and oncologists, who need to promptly start a second-line treatment in the case of TP. Advanced magnetic resonance imaging (MRI) techniques such as diffusion-weighted imaging, perfusion MRI, and proton magnetic resonance spectroscopic imaging are more efficient than conventional MRI in differentiating PsP from TP. None of these techniques are fully effective, but current advances in computer science and the advent of artificial intelligence are opening up new possibilities in the imaging field with radiomics (i.e., extraction of a large number of quantitative MRI features describing tumor density, texture, and geometry). These features are used to build predictive models for diagnosis, prognosis, and therapeutic response. Method: Out of 7350 records for MR spectroscopy, GBM, glioma, recurrence, diffusion, perfusion, pseudoprogression, radiomics, and advanced imaging, we screened 574 papers. A total of 228 were eligible, and we analyzed 72 of them, in order to establish the role of each imaging modality and the usefulness and limitations of radiomics analysis.

Diagnostic performance of DSC perfusion MRI to distinguish tumor progression and treatment-related changes: a systematic review and meta-analysis

Background

In patients with high-grade glioma (HGG), true disease progression and treatment-related changes often appear similar on magnetic resonance imaging (MRI), making it challenging to evaluate therapeutic response. Dynamic susceptibility contrast (DSC) MRI has been extensively studied to differentiate between disease progression and treatment-related changes. This systematic review evaluated and synthesized the evidence for using DSC MRI to distinguish true progression from treatment-related changes.

Methods

We searched Ovid MEDLINE and the Ovid MEDLINE in-process file (January 2005-October 2019) and the reference lists. Studies on test performance of DSC MRI using relative cerebral blood volume in HGG patients were included. One investigator abstracted data, and a second investigator confirmed them; two investigators independently assessed study quality. Meta-analyses were conducted to quantitatively synthesize area under the receiver operating curve (AUROC), sensitivity, and specificity.

Results

We screened 1177 citations and included 28 studies with 638 patients with true tumor progression, and 430 patients with treatment-related changes. Nineteen studies reported AUROC and the combined AUROC is 0.85 (95% CI, 0.81-0.90). All studies contributed data for sensitivity and specificity, and the pooled sensitivity and specificity are 0.84 (95% CI, 0.80-0.88), and 0.78 (95% CI, 0.72-0.83). Extensive subgroup analyses based on study, treatment, and imaging characteristics generally showed similar results.

Conclusions

There is moderate strength of evidence that relative cerebral blood volume obtained from DSC imaging demonstrated "excellent" ability to discriminate true tumor progression from treatment-related changes, with robust sensitivity and specificity.

Magnetic resonance spectroscopy outperforms perfusion in distinguishing between pseudoprogression and disease progression in patients with glioblastoma

Background

There is a need to establish biomarkers that distinguish between pseudoprogression (PsP) and true tumor progression in patients with glioblastoma (GBM) treated with chemoradiation.

Methods

We analyzed magnetic resonance spectroscopic imaging (MRSI) and dynamic susceptibility contrast (DSC) MR perfusion data in patients with GBM with PsP or disease progression after chemoradiation. MRSI metabolites of interest included intratumoral choline (Cho), myo-inositol (mI), glutamate + glutamine (Glx), lactate (Lac), and creatine on the contralateral hemisphere (c-Cr). Student T-tests and area under the ROC curve analyses were used to detect group differences in metabolic ratios and their ability to predict clinical status, respectively.

Results

28 subjects (63 ± 9 years, 19 men) were evaluated. Subjects with true progression (n = 20) had decreased enhancing region mI/c-Cr (P = .011), a marker for more aggressive tumors, compared to those with PsP, which predicted tumor progression (AUC: 0.84 [0.76, 0.92]). Those with true progression had elevated Lac/Glx (P = .0009), a proxy of the Warburg effect, compared to those with PsP which predicted tumor progression (AUC: 0.84 [0.75, 0.92]). Cho/c-Cr did not distinguish between PsP and true tumor progression. Despite rCBV (AUC: 0.70 [0.60, 0.80]) and rCBF (AUC: 0.75 [0.65, 0.84]) being individually predictive of tumor response, they added no additional predictive value when combined with MRSI metabolic markers.

