Volumetric assessment of glioblastoma and its predictive value for survival

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.

Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities

BACKGROUND

While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival.

METHODS

A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership.

RESULTS

Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each).

CONCLUSIONS

Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.

Tumour Size and Overall Survival in a Cohort of Patients with Unifocal Glioblastoma: A Uni- and Multivariable Prognostic Modelling and Resampling Study

Published models inconsistently associate glioblastoma size with overall survival (OS). This study aimed to investigate the prognostic effect of tumour size in a large cohort of patients diagnosed with GBM and interrogate how sample size and non-linear transformations may impact on the likelihood of finding a prognostic effect. In total, 279 patients with a IDH-wildtype unifocal WHO grade 4 GBM between 2014 and 2020 from a retrospective cohort were included. Uni-/multivariable association between core volume, whole volume (CV and WV), and diameter with OS was assessed with (1) Cox proportional hazard models +/- log transformation and (2) resampling with 1,000,000 repetitions and varying sample size to identify the percentage of models, which showed a significant effect of tumour size. Models adjusted for operation type and a diameter model adjusted for all clinical variables remained significant (p = 0.03). Multivariable resampling increased the significant effects (p < 0.05) of all size variables as sample size increased. Log transformation also had a large effect on the chances of a prognostic effect of WV. For models adjusted for operation type, 19.5% of WV vs. 26.3% log-WV (n = 50) and 69.9% WV and 89.9% log-WV (n = 279) were significant. In this large well-curated cohort, multivariable modelling and resampling suggest tumour volume is prognostic at larger sample sizes and with log transformation for WV.

Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial

Background

Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods.

Methods

An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images.

Results

There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD.

Conclusions

Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.

Association of MRI Volume Parameters in Predicting Patient Outcome at Time of Initial Diagnosis of Glioblastoma

PURPOSE

Patients with glioblastoma (GBM) may demonstrate varying patterns of infiltration and relapse. Improving the ability to predict these patterns may influence the management strategies at the time of initial diagnosis. This study aims to examine the impact of the ratio (T2/T1) of the non-enhancing volume in T2-weighted images (T2) to the enhancing volume in MRI T1-weighted gadolinium-enhanced images (T1gad) on patient outcome.

METHODS AND MATERIALS

A retrospective audit was performed from established prospective databases in patients managed consecutively with radiation therapy (RT) for GBM between 2016 and 2019. Patient, tumour and treatment-related factors were assessed in relation to outcome. Volumetric data from the initial diagnostic MRI were obtained via the manual segmentation of the T1gd and T2 abnormalities. A T2/T1 ratio was calculated from these volumes. The initial relapse site was assessed on MRI in relation to the site of the original T1gad volume and surgical cavity. The major endpoints were median relapse-free survival (RFS) from the date of diagnosis and site of initial relapse (defined as either local at the initial surgical site or any distance more than 20 mm from initial T1gad abnormality). The analysis was performed for association between known prognostic factors as well as the radiological factors using log-rank tests for subgroup comparisons, with correction for multiple comparisons.

RESULTS

One hundred and seventy-seven patients with GBM were managed consecutively with RT between 2016 and 2019 and were eligible for the analysis. The median age was 62 years. Seventy-four percent were managed under a 60Gy (Stupp) protocol, whilst 26% were on a 40Gy (Elderly) protocol. Major neuroanatomical subsites were Lateral Temporal (18%), Anterior Temporal (13%) and Medial Frontal (10%). Median volumes on T1gd and T2 were 20 cm3 (q1-3:8-43) and 37 cm3 (q1-3: 17-70), respectively. The median T2/T1 ratio was 2.1. For the whole cohort, the median OS was 16.0 months (95%CI:14.1-18.0). One hundred and forty-eight patients have relapsed with a median RFS of 11.4 months (95%CI:10.4-12.5). A component of distant relapse was evident in 43.9% of relapses, with 23.6% isolated relapse. Better ECOG performance Status (p = 0.007), greater extent of resection (p = 0.020), MGMT methylation (p < 0.001) and RT60Gy Dose (p = 0.050) were associated with improved RFS. Although the continuous variable of initial T1gd volume (p = 0.39) and T2 volume (p = 0.23) were not associated with RFS, the lowest T2/T1 quartile (reflecting a relatively lower T2 volume compared to T1gd volume) was significantly associated with improved RFS (p = 0.016) compared with the highest quartile. The lowest T2/T1 ratio quartile was also associated with a lower risk of distant relapse (p = 0.031).

CONCLUSION

In patients diagnosed with GBM, the volumetric parameters of the diagnostic MRI with a ratio of T2 and T1gad abnormality may assist in the prediction of relapse-free survival and patterns of relapse. A further understanding of these relationships has the potential to impact the design of future radiation therapy target volume delineation protocols.

Interstitial Photodynamic Therapy of Glioblastomas: A Long-Term Follow-up Analysis of Survival and Volumetric MRI Data

BACKGROUND

The treatment of glioblastomas, the most common primary malignant brain tumors, with a devastating survival perspective, remains a major challenge in medicine. Among the recently explored therapeutic approaches, 5-aminolevulinic acid (5-ALA)-mediated interstitial photodynamic therapy (iPDT) has shown promising results.

METHODS

A total of 16 patients suffering from de novo glioblastomas and undergoing iPDT as their primary treatment were retrospectively analyzed regarding survival and the characteristic tissue regions discernible in the MRI data before treatment and during follow-up. These regions were segmented at different stages and were analyzed, especially regarding their relation to survival.

