Large-volume low apparent diffusion coefficient lesions predict poor survival in bevacizumab-treated glioblastoma patients

Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study

OBJECTIVES

To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL.

METHODS

83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman's correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models.

RESULTS

Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966-0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895-0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953-0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828.

CONCLUSION

rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.

The Use of Apparent Diffusion Coefficient Values for Differentiating Bevacizumab-Related Cytotoxicity from Tumor Recurrence and Radiation Necrosis in Glioblastoma

OBJECTIVES

Glioblastomas (GBM) are the most common primary invasive neoplasms of the brain. Distinguishing between lesion recurrence and different types of treatment related changes in patients with GBM remains challenging using conventional MRI imaging techniques. Therefore, accurate and precise differentiation between true progression or pseudoresponse is crucial in deciding on the appropriate course of treatment. This retrospective study investigated the potential of apparent diffusion coefficient (ADC) map values derived from diffusion-weighted imaging (DWI) as a noninvasive method to increase diagnostic accuracy in treatment response.

METHODS

A cohort of 21 glioblastoma patients (mean age: 59.2 ± 11.8, 12 Male, 9 Female) that underwent treatment with bevacizumab were selected. The ADC values were calculated from the DWI images obtained from a standardized brain protocol across 1.5-T and 3-T MRI scanners. Ratios were calculated for rADC values. Lesions were classified as bevacizumab-induced cytotoxicity based on characteristic imaging features (well-defined regions of restricted diffusion with persistent diffusion restriction over the course of weeks without tissue volume loss and absence of contrast enhancement). The rADC value was compared to these values in radiation necrosis and recurrent lesions, which were concluded in our prior study. The nonparametric Wilcoxon signed rank test with p < 0.05 was used for significance.

RESULTS

The mean ± SD age of the selected patients was 59.2 ± 11.8. ADC values and corresponding mean rADC values for bevacizumab-induced cytotoxicity were 248.1 ± 67.2 and 0.39 ± 0.10, respectively. These results were compared to the ADC values and corresponding mean rADC values of tumor progression and radiation necrosis. Significant differences between rADC values were observed in all three groups (p < 0.001). Bevacizumab-induced cytotoxicity had statistically significant lower ADC values compared to both tumor recurrence and radiation necrosis.

CONCLUSION

The study demonstrates the potential of ADC values as noninvasive imaging biomarkers for differentiating recurrent glioblastoma from radiation necrosis and bevacizumab-induced cytotoxicity.

Longitudinal Evaluation of DCE-MRI as an Early Indicator of Progression after Standard Therapy in Glioblastoma

Background and Purpose: Distinguishing treatment-induced imaging changes from progressive disease has important implications for avoiding inappropriate discontinuation of a treatment. Our goal in this study is to evaluate the utility of dynamic contrast-enhanced (DCE) perfusion MRI as a biomarker for the early detection of progression. We hypothesize that DCE-MRI may have the potential as an early predictor for the progression of disease in GBM patients when compared to the current standard of conventional MRI. Methods: We identified 26 patients from 2011 to 2023 with newly diagnosed primary glioblastoma by histopathology and gross or subtotal resection of the tumor. Then, we classified them into two groups: patients with progression of disease (POD) confirmed by pathology or change in chemotherapy and patients with stable disease without evidence of progression or need for therapy change. Finally, at least three DCE-MRI scans were performed prior to POD for the progression cohort, and three consecutive DCE-MRI scans were performed for those with stable disease. The volume of interest (VOI) was delineated by a neuroradiologist to measure the maximum values for Ktrans and plasma volume (Vp). A Friedman test was conducted to evaluate the statistical significance of the parameter changes between scans. Results: The mean interval between subsequent scans was 57.94 days, with POD-1 representing the first scan prior to POD and POD-3 representing the third scan. The normalized maximum Vp values for POD-3, POD-2, and POD-1 are 1.40, 1.86, and 3.24, respectively (FS = 18.00, p = 0.0001). It demonstrates that Vp max values are progressively increasing in the three scans prior to POD when measured by routine MRI scans. The normalized maximum Ktrans values for POD-1, POD-2, and POD-3 are 0.51, 0.09, and 0.51, respectively (FS = 1.13, p < 0.57). Conclusions: Our analysis of the longitudinal scans leading up to POD significantly correlated with increasing plasma volume (Vp). A longitudinal study for tumor perfusion change demonstrated that DCE perfusion could be utilized as an early predictor of tumor progression.

Relative cerebral blood volume as response predictor in the treatment of recurrent glioblastoma with anti-angiogenic therapy

BACKGROUND

Glioblastoma is one of the most common brain tumors in adult populations, usually carrying a poor prognosis. While several studies have researched the impact of anti-angiogenic therapies, especially anti-VEFG treatments in glioblastoma, few have attempted to assess its progress using imaging studies.