Conclusions

Incorporating enhancing lesion MRSI measures of mI/c-Cr and Lac/Glx into brain tumor imaging protocols can distinguish between PsP and true progression and inform patient management decisions.

Neuroinflammation and immunoregulation in glioblastoma and brain metastases: Recent developments in imaging approaches

Brain tumors and brain metastases induce changes in brain tissue remodeling that lead to immunosuppression and trigger an inflammatory response within the tumor microenvironment. These immune and inflammatory changes can influence invasion and metastasis. Other neuroinflammatory and necrotic lesions may occur in patients with brain cancer or brain metastases as sequelae from treatment with radiotherapy. Glioblastoma (GBM) is the most aggressive primary malignant brain cancer in adults. Imaging methods such as positron emission tomography (PET) and different magnetic resonance imaging (MRI) techniques are highly valuable for the diagnosis and therapeutic evaluation of GBM and other malignant brain tumors. However, differentiating between tumor tissue and inflamed brain tissue with imaging protocols remains a challenge. Here, we review recent advances in imaging methods that have helped to improve the specificity of primary tumor diagnosis versus evaluation of inflamed and necrotic brain lesions. We also comment on advances in differentiating metastasis from neuroinflammation processes. Recent advances include the radiosynthesis of ¹⁸ F-FIMP, an L-type amino acid transporter 1 (LAT1)-specific PET probe that allows clearer differentiation between tumor tissue and inflammation compared to previous probes, and the combination of different advanced imaging protocols with the inclusion of radiomics and machine learning algorithms.

Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas

ABSTRACT

Advanced imaging techniques provide a powerful tool to assess the intratumoral and intertumoral heterogeneity of gliomas. Advances in the molecular understanding of glioma subgroups may allow improved diagnostic assessment combining imaging and molecular tumor features, with enhanced prognostic utility and implications for patient treatment. In this article, a comprehensive overview of the physiologic basis for conventional and advanced imaging techniques is presented, and clinical applications before and after treatment are discussed. An introduction to the principles of radiomics and the advanced integration of imaging, clinical outcomes, and genomic data highlights the future potential for this field of research to better stratify and select patients for standard as well as investigational therapies.

Identify glioma recurrence and treatment effects with triple-tracer PET/CT

Background

Differential diagnosis of tumour recurrence (TuR) from treatment effects (TrE), mostly induced by radiotherapy and chemotherapy, is still difficult by using conventional computed tomography (CT) or magnetic resonance (MR) imaging. We have investigated the diagnostic performance of PET/CT with 3 tracers, ¹³N-NH₃, ¹⁸F-FDOPA, and ¹⁸F-FDG, to identify TuR and TrE in glioma patients following treatment.

Methods

Forty-three patients with MR-suspected recurrent glioma were included. The maximum and mean standardized uptake values (SUVmax and SUVmean) of the lesion and the lesion-to-normal grey-matter cortex uptake (L/G) ratio were obtained from each tracer PET/CT. TuR or TrE was determined by histopathology or clinical MR follow-up for at least 6 months.

Results

In this cohort, 34 patients were confirmed to have TuR, and 9 patients met the diagnostic standard of TrE. The SUVmax and SUVmean of ¹³N-NH₃ and ¹⁸F-FDOPA PET/CT at TuR lesions were significantly higher compared with normal brain tissue (¹³N-NH₃ 0.696 ± 0.558, 0.625 ± 0.507 vs 0.486 ± 0.413; ¹⁸F-FDOPA 0.455 ± 0.518, 0.415 ± 0.477 vs 0.194 ± 0.203; both P < 0.01), but there was no significant difference in ¹⁸F-FDG (6.918 ± 3.190, 6.016 ± 2.807 vs 6.356 ± 3.104, P = 0.290 and 0.493). L/G ratios of ¹³N-NH₃ and ¹⁸F-FDOPA were significantly higher in TuR than in TrE group (¹³N-NH_3, 1.573 ± 0.099 vs 1.025 ± 0.128, P = 0.008; ¹⁸F-FDOPA, 2.729 ± 0.131 vs 1.514 ± 0.141, P < 0.001). The sensitivity, specificity and AUC (area under the curve) by ROC (receiver operating characteristic) analysis were 57.7%, 100% and 0.803, for ¹³N-NH₃; 84.6%, 100% and 0.938, for ¹⁸F-FDOPA; and 80.8%, 100%, and 0.952, for the combination, respectively.

Conclusion

Our results suggest that although multiple tracer PET/CT may improve differential diagnosis efficacy, for glioma TuR from TrE, ¹⁸F-FDOPA PET-CT is the most reliable. The combination of ¹⁸F-FDOPA and ¹³N-NH₃ does not increase the diagnostic efficiency, while ¹⁸F-FDG is not worthy for differential diagnosis of glioma TuR and TrE.

Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance

PURPOSE

Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem).

METHODS

Study participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists' classifications.

RESULTS

SVM classification based on combined perfusion and structural features outperformed radiologists' classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists' classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists).

CONCLUSION

Our results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001).

Clinical Review of Computed Tomography and MR Perfusion Imaging in Neuro-Oncology

Neuroimaging plays an essential role in the initial diagnosis and continued surveillance of intracranial neoplasms. The advent of perfusion techniques with computed tomography and MR imaging have proven useful in neuro-oncology, offering enhanced approaches for tumor grading, guiding stereotactic biopsies, and monitoring treatment efficacy. Perfusion imaging can help to identify treatment-related processes, such as radiation necrosis, pseudoprogression, and pseudoregression, and can help to inform treatment-related decision making. Perfusion imaging is useful to differentiate between tumor types and between tumor and nonneoplastic conditions. This article reviews the clinical relevance and implications of perfusion imaging in neuro-oncology and highlights promising perfusion biomarkers.

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers

After chemoradiotherapy for glioblastoma, pseudoprogression can occur and must be distinguished from true progression to correctly manage glioblastoma treatment and follow-up. Conventional treatment response assessment is evaluated via conventional MRI (contrast-enhanced T1-weighted and T2/FLAIR), which is unreliable. The emergence of advanced MRI techniques, MR spectroscopy, and PET tracers has improved pseudoprogression diagnostic accuracy. This review presents a literature review of the different imaging techniques and potential imaging biomarkers to differentiate pseudoprogression from true progression.

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review: Part 1 - Molecular, morphological and clinical features

With new therapeutic protocols, more patients treated for glioblastoma have experienced a suspicious radiologic image of progression (pseudoprogression) during follow-up. Pseudoprogression should be differentiated from true progression because the disease management is completely different. In the case of pseudoprogression, the follow-up continues, and the patient is considered stable. In the case of true progression, a treatment adjustment is necessary. Presently, a pseudoprogression diagnosis certainly needs to be pathologically confirmed. Some important efforts in the radiological, histopathological, and genomic fields have been made to differentiate pseudoprogression from true progression, and the assessment of response criteria exists but remains limited. The aim of this paper is to highlight clinical and pathological markers to differentiate pseudoprogression from true progression through a literature review.

Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas

Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.

Diagnostic Performance of PET and Perfusion-Weighted Imaging in Differentiating Tumor Recurrence or Progression from Radiation Necrosis in Posttreatment Gliomas: A Review of Literature

Tumor resection followed by chemoradiation remains the current criterion standard treatment for high-grade gliomas. Regardless of aggressive treatment, tumor recurrence and radiation necrosis are 2 different outcomes. Differentiation of tumor recurrence from radiation necrosis remains a critical problem in these patients because of considerable overlap in clinical and imaging presentations. Contrast-enhanced MR imaging is the universal imaging technique for diagnosis, treatment evaluation, and detection of recurrence of high-grade gliomas. PWI and PET with novel radiotracers have an evolving role for monitoring treatment response in high-grade gliomas. In the literature, there is no clear consensus on the superiority of either technique or their complementary information. This review aims to elucidate the diagnostic performance of individual and combined use of functional (PWI) and metabolic (PET) imaging modalities to distinguish recurrence from posttreatment changes in gliomas.