PURPOSE

We attempted to analyze whether relative cerebral blood volume (rCBV) from dynamic susceptibility-weighted contrast-enhanced MRI (DSC-MRI) could predict response in patients with glioblastoma undergoing Bevacizumab (BVZ) treatment.

METHODS

We performed a retrospective study evaluating patients with recurrent glioblastoma receiving anti-angiogenic therapy with BVZ between 2012 and 2017 in our institution. Patients were scheduled for routine MRIs at baseline and first-month follow-up visits. Studies were processed for DSC-MRI, cT1, and FLAIR images, from which relative cerebral blood volume measurements were obtained. We assessed patient response using the Response Assessment in Neuro-Oncology (RANO) working group criteria and overall survival.

RESULTS

40 patients were included in the study and were classified as Bevacizumab responders and non-responders. The average rCBV before treatment was 4.5 for both groups, and average rCBV was 2.5 for responders and 5.4 for non-responders. ROC curve set a cutoff point of 3.7 for rCBV predictive of response to BVZ. Cox Multivariate analysis only showed rCBV as a predictive factor of OS.

CONCLUSION

A statistically significant difference was found in rCBV between patients who responded and those who did not respond to BVZ treatment. rCBV may be a low-cost and effective marker to assess response to Bevacizumab treatment in GBM.

Taming Glioblastoma in "Real Time": Integrating Multimodal Advanced Neuroimaging/AI Tools Towards Creating a Robust and Therapy Agnostic Model for Response Assessment in Neuro-Oncology

The highly aggressive nature of glioblastoma carries a dismal prognosis despite aggressive multimodal therapy. Alternative treatment regimens, such as immunotherapies, are known to intensify the inflammatory response in the treatment field. Follow-up imaging in these scenarios often mimics disease progression on conventional MRI, making accurate evaluation extremely challenging. To this end, revised criteria for assessment of treatment response in high-grade gliomas were successfully proposed by the RANO Working Group to distinguish pseudoprogression from true progression, with intrinsic constraints related to the postcontrast T1-weighted MRI sequence. To address these existing limitations, our group proposes a more objective and quantifiable "treatment agnostic" model, integrating into the RANO criteria advanced multimodal neuroimaging techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility contrast-perfusion weighted imaging (DSC-PWI), dynamic contrast enhanced (DCE)-MRI, MR spectroscopy, and amino acid-based positron emission tomography (PET) imaging tracers, along with artificial intelligence (AI) tools (radiomics, radiogenomics, and radiopathomics) and molecular information to address this complex issue of treatment-related changes versus tumor progression in "real-time", particularly in the early posttreatment window. Our perspective delineates the potential of incorporating multimodal neuroimaging techniques to improve consistency and automation for the assessment of early treatment response in neuro-oncology.

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 10⁶ C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

The relationship between radiation dose and bevacizumab-related imaging abnormality in patients with brain tumors: A voxel-wise normal tissue complication probability (NTCP) analysis

PURPOSE

Bevacizumab-related imaging abnormality (BRIA), appearing as areas of restricted diffusion on magnetic resonance imaging (MRI) and representing atypical coagulative necrosis pathologically, has been observed in patients with brain tumors receiving radiotherapy and bevacizumab. We investigated the role of cumulative radiation dose in BRIA development in a voxel-wise analysis.

METHODS

Patients (n = 18) with BRIA were identified. All had high-grade gliomas or brain metastases treated with radiotherapy and bevacizumab. Areas of BRIA were segmented semi-automatically on diffusion-weighted MRI with apparent diffusion coefficient (ADC) images. To avoid confounding by possible tumor, hypoperfusion was confirmed with perfusion imaging. ADC images and radiation dose maps were co-registered to a high-resolution T1-weighted MRI and registration accuracy was verified. Voxel-wise normal tissue complication probability analyses were performed using a logistic model analyzing the relationship between cumulative voxel equivalent total dose in 2 Gy fractions (EQD2) and BRIA development at each voxel. Confidence intervals for regression model predictions were estimated with bootstrapping.

RESULTS

Among 18 patients, 39 brain tumors were treated. Patients received a median of 4.5 cycles of bevacizumab and 1-4 radiation courses prior to BRIA appearance. Most (64%) treated tumors overlapped with areas of BRIA. The median proportion of each BRIA region of interest volume overlapping with tumor was 98%. We found a dose-dependent association between cumulative voxel EQD2 and the relative probability of BRIA (β0 = -5.1, β1 = 0.03 Gy-1, γ = 1.3).