Glioblastoma Recurrence Versus Treatment Effect in a Pathology-Documented Series

OBJECTIVE

Patients diagnosed with glioblastoma (GBM) are treated with surgery followed by fractionated radiotherapy with concurrent and adjuvant temozolomide. Patients are monitored with serial magnetic resonance imaging (MRI). However, treatment-related changes frequently mimic disease progression. We reviewed a series of patients undergoing surgery for presumed first-recurrence GBM, where pathology reports were available for tissue diagnosis, in order to better understand factors associated with a diagnosis of treatment-related changes on final pathology.

METHODS

Patient records at a single institution between 2005 and 2015 were retrospectively reviewed. Pathology reports were reviewed to determine diagnosis of recurrent GBM or treatment effect. Survival analysis was performed interrogating overall survival (OS) and progression-free survival (PFS). Correlation with radiation treatment plans was also examined.

RESULTS

One-hundred-twenty-three patients were identified. One-hundred-sixteen patients (94%) underwent resection and seven underwent biopsy. Treatment-related changes were reported in 20 cases (16%). These patients had longer median OS and PFS from the time of recurrence than patients with true disease progression. However, there was no significant difference in OS from the time of initial diagnosis. Treatment effect was associated with surgery within 90 days of completing radiation. In patients receiving radiation at our institution (n = 53), larger radiation target volume and a higher maximum dose were associated with treatment effect.

CONCLUSION

Treatment effect was associated with surgery nearer to completion of radiation, a larger radiation target volume, and a higher maximum point dose. Treatment effect was associated with longer PFS and OS from the time of recurrence, but not from the time of initial diagnosis.

Advanced multimodal imaging in differentiating glioma recurrence from post-radiotherapy changes

Gliomas are the most common malignant primary brain tumor, and their prognosis is extremely poor. Radiotherapy is an important treatment for glioma patients, but the changes caused by radiotherapy have brought difficulties in clinical image evaluation because differentiating glioma recurrence from post-radiotherapy changes including pseudo-progression (PD) and radiation necrosis (RN) remains a challenge. Therefore, accurate and reliable imaging evaluation is very important for making clinical decisions. In recent years, advanced multimodal imaging techniques have been applied to achieve the goal of better differentiating glioma recurrence from post-radiotherapy changes for minimizing errors associated with interpretation of treatment effects. In this review, we discuss the recent applications of advanced multimodal imaging such as diffusion MRI sequences, amide proton transfer MRI sequences, perfusion MRI sequences, MR spectroscopy and multinuclides PET/CT in the evaluation of post-radiotherapy treatment response in glioma patients and highlight their potential role in differentiating post-radiotherapy changes from glioma recurrence.

The wavelet power spectrum of perfusion weighted MRI correlates with tumor vascularity in biopsy-proven glioblastoma samples

Background

Wavelet transformed reconstructions of dynamic susceptibility contrast (DSC) MR perfusion (wavelet-MRP) are a new and elegant way of visualizing vascularization. Wavelet-MRP maps yield a clear depiction of hypervascular tumor regions, as recently shown.

Objective

The aim of this study was to elucidate a possible connection of the wavelet-MRP power spectrum in glioblastoma (GBM) with local vascularity and cell proliferation.

Methods

For this IRB-approved study 12 patients (63.0+/-14.9y; 7m) with histologically confirmed IDH-wildtype GBM were included. Target regions for biopsies were prospectively marked on tumor regions as seen on preoperative 3T MRI. During subsequent neurosurgical tumor resection 43 targeted biopsies were taken from these target regions, of which all 27 matching samples were analyzed. All specimens were immunohistochemically analyzed for endothelial cell marker CD31 and proliferation marker Ki67 and correlated to the wavelet-MRP power spectrum as derived from DSC perfusion weighted imaging.

Results

There was a strong correlation between wavelet-MRP power spectrum (median = 4.41) and conventional relative cerebral blood volume (median = 5.97 ml/100g) in Spearman's rank-order correlation (κ = .83, p < .05). In a logistic regression model, the wavelet-MRP power spectrum showed a significant correlation to CD31 dichotomized to no or present staining (p = .04), while rCBV did not show a significant correlation to CD31 (p = .30). No significant association between Ki67 and rCBV or wavelet-MRP was found (p = .62 and p = .70, respectively).