CONCLUSIONS

BRIA is likely a radiation dose-dependent phenomenon in patients with brain tumors receiving bevacizumab and radiotherapy. The combination of radiation effects and tumor microenvironmental factors in potentiating BRIA in this population should be further investigated.

Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study

The purpose of the present pilot study was to estimate T1 and T2 metric values derived simultaneously from a new, rapid Magnetic Resonance Fingerprinting (MRF) technique, as well as to assess their ability to characterize-brain metastases (BM) and normal-appearing brain tissues. Fourteen patients with BM underwent MRI, including prototype MRF, on a 3T scanner. In total, 108 measurements were analyzed: 42 from solid parts of BM's (21 each on T1 and T2 maps) and 66 from normal-appearing brain tissue (11 ROIs each on T1 and T2 maps for gray matter [GM], white matter [WM], and cerebrospinal fluid [CSF]). The BM's mean T1 and T2 values differed significantly from normal-appearing WM (p < 0.05). The mean T1 values from normal-appearing GM, WM, and CSF regions were 1205 ms, 840 ms, and 4233 ms, respectively. The mean T2 values were 108 ms, 78 ms, and 442 ms, respectively. The mean T1 and T2 values for untreated BM (n = 4) were 2035 ms and 168 ms, respectively. For treated BM (n = 17) the T1 and T2 values were 2163 ms and 141 ms, respectively. MRF technique appears to be a promising and rapid quantitative method for the characterization of free water content and tumor morphology in BMs.

Perfusion and diffusion-weighted imaging parameters: Comparison between pre- and postbiopsy MRI for high-grade glioma

We aimed to evaluate the differences in dynamic susceptibility contrast (DSC)- magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) parameters between the pre- and postbiopsy MRI obtained before treatment in patients with diffuse midline glioma, H3K27-altered. The data of 25 patients with pathologically proven diffuse midline glioma, H3K27-altered, were extracted from our hospital's database between January 2017 and August 2021. Twenty (median age, 13 years; range, 3-52 years; 12 women) and 8 (13.5 years; 5-68 years; 1 woman) patients underwent preoperative DSC-MRI and DWI before and after biopsy, respectively. The normalized corrected relative cerebral blood volume (ncrCBV), normalized relative cerebral blood flow (nrCBF), and normalized maximum, mean, and minimum apparent diffusion coefficient (ADC) were calculated using the volumes-of-interest of the tumor and normal-appearing reference region. The macroscopic postbiopsy changes (i.e., biopsy tract, tissue defect, and hemorrhage) were meticulously excluded from the postbiopsy measurements. The DSC-MRI and DWI parameters of the pre- and postbiopsy groups were compared using the Mann-Whitney U test. The ncrCBV was significantly lower in the postbiopsy group than in the prebiopsy group [prebiopsy group: median 1.293 (range, 0.513 to 2.547) versus postbiopsy group: 0.877 (0.748 to 1.205), P = .016]. No significant difference was observed in the nrCBF and normalized ADC values, although the median nrCBF was lower in the postbiopsy group. The DSC-MRI parameters differed between the pre- and postbiopsy MRI obtained pretreatment, although the macroscopic postbiopsy changes were carefully excluded from the analysis. The results emphasize the potential danger of integrating and analyzing DSC-MRI parameters derived from pre- and postbiopsy MRI.

Pretreatment ADC Histogram Analysis as a Prognostic Imaging Biomarker for Patients with Recurrent Glioblastoma Treated with Bevacizumab: A Systematic Review and Meta-analysis

BACKGROUND

The mean ADC value of the lower Gaussian curve (ADC_L) derived from the bi-Gaussian curve-fitting histogram analysis has been reported as a predictive/prognostic imaging biomarker in patients with recurrent glioblastoma treated with bevacizumab; however, its systematic summary has been lacking.

PURPOSE

We applied a systematic review and meta-analysis to investigate the predictive/prognostic performance of ADC_L in patients with recurrent glioblastoma treated with bevacizumab.

DATA SOURCES

We performed a literature search using PubMed, Scopus, and EMBASE.

STUDY SELECTION

A total of 1344 abstracts were screened, of which 83 articles were considered potentially relevant. Data were finally extracted from 6 studies including 578 patients.

DATA ANALYSIS

Forest plots were generated to illustrate the hazard ratios of overall survival and progression-free survival. The heterogeneity across the studies was assessed using the Cochrane Q test and I² values.

DATA SYNTHESIS

The pooled hazard ratios for overall survival and progression-free survival in patients with an ADC_L lower than the cutoff values were 1.89 (95% CI, 1.53-2.31) and 1.98 (95% CI, 1.54-2.55) with low heterogeneity among the studies. Subgroup analysis of the bevacizumab-free cohort showed a pooled hazard ratio for overall survival of 1.20 (95% CI, 1.08-1.34) with low heterogeneity.

LIMITATIONS

The conclusions are limited by the difference in the definition of recurrence among the included studies.

CONCLUSIONS

This systematic review with meta-analysis supports the prognostic value of ADC_L in patients with recurrent glioblastoma treated with bevacizumab, with a low ADC_L demonstrating decreased overall survival and progression-free survival. On the other hand, the predictive role of ADC_L for bevacizumab treatment was not confirmed.

Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18-80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

Diffusion Magnetic Resonance Imaging Phenotypes Predict Overall Survival Benefit From Bevacizumab or Surgery in Recurrent Glioblastoma With Large Tumor Burden

BACKGROUND

Diffusion magnetic resonance (MR) characteristics are a predictive imaging biomarker for survival benefit in recurrent glioblastoma treated with anti-vascular endothelial growth factor (VEGF) therapy; however, its use in large volume recurrence has not been evaluated.

OBJECTIVE

To determine if diffusion MR characteristics can predict survival outcomes in patients with large volume recurrent glioblastoma treated with bevacizumab or repeat resection.

METHODS

A total of 32 patients with large volume (>20 cc or > 3.4 cm diameter) recurrent glioblastoma treated with bevacizumab and 35 patients treated with repeat surgery were included. Pretreatment tumor volume and apparent diffusion coefficient (ADC) histogram analysis were used to phenotype patients as having high (>1.24 μm2/ms) or low (<1.24 μm2/ms) ADCL, the mean value of the lower peak in a double Gaussian model of the ADC histogram within the contrast enhancing tumor.

RESULTS

In bevacizumab and surgical cohorts, volume was correlated with overall survival (Bevacizumab: P = .009, HR = 1.02; Surgical: P = .006, HR = 0.96). ADCL was an independent predictor of survival in the bevacizumab cohort (P = .049, HR = 0.44), but not the surgical cohort (P = .273, HR = 0.67). There was a survival advantage of surgery over bevacizumab in patients with low ADCL (P = .036, HR = 0.43) but not in patients with high ADCL (P = .284, HR = 0.69).

CONCLUSION

Pretreatment diffusion MR imaging is an independent predictive biomarker for overall survival in recurrent glioblastoma with a large tumor burden. Large tumors with low ADCL have a survival benefit when treated with surgical resection, whereas large tumors with high ADCL may be best managed with bevacizumab.

Diffusion and Perfusion MRI Predicts Response Preceding and Shortly After Radiosurgery to Brain Metastases: A Pilot Study

BACKGROUND AND PURPOSE

To determine the ability of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict long-term response of brain metastases prior to and within 72 hours of stereotactic radiosurgery (SRS).

METHODS

In this prospective pilot study, multiple b-value DWI and T1-weighted DCE-MRI were performed in patients with brain metastases before and within 72 hours following SRS. Diffusion-weighted images were analyzed using the monoexponential and intravoxel incoherent motion (IVIM) models. DCE-MRI data were analyzed using the extended Tofts pharmacokinetic model. The parameters obtained with these methods were correlated with brain metastasis outcomes according to modified Response Assessment in Neuro-Oncology Brain Metastases criteria.

RESULTS

We included 25 lesions from 16 patients; 16 patients underwent pre-SRS MRI and 12 of 16 patients underwent both pre- and early (within 72 hours) post-SRS MRI. The perfusion fraction (f) derived from IVIM early post-SRS was higher in lesions demonstrating progressive disease than in lesions demonstrating stable disease, partial response, or complete response (q = .041). Pre-SRS extracellular extravascular volume fraction, v_e , and volume transfer coefficient, K^trans , derived from DCE-MRI were higher in nonresponders versus responders (q = .041).

CONCLUSIONS

Quantitative DWI and DCE-MRI are feasible imaging methods in the pre- and early (within 72 hours) post-SRS evaluation of brain metastases. DWI- and DCE-MRI-derived parameters demonstrated physiologic changes (tumor cellularity and vascularity) and offer potentially useful biomarkers that can predict treatment response. This allows for initiation of alternate therapies within an effective time window that may help prevent disease progression.

Advanced magnetic resonance imaging to support clinical drug development for malignant glioma

Even though the treatment options and survival of patients with glioblastoma multiforme (GBM), the most common type of malignant glioma, have improved over the past decade, there is still a high unmet medical need to develop novel therapies. Complexity in pathology and therapy require biomarkers to characterize tumors, to define malignant and active areas, to assess disease prognosis, and to quantify and monitor therapy response. While conventional magnetic resonance imaging (MRI) techniques have improved these assessments, limitations remain. In this review, we evaluate the role of various non-invasive biomarkers based on advanced structural and functional MRI techniques in the context of GBM drug development over the past 5 years.