Conclusion

The wavelet-MRP power spectrum derived from existing DSC-MRI data might be a promising new surrogate for tumor vascularity in GBM.

Adding DSC PWI and DWI to BT-RADS can help identify postoperative recurrence in patients with high-grade gliomas

BACKGROUND

The Brain Tumor Reporting and Data System (BT-RADS) category 3 is suitable for identifying cases with intermediate probability of tumor recurrence that do not meet the Response Assessment in Neuro-Oncology (RANO) criteria for progression. The aim of this study was to evaluate the added value of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC PWI) and diffusion-weighted imaging (DWI) to BT-RADS for differentiating tumor recurrence from non-recurrence in postoperative high-grade glioma (HGG) patients with category 3 lesions.

METHODS

Patients with BT-RADS category 3 lesions were included. The maximal relative cerebral blood volume (rCBV_max) and the mean apparent diffusion coefficient (ADC_mean) values were measured. The added value of DSC PWI and DWI to BT-RADS was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Fifty-one of 91 patients had tumor recurrence, and 40 patients did not. There were significant differences in rCBV_max and ADC_mean between the tumor recurrence group and non-recurrence group. Compared to BT-RADS alone, the addition of DSC PWI to BT-RADS increased the area under curve (AUC) from 0.76 (95% confidence interval [CI] 0.66-0.84) to 0.90 (95% CI 0.81-0.95) for differentiating tumor recurrence from non-recurrence. The addition of DWI to BT-RADS increased the AUC from 0.76 (95% CI 0.66-0.84) to 0.88 (95% CI 0.80-0.94). The combination of BT-RADS, DSC PWI, and DWI exhibited the best diagnostic performance (AUC = 0.95; 95% CI 0.88-0.98) for differentiating tumor recurrence from non-recurrence.

CONCLUSION

Adding DSC PWI and DWI to BT-RADS can significantly improve the diagnostic performance for differentiating tumor recurrence from non-recurrence in BT-RADS category 3 lesions.

Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques

The Response Assessment in Neuro-Oncology criteria were developed as an objective tool for radiologic assessment of treatment response in high-grade gliomas. Imaging plays a critical role in the management of the patient with glioma, from initial diagnosis to posttreatment follow-up, which can be particularly challenging for radiologists. Interpreting findings after surgery, radiation, and chemotherapy requires profound knowledge about the tumor biology, as well as the peculiar changes expected to ensue as a consequence of each treatment technique. In this article, we discuss the imaging findings associated with tumor progression, tumor response, pseudoprogression, and pseudoresponse according to the Response Assessment in Neuro-Oncology criteria for high-grade and lower-grade gliomas. We describe relevant practical issues when evaluating patients with glioma, such as the need for imaging in the first 48 hours, the radiation therapy planning and isodose curves, the significance of T2/FLAIR hyperintense lesions, the impact of the timing for the evaluation after radiation therapy, and the definition of progressive disease on the histologic specimen. We also illustrate the correlation among the findings on conventional MR imaging with advanced techniques, such as perfusion, diffusion-weighted imaging, spectroscopy, and amino acid PET. Because many of the new lesions represent a mixture of tumor cells and tissue with radiation injury, the radiologist aims to identify the predominant component of the lesion and categorize the findings according to Response Assessment in Neuro-Oncology criteria so that the patient can receive the best treatment.

DCE-MRI perfusion predicts pseudoprogression in metastatic melanoma treated with immunotherapy

PURPOSE

It can be challenging to differentiate pseudoprogression from progression. We assessed the ability of dynamic contrast enhanced T1 MRI (DCE-MRI) perfusion to identify pseudoprogression in melanoma brain metastases.

METHODS

Patients with melanoma brain metastases who underwent immunotherapy and DCE-MRI were identified. Enhancing lesions ≥  5mm in diameter on DCE-MRI and that were new or increased in size between a week from beginning the treatment, and a month after completing the treatment were included in the analysis. The 90th percentiles of rVp and rKtrans and the presence or absence of hemorrhage were recorded. Histopathology served as the reference standard for pseudoprogression. If not available, pseudoprogression was defined as neurological and radiographic stability or improvement without any new treatment for ≥ 2 months.