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review

Simple Summary

An accurate survival analysis is crucial for disease management in glioblastoma (GBM) patients. Due to the ability of the diffusion MRI techniques of providing a quantitative assessment of GBM tumours, an ever-growing number of studies aimed at investigating the role of diffusion MRI metrics in survival prediction of GBM patients. Since the role of diffusion MRI in prediction and evaluation of survival outcomes has not been fully addressed and results are often controversial or unsatisfactory, we performed this systematic review in order to collect, summarize and evaluate all studies evaluating the role of diffusion MRI metrics in predicting survival in GBM patients. We found that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

Abstract

Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

Early assessment of recurrent glioblastoma response to bevacizumab treatment by diffusional kurtosis imaging: a preliminary report

PURPOSE

The purpose of this preliminary study is to apply diffusional kurtosis imaging to assess the early response of recurrent glioblastoma to bevacizumab treatment.

METHODS

This prospective cohort study included 10 patients who had been diagnosed with recurrent glioblastoma and scheduled to receive bevacizumab treatment. Diffusional kurtosis images were obtained from all the patients 0-7 days before (pre-bevacizumab) and 28 days after (post-bevacizumab) initiating bevacizumab treatment. The mean, 10th, and 90th percentile values were derived from the histogram of diffusional kurtosis imaging metrics in enhancing and non-enhancing lesions, selected on post-contrast T1-weighted and fluid-attenuated inversion recovery images. Correlations of imaging measures with progression-free survival and overall survival were evaluated using Spearman's rank correlation coefficient. The significance level was set at P < 0.05.

RESULTS

Higher pre-bevacizumab non-enhancing lesion volume was correlated with poor overall survival (r = -0.65, P = 0.049). Higher post-bevacizumab mean diffusivity and axial diffusivity (D_∥, D_∥10% and D_∥90%) in non-enhancing lesions were correlated with poor progression-free survival (r = -0.73, -0.83, -0.71 and -0.85; P < 0.05). Lower post-bevacizumab axial kurtosis (K_∥10%) in non-enhancing lesions was correlated with poor progression-free survival (r = 0.81, P = 0.008).

CONCLUSIONS

This preliminary study demonstrates that diffusional kurtosis imaging metrics allow the detection of tissue changes 28 days after initiating bevacizumab treatment and that they may provide information about tumor progression.

Comparison between MRI-derived ADC maps and 18FLT-PET in pre-operative glioblastoma

BACKGROUND AND PURPOSE

Among principal MRI sequences used for a better pre-therapeutic characterization of glioblastoma (GBM), DWI-derived ADC is expected to be a good parameter for the evaluation of cellularity, due to restricted water diffusivity. We aimed here to compare ADC maps to ¹⁸FLT-PET, a proliferation tracer, in GBM cases.

MATERIALS AND METHODS

Patients underwent ¹⁸FLT-PET, followed by multiparametric magnetic resonance imaging (MRI) just prior to surgery. We analysed in this study twenty GBM confirmed patients. The 5th percentile (5p) of the ADC values were thresholded to define the ADC_min ROI, while the 95th percentile (95p) of the SUV FLT values were used to define the FLT_max ROI. The statistical and spatial correlations between these two groups of ROIs were analyzed.

RESULTS

We did not observe any significant correlations between ADC_min and FLT_max cut-off values (R²=0.0285), neither between ADC_min and FLT_max ROIs (mean Dice=0.09±0.12). Mean ADC values in the FLT_max defined ROI were significantly higher than the values in the ADC_min ROI (P<0.001). Mean FLT values in the FLT_max ROI were significantly higher than the values in the ADC_min ROI (P<0.001).

CONCLUSIONS

When comparing ADC maps to ¹⁸FLT uptake, we did not observe significant anatomical overlap nor correlation, between the regions of low ADC and high FLT disabling to clearly link ADC values to cellular proliferation. The exact significance of ADC maps in GBM has yet to be elaborated.

Survival prediction based on qualitative MRI diffusion signature in patients with recurrent high grade glioma treated with bevacizumab

Background

Bevacizumab was approved by the FDA for the treatment of recurrent or progressive glioblastoma (GBM). Imaging responses are typically assessed by gadolinium-enhanced MRI. We sought to determine the significance of qualitative diffusion signature (manifest as variable degree of dark signal) on ADC maps in recurrent gliomas after treatment with bevacizumab.

Methods

We performed an institutional review board (IRB) approved retrospective study on patients who underwent MRI of the brain after 8 weeks of receiving bevacizumab for recurrent glioma. Patients were divided into three groups based on qualitative diffusion signature: (I) lesion not bright on diffusion weighted imaging (DWI) suggestive of no restricted diffusion (FDR₀); (II) lesion bright on DWI with corresponding homogenous dark signal on apparent diffusion coefficient (ADC) maps suggestive of focal restricted diffusion likely due to bevacizumab induced necrosis (FDR_n); and (III) lesion bright on DWI with corresponding homogenous faint dark signal on ADC maps suggestive of focal restricted diffusion likely due to viable tumor or heterogeneous spectrum of dark and faint dark signals on ADC maps suggestive of focal restricted diffusion likely due to viable tumor surrounding the bevacizumab induced necrosis (FDR_t).

Results

Based on the qualitative signal on diffusion weighted sequences after bevacizumab therapy, total number of patients in group (I) were 14 (36%), in group (II) were 17 (44%); and in group (III) were 8 (20%). The median overall survival (OS) from the time of recurrence in patients belonging to group (II) was 364 days vs. 183 days for those with group (I) vs. 298 days for group (III). On simultaneous comparison of survival differences in all three groups by Kaplan-Meier analysis, group (II) was significant in predicting survival with P values for the log-rank tests <0.033.

Conclusions

In patients with recurrent glioma treated with bevacizumab, the presence of homogenous dark signal (FDR_n) on ADC maps at 8 weeks follow-up MRI correlated with a longer survival. Thus, use of this qualitative diffusion signature in adjunct to contrast enhanced MRI may have the widest potential impact on routine clinical care for patients with recurrent high-grade gliomas. However, prospective studies analysing its predictive value are warranted.

Imaging in neuro-oncology

Imaging plays several key roles in managing brain tumors, including diagnosis, prognosis, and treatment response assessment. Ongoing challenges remain as new therapies emerge and there are urgent needs to find accurate and clinically feasible methods to noninvasively evaluate brain tumors before and after treatment. This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize several key areas of their applications, including improving the accuracy of diagnosis and addressing the challenging clinical problems such as evaluation of pseudoprogression and anti-angiogenic therapy, and rising challenges of imaging with immunotherapy.

An Update on the Approach to the Imaging of Brain Tumors

PURPOSE OF REVIEW

Neuroimaging plays a critical role in diagnosis of brain tumors and in assessment of response to therapy. However, challenges remain, including accurately and reproducibly assessing response to therapy, defining endpoints for neuro-oncology trials, providing prognostic information, and differentiating progressive disease from post-therapeutic changes particularly in the setting of antiangiogenic and other novel therapies.

RECENT FINDINGS

Recent advances in the imaging of brain tumors include application of advanced MRI imaging techniques to assess tumor response to therapy and analysis of imaging features correlating to molecular markers, grade, and prognosis. This review aims to summarize recent advances in imaging as applied to current diagnostic and therapeutic neuro-oncologic challenges.

Apparent diffusion coefficient changes predict survival after intra-arterial bevacizumab treatment in recurrent glioblastoma

PURPOSE

Superselective intra-arterial cerebral infusion (SIACI) of bevacizumab (BV) has emerged as a novel therapy in the treatment of recurrent glioblastoma (GB). This study assessed the use of apparent diffusion coefficient (ADC) in predicting length of survival after SIACI BV and overall survival in patients with recurrent GB.

METHODS

Sixty-five patients from a cohort enrolled in a phase I/II trial of SIACI BV for treatment of recurrent GB were retrospectively included in this analysis. MR imaging with a diffusion-weighted (DWI) sequence was performed before and after treatment. ROIs were manually delineated on ADC maps corresponding to the enhancing and non-enhancing portions of the tumor. Cox and logistic regression analyses were performed to determine which ADC values best predicted survival.

RESULTS

The change in minimum ADC in the enhancing portion of the tumor after SIACI BV therapy was associated with an increased risk of death (hazard ratio = 2.0, 95% confidence interval(CI) [1.04-3.79], p = 0.038), adjusting for age, tumor size, BV dose, and prior IV BV treatments. Similarly, the change in ADC after SIACI BV therapy was associated with greater likelihood of surviving less than 1 year after therapy (odds ratio = 7.0, 95% CI [1.08-45.7], p = 0.04). Having previously received IV BV was associated with increased risk of death (OR 18, 95% CI [1.8-180.0], p = 0.014).

CONCLUSION

In patients with recurrent GB treated with SIACI BV, the change in ADC value after treatment is predictive of overall survival.

Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma

OBJECTIVE

To develop and validate a volume-based, quantitative imaging marker by integrating multi-parametric MR images for predicting glioblastoma survival, and to investigate its relationship and synergy with molecular characteristics.