RESULTS

Forty-four patients were identified; 64% received ipilimumab monotherapy for a median duration of 9 weeks (range, 1-138). Sixty-four lesions in 44 patients were included in the study. Of these, nine lesions in eight patients were determined to be pseudoprogression and seven lesions were previously irradiated. Forty-four progression lesions and eight pseudoprogression lesions were hemorrhagic. Median lesion volume for pseudoprogression and progression were not significantly different, at 2.3 cm³ and 3.2 cm³, respectively (p = 0.82). The rVp⁹⁰ was smaller in pseudoprogression versus progression, at 2.2 and 5.3, respectively (p = 0.02), and remained significant after false discovery rate adjustment (p = 0.04).

CONCLUSIONS

Pseudoprogression exhibited significantly lower rVp⁹⁰ on DCE-MRI compared with progression. This knowledge can be useful for managing growing lesions in patients with melanoma brain metastases who are receiving immunotherapy.

Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma

Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps.

MRI scans of glioblastoma patients can be misleading and some patients appear to show features of progressive disease although they respond to treatment. Here, the authors use MRI images of progressive disease or pseudoprogression and build a classifier using machine learning to distinguish the two.

Utility of Dynamic Susceptibility Contrast Perfusion-Weighted MR Imaging and 11C-Methionine PET/CT for Differentiation of Tumor Recurrence from Radiation Injury in Patients with High-Grade Gliomas

BACKGROUND AND PURPOSE

Both ¹¹C-methionine PET/CT and DSC-PWI could be used to differentiate radiation injury from recurrent brain tumors. Our aim was to assess the performance of MET PET/CT and DSC-PWI for differentiation of recurrence and radiation injury in patients with high-grade gliomas and to quantitatively analyze the diagnostic values of PET and PWI parameters.

MATERIALS AND METHODS

Forty-two patients with high-grade gliomas were enrolled in this study. The final diagnosis was determined by histopathologic analysis or clinical follow-up. PWI and PET parameters were recorded and compared between patients with recurrence and those with radiation injury using Student t tests. Receiver operating characteristic and logistic regression analyses were used to determine the diagnostic performance of each parameter.

RESULTS

The final diagnosis was recurrence in 33 patients and radiation injury in 9. PET/CT showed a patient-based sensitivity and specificity of 0.909 and 0.556, respectively, while PWI showed values of 0.667 and 0.778, respectively. The maximum standardized uptake value, mean standardized uptake value, tumor-to-background maximum standardized uptake value, and mean relative CBV were significantly higher for patients with recurrence than for patients with radiation injury. All these parameters showed a high discriminative power in receiver operating characteristic analysis. The optimal cutoff values for the tumor-to-background maximum standardized uptake value and mean relative CBV were 1.85 and 1.83, respectively, and corresponding sensitivities and specificities for the diagnosis of recurrence were 0.97 and 0.667 and 0.788 and 0.88, respectively. Areas under the curve for the tumor-to-background maximum standardized uptake value and mean relative CBV were 0.847 ± 0.077 and 0.845 ± 0.078, respectively. Combined assessment of the tumor-to-background maximum standardized uptake value and mean relative CBV showed the largest area under the curve (0.953 ± 0.031), with corresponding sensitivity and specificity of 0.848 and 1.0, respectively.

CONCLUSIONS

Both ¹¹C-methionine PET/CT and PWI are equally accurate in the differentiation of recurrence from radiation injury in patients with high-grade gliomas, and a combination of the 2 modalities could result in increased diagnostic accuracy.

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

Treatment evaluation of patients with glioblastomas is important to aid in clinical decisions. Conventional MRI with contrast is currently the standard method, but unable to differentiate tumor progression from treatment-related effects. Pseudoprogression appears as new enhancement, and thus mimics tumor progression on conventional MRI. Contrarily, a decrease in enhancement or edema on conventional MRI during antiangiogenic treatment can be due to pseudoresponse and is not necessarily reflective of a favorable outcome. Neovascularization is a hallmark of tumor progression but not for posttherapeutic effects. Perfusion-weighted MRI provides a plethora of additional parameters that can help to identify this neovascularization. This review shows that perfusion MRI aids to identify tumor progression, pseudoprogression, and pseudoresponse. The review provides an overview of the most applicable perfusion MRI methods and their limitations. Finally, future developments and remaining challenges of perfusion MRI in treatment evaluation in neuro-oncology are discussed. Level of Evidence: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:11-22.