METHODS

We retrospectively analysed 108 patients with primary glioblastoma. The discovery cohort consisted of 62 patients from the cancer genome atlas (TCGA). Another 46 patients comprising 30 from TCGA and 16 internally were used for independent validation. Based on integrated analyses of T1-weighted contrast-enhanced (T1-c) and diffusion-weighted MR images, we identified an intratumoral subregion with both high T1-c and low ADC, and accordingly defined a high-risk volume (HRV). We evaluated its prognostic value and biological significance with genomic data.

RESULTS

On both discovery and validation cohorts, HRV predicted overall survival (OS) (concordance index: 0.642 and 0.653, P < 0.001 and P = 0.038, respectively). HRV stratified patients within the proneural molecular subtype (log-rank P = 0.040, hazard ratio = 2.787). We observed different OS among patients depending on their MGMT methylation status and HRV (log-rank P = 0.011). Patients with unmethylated MGMT and high HRV had significantly shorter survival (median survival: 9.3 vs. 18.4 months, log-rank P = 0.002).

CONCLUSION

Volume of the high-risk intratumoral subregion identified on multi-parametric MRI predicts glioblastoma survival, and may provide complementary value to genomic information.

KEY POINTS

• High-risk volume (HRV) defined on multi-parametric MRI predicted GBM survival. • The proneural molecular subtype tended to harbour smaller HRV than other subtypes. • Patients with unmethylated MGMT and high HRV had significantly shorter survival. • HRV complements genomic information in predicting GBM survival.

Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma

BACKGROUND AND PURPOSE

Glioblastoma and primary CNS lymphoma dictate different neurosurgical strategies; it is critical to distinguish them preoperatively. However, current imaging modalities do not effectively differentiate them. We aimed to examine the use of DWI and T1-weighted dynamic contrast-enhanced-MR imaging as potential discriminative tools.

MATERIALS AND METHODS

We retrospectively reviewed 18 patients with primary CNS lymphoma and 36 matched patients with glioblastoma with pretreatment DWI and dynamic contrast-enhanced-MR imaging. VOIs were drawn around the tumor on contrast-enhanced T1WI and FLAIR images; these images were transferred onto coregistered ADC maps to obtain the ADC and onto dynamic contrast-enhanced perfusion maps to obtain the plasma volume and permeability transfer constant. Histogram analysis was performed to determine the mean and relative ADC_mean and relative 90th percentile values for plasma volume and the permeability transfer constant. Nonparametric tests were used to assess differences, and receiver operating characteristic analysis was performed for optimal threshold calculations.

RESULTS

The enhancing component of primary CNS lymphoma was found to have significantly lower ADC_mean (1.1 × 10^-3 versus 1.4 × 10^-3; P < .001) and relative ADC_mean (1.5 versus 1.9; P < .001) and relative 90th percentile values for plasma volume (3.7 versus 5.0; P < .05) than the enhancing component of glioblastoma, but not significantly different relative 90th percentile values for the permeability transfer constant (5.4 versus 4.4; P = .83). The nonenhancing portions of glioblastoma and primary CNS lymphoma did not differ in these parameters. On the basis of receiver operating characteristic analysis, mean ADC provided the best threshold (area under the curve = 0.83) to distinguish primary CNS lymphoma from glioblastoma, which was not improved with normalized ADC or the addition of perfusion parameters.

CONCLUSIONS

ADC was superior to dynamic contrast-enhanced-MR imaging perfusion, alone or in combination, in differentiating primary CNS lymphoma from glioblastoma.

Progressing Bevacizumab-Induced Diffusion Restriction Is Associated with Coagulative Necrosis Surrounded by Viable Tumor and Decreased Overall Survival in Patients with Recurrent Glioblastoma

BACKGROUND AND PURPOSE

Patients with recurrent glioblastoma often exhibit regions of diffusion restriction following the initiation of bevacizumab therapy. Studies suggest that these regions represent either diffusion-restricted necrosis or hypercellular tumor. This study explored postmortem brain specimens and a population analysis of overall survival to determine the identity and implications of such lesions.

MATERIALS AND METHODS

Postmortem examinations were performed on 6 patients with recurrent glioblastoma on bevacizumab with progressively growing regions of diffusion restriction. ADC values were extracted from regions of both hypercellular tumor and necrosis. A receiver operating characteristic analysis was performed to define optimal ADC thresholds for differentiating tissue types. A retrospective population study was also performed comparing the overall survival of 64 patients with recurrent glioblastoma treated with bevacizumab. Patients were separated into 3 groups: no diffusion restriction, diffusion restriction that appeared and progressed within 5 months of bevacizumab initiation, and delayed or stable diffusion restriction. An additional analysis was performed assessing tumor O⁶-methylguanine-DNA-methyltransferase methylation.

RESULTS

The optimal ADC threshold for differentiation of hypercellularity and necrosis was 0.736 × 10^-3mm²/s. Progressively expanding diffusion restriction was pathologically confirmed to be coagulative necrosis surrounded by viable tumor. Progressive lesions were associated with the worst overall survival, while stable lesions showed the greatest overall survival (P < .05). Of the 40% of patients with O⁶-methylguanine-DNA-methyltransferase methylated tumors, none developed diffusion-restricted lesions.

CONCLUSIONS

Progressive diffusion-restricted lesions were pathologically confirmed to be coagulative necrosis surrounded by viable tumor and associated with decreased overall survival. Stable lesions were, however, associated with increased overall survival. All lesions were associated with O⁶-methylguanine-DNA-methyltransferase unmethylated tumors.

Effect of Bevacizumab Plus Temozolomide-Radiotherapy for Newly Diagnosed Glioblastoma with Different MGMT Methylation Status: A Meta-Analysis of Clinical Trials

BACKGROUND MGMT methylation status can influence the therapeutic effect and prognosis of glioblastoma (GBM). There are conflicting results from studies evaluating the efficacy of bevacizumab (BV) when it is combined with temozolomide (TMZ) and radiotherapy (RT) in patients diagnosed with GBM with different MGMT methylation status. MATERIAL AND METHODS Data were extracted from publications in PubMed, Embase, and The Cochrane Library, with the last search performed March 23, 2016. Data on overall survival (OS), progression-free survival (PFS), and MGMT methylation status were obtained. RESULTS Data from 3 clinical trials for a total of 1443 subjects were used for this meta-analysis. MGMT methylated and unmethylated patients showed improved PFS in the BV group (pooled HRs, 0.769, 95% CIs 0.604-0.978, P=0.032; 0.675, 95%CIs 0.466-0.979, P=0.038). For patients with either type of GBM, BV did not improve the OS based on the pooled HRs 1.132 (95% CIs 0.876-1.462; P=0.345) for methylated and 1.018 (95% CIs 0.879-1.179; P=0.345) for unmethylated. CONCLUSIONS Bevacizumab combined with temozolomide-radiotherapy correlated with improved PFS for treatment of patients with different MGMT methylation status of newly diagnosed GBM. There was insufficient evidence to determine the synergistic effects of combining BV with TMZ and RT on improving survival in patients with different MGMT methylation status.

Response Assessment in Neuro-Oncology Criteria and Clinical Endpoints

The Response Assessment in Neuro-Oncology (RANO) Working Group is an international multidisciplinary group whose goal is to improve response criteria and define endpoints for neuro-oncology trials. The RANO criteria for high-grade gliomas attempt to address the issues of pseudoprogression, pseudoresponse, and nonenhancing tumor progression. Incorporation of advanced MR imaging may eventually help improve the ability of these criteria to define enhancing and nonenhancing disease better. The RANO group has also developed criteria for neurologic response and evaluation of patients receiving immunologic therapies. RANO criteria have been developed for brain metastases and are in progress for meningiomas, leptomeningeal disease, spinal tumors, and pediatric tumors.

Restriction spectrum imaging predicts response to bevacizumab in patients with high-grade glioma

BACKGROUND

Diffusion-weighted imaging has shown initial promise for evaluating response to bevacizumab in patients with high-grade glioma (HGG). However, it is well recognized that the apparent diffusion coefficient (ADC) is influenced by bevacizumab-induced reductions in edema, which may limit its prognostic value. We demonstrate that an advanced diffusion-weighted imaging technique, restriction spectrum imaging (RSI), improves the evaluation of response to bevacizumab because unlike ADC, RSI is not affected by resolution of edema.

METHODS

RSI and ADC maps were analyzed for 40 patients with HGG prior to and following initiation of bevacizumab. Volumes of interest were drawn for regions of contrast enhancement (CE) and fluid attenuated inversion recovery (FLAIR) hyperintensity and histogram percentiles within volumes of interest were calculated for ADC 10th percentile (ADC-CE_10%, ADC-FLAIR_10%) and for RSI 90th percentile (RSI-CE_90%, RSI-FLAIR_90%). Cox proportional hazard models were used to evaluate the relationship between imaging parameters, progression-free survival (PFS), and overall survival (OS).

RESULTS

An increase in RSI-FLAIR_90% following bevacizumab was the strongest predictor of poor PFS (P= .016) and OS (P= .004), whereas decreases in ADC-FLAIR_10% showed a weaker association with OS only (P= .041). Within the CE region, increases in RSI-CE_90% alone were associated with poorer OS. Correlational analysis revealed that decreases in FLAIR volume were associated with decreases in ADC-FLAIR_10%, but not with changes in RSI-FLAIR_90%.

CONCLUSION

RSI is less influenced by changes in edema, conferring an advantage of RSI over ADC for evaluating response to anti-angiogenic therapy in patients with HGG.