Magnetic Resonance Imaging Parameters and Their Impact on Survival of Patients with Glioblastoma: Tumor Perfusion Predicts Survival

BACKGROUND

Many prognostic factors influence overall survival (OS) of patients with glioblastoma. Despite gross total resection and Stupp protocol adherence, many patients have poor survival. Perfusion magnetic resonance imaging may assist in diagnosis, treatment monitoring, and prognostication.

METHODS

This retrospective study of 36 patients with glioblastoma assessed influence of preoperative magnetic resonance imaging parameters reflecting tumor cell density and vascularity and patient age on OS.

RESULTS

The area under curve based on optimal receiver operating characteristic curves for the perfusion parameters normalized relative tumor blood volume (n_rTBV) and normalized relative tumor blood flow (n_rTBF) were 0.92 and 0.89, respectively, and the highest among all imaging parameters and age. OS showed strongly negative correlations with corrected n_rTBV (R = -0.70; P < 0.001) and n_rTBF (R = -0.67; P < 0.001). The Cox model, which included age and imaging parameters, demonstrated that n_rTBV and n_rTBF were most predictive of OS, with hazard ratios of 5.97 (P = 0.0001) and 8.76 (P = 0.0001), respectively, compared with 1.63 (P = 0.19) for age. Eighteen patients with corrected n_rTBV ≤2.5 (best cutoff value) had a median OS of 15.1 months (95% confidence interval (CI), 11.34-21.25) compared with 2.8 months (95% CI, 1.48-4.03; P < 0.001) for 18 patients with corrected n_rTBV >2.5. Twenty-four patients with n_rTBF ≤2.79 had a median OS of 12 months (95% CI, 10.46-17.9) compared with 2.8 months for 12 patients with n_rTBF >2.79 (95% CI, 1.31-4.2; P < 0.001).

CONCLUSIONS

The dominant predictors of OS are normalized perfusion parameters n_rTBV and n_rTBF. Preoperative perfusion imaging may be used as a surrogate to predict glioblastoma aggressiveness and survival independent of treatment.

Survival after radiation therapy for high-grade glioma

Background

High-grade gliomas (HGGs) are a heterogeneous disease group, with variable prognosis, inevitably causing deterioration of the quality of life. The estimated 2-year overall survival is 20%, despite the best trimodality treatment consisting of surgery, chemotherapy, and radiotherapy.

Aim

To evaluate long-term survival outcomes and factors influencing the survival of patients with high-grade gliomas treated with radiotherapy.

Materials and methods

Data from 47 patients diagnosed with high-grade gliomas between 2009 and 2014 and treated with three-dimensional radiotherapy (3DRT) or intensity-modulated radiotherapy (IMRT) were analyzed retrospectively.

Results

Median survival was 16.6 months; 29 patients (62%) died before the time of analysis. IMRT was employed in 68% of cases. The mean duration of radiotherapy was 56 days, and the mean delay to the start of radiotherapy was 61.7 days (range, 27-123 days). There were no statistically significant effects of duration of radiotherapy or delay to the start of radiotherapy on patient outcomes.

Conclusions

Age, total amount of gross resection, histological type, and use of adjuvant temozolomide influenced survival rate (p < 0.05). The estimated overall survival was 18 months (Kaplan-Meier estimator). Our results corroborated those reported in the literature.

Differentiating pseudoprogression from true progression: analysis of radiographic, biologic, and clinical clues in GBM

INTRODUCTION

Pseudoprogression (PsP) is a diagnostic dilemma in glioblastoma (GBM) after chemoradiotherapy (CRT). Magnetic resonance imaging (MRI) features may fail to distinguish PsP from early true progression (eTP), however clinical findings may aid in their distinction.

METHODS

Sixty-seven patients received CRT for GBM between 2003 and 2016, and had pre- and post-treatment imaging suitable for retrospective evaluation using RANO criteria. Patients with signs of progression within the first 12-weeks post-radiation (P-12) were selected. Lesions that improved or stabilized were defined as PsP, and lesions that progressed were defined as eTP.

RESULTS

The median follow up for all patients was 17.6 months. Signs of progression developed in 35/67 (52.2%) patients within P-12. Of these, 20/35 (57.1%) were subsequently defined as eTP and 15/35 (42.9%) as PsP. MRI demonstrated increased contrast enhancement in 84.2% of eTP and 100% of PsP, and elevated CBV in 73.7% for eTP and 93.3% for PsP. A decrease in FLAIR was not seen in eTP patients, but was seen in 26.7% PsP patients. Patients with eTP were significantly more likely to require increased steroid doses or suffer clinical decline than PsP patients (OR 4.89, 95% CI 1.003-19.27; p = 0.046). KPS declined in 25% with eTP and none of the PsP patients.

CONCLUSIONS

MRI imaging did not differentiate eTP from PsP, however, KPS decline or need for increased steroids was significantly more common in eTP versus PsP. Investigation and standardization of clinical assessments in response criteria may help address the diagnostic dilemma of pseudoprogression after frontline treatment for GBM.

Incidence of pseudoprogression in low-grade gliomas treated with radiotherapy

Background

As the incidence of pseudo-progressive disease (psPD), or pseudoprogression, in low-grade glioma (LGG) is unknown, we retrospectively investigated this phenomenon in a cohort of LGG patients given radiotherapy (RT).

Methods

All MRI scans and clinical data from patients with histologically proven LGG treated with radiation between 2000 and 2011 were reviewed. PsPD was scored when a new enhancing lesion occurred after RT and subsequently disappeared or remained stable for at least a year without therapy, including dexamethasone.

Results

Sixty-three out of 71 patients who received RT for LGG were deemed eligible for evaluation of psPD. The median follow-up was 5 years (range 1‒10 y). PsPD was seen in 13 patients (20.6%). PsPD occurred after a median of 12 months with a range of 3-78 months. The median duration of psPD was 6 months, with a range of 2-26 months and always occurred within the RT high dose fields of at least 45 Gy. The area of the enhancement at the time of psPD was significantly smaller compared with the area of enhancement during "true" progression (median size 54mm2 [range 12-340mm2] vs 270mm2 [range 30-3420mm2], respectively; P = .009).

Conclusions

PsPD occurs frequently in LGG patients receiving RT. This supports the policy to postpone a new line of treatment until progression is evident, especially when patients have small contrast enhancing lesions within the RT field.

Apparent transverse relaxation (R2∗) on MRI as a method to differentiate treatment effect (pseudoprogression) versus progressive disease in chemoradiation for malignant glioma

INTRODUCTION

Pseudoprogression (psPD) is a transient post-treatment imaging change that is commonly seen when treating glioma with chemotherapy and radiation. The use of apparent transverse relaxation rate (R2∗), which is calculated from a contrast-free multi-echo gradient echo Magnetic Resonance Imaging (MRI) sequence, may allow for quantitative identification of patients with suspected psPD.

METHODS

We acquired a multi-echo gradient echo sequence using a 3T-Siemens Prisma MRI. The signal decay through the echoes was fitted to provide the R2∗ coefficient. We segmented the T₁ -gadolinium enhancing the image to provide a contrast enhancing lesion (CEL) and the FLAIR hyperintensity to provide a non-enhancing lesion (NEL). These regions of interest were applied to the multi-echo gradient echo to acquire a mean R2∗ within the CEL and NEL. We additionally acquired ADC data to attempt to corroborate our findings.

RESULTS

We found that patients who later exhibited PD exhibited a higher R2∗ within the CEL as well as a higher ratio of CEL to NEL. Our data correctly distinguished pseudoprogression from treatment effect in 9/9 patients, while ADC corrected identified 7/9 patients using an absolute ADC of 1200 × 10^-6  mm² /s.

CONCLUSIONS

Our method seems promising for the accurate identification of psPD, and the technique is amenable to evaluation in larger, multi-centre patient cohorts.