RESULTS

In comparison to the reference cohorts treated with other therapies, the iPDT cohort showed a significantly prolonged progression-free survival (PFS) and overall survival (OS). A total of 10 of 16 patients experienced prolonged OS (≥ 24 months). The dominant prognosis-affecting factor was the MGMT promoter methylation status (methylated: median PFS of 35.7 months and median OS of 43.9 months) (unmethylated: median PFS of 8.3 months and median OS of 15.0 months) (combined: median PFS of 16.4 months and median OS of 28.0 months). Several parameters with a known prognostic relevance to survival after standard treatment were not found to be relevant to this iPDT cohort, such as the necrosis-tumor ratio, tumor volume, and posttreatment contrast enhancement. After iPDT, a characteristic structure (iPDT remnant) appeared in the MRI data in the former tumor area.

CONCLUSIONS

In this study, iPDT showed its potential as a treatment option for glioblastomas, with a large fraction of patients having prolonged OS. Parameters of prognostic relevance could be derived from the patient characteristics and MRI data, but they may partially need to be interpreted differently compared to the standard of care.

Evolution and implementation of radiographic response criteria in neuro-oncology

Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice.

Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation

In glioblastoma, the response to treatment assessment is essentially based on the 2D tumor size evolution but remains disputable. Volumetric approaches were evaluated for a more accurate estimation of tumor size. This study included 57 patients and compared two volume measurement methods to determine the size of different glioblastoma regions of interest: the contrast-enhancing area, the necrotic area, the gross target volume and the volume of the edema area. The two methods, the ellipsoid formula (the calculated method) and the manual delineation (the measured method) showed a high correlation to determine glioblastoma volume and a high agreement to classify patients assessment response to treatment according to RANO criteria. This study revealed that calculated and measured methods could be used in clinical practice to estimate glioblastoma volume size and to evaluate tumor size evolution.

Radiomics evaluates the EGFR mutation status from the brain metastasis: a multi-center study

Objective. To develop and externally validate habitat-based MRI radiomics for preoperative prediction of the EGFR mutation status based on brain metastasis (BM) from primary lung adenocarcinoma (LA). Approach. We retrospectively reviewed 150 and 38 patients from hospital 1 and hospital 2 between January 2017 and December 2021 to form a primary and an external validation cohort, respectively. Radiomics features were calculated from the whole tumor (W), tumor active area (TAA) and peritumoral oedema area (POA) in the contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI image. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures (RSs) based on W (RS-W), TAA (RS-TAA), POA (RS-POA) and in combination (RS-Com). The area under receiver operating characteristic curve (AUC) and accuracy analysis were performed to assess the performance of radiomics models. Main results. RS-TAA and RS-POA outperformed RS-W in terms of AUC, ACC and sensitivity. The multi-region combined RS-Com showed the best prediction performance in the primary validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.901 versus 0.699 versus 0.812 versus 0.883) and external validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.900 versus 0.637 versus 0.814 versus 0.842) cohort. Significance. The developed habitat-based radiomics models can accurately detect the EGFR mutation in patients with BM from primary LA, and may provide a preoperative basis for personal treatment planning.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

Newly Diagnosed IDH-Wildtype Glioblastoma and Temporal Muscle Thickness: A Multicenter Analysis

Simple Summary

Cancer associated cachexia and loss of skeletal muscle mass is a negative prognostic marker for survival. Temporal muscle thickness (TMT) is an easily accessible parameter that has been suggested as a prognostic marker in glioblastoma. In this multicenter study we retrospectively analyzed a cohort of 335 patients with newly diagnosed glioblastoma for their overall survival (OS) and TMT. Although previous studies found TMT to be an independent prognostic marker for OS, we could not reproduce these results. Instead, TMT seems to be a surrogate parameter for other epidemiological data.

Abstract

Background: Reduced temporal muscle thickness (TMT) has been discussed as a prognostic marker in IDH-wildtype glioblastoma. This retrospective multicenter study was designed to investigate whether TMT is an independent prognostic marker in newly diagnosed glioblastoma. Methods: TMT was retrospectively measured in 335 patients with newly diagnosed glioblastoma between 1 January 2014 and 31 December 2019 at the University Hospitals of Leipzig and Rostock. The cohort was dichotomized by TMT and tested for association with overall survival (OS) after 12 months by multivariate proportional hazard calculation. Results: TMT of 7.0 mm or more was associated with increased OS (46.3 ± 3.9% versus 36.6 ± 3.9%, p > 0.001). However, the sub-groups showed significant epidemiological differences. In multivariate proportional hazard calculation, patient age (HR 1.01; p = 0.004), MGMT promoter status (HR 0.76; p = 0.002), EOR (HR 0.61), adjuvant irradiation (HR 0.24) and adjuvant chemotherapy (HR 0.40; all p < 0.001) were independent prognostic markers for OS. However, KPS (HR 1.00, p = 0.31), BMI (HR 0.98, p = 0.11) and TMT (HR 1.06; p = 0.07) were not significantly associated with OS. Conclusion: TMT has not appeared as a statistically independent prognostic marker in this cohort of patients with newly diagnosed IDH-wildtype glioblastoma.

Dynamic 11C-Methionine PET-CT: Prognostic Factors for Disease Progression and Survival in Patients with Suspected Glioma Recurrence

Simple Summary

Recurrence after initial treatments is an expected event in glioma patients, particularly for high-grade glioma, with a median progression-free survival of 8–11 weeks. The prognostic evaluation of disease is a crucial step in the planning of therapeutic strategies, in both the primary and recurrence stages of disease. The aim of our retrospective study was to assess the prognostic value of ¹¹C-methionine PET-CT dynamic and semiquantitative parameters in patients with suspected glioma recurrence at MR, in terms of progression-free survival and overall survival. In a population of sixty-seven consecutive patients, both static and kinetic analyses provided parameters (i.e., tumour-to-background ratio and SUVmax associated with time-to-peak, respectively) able to predict both progression-free and overall survival in the whole population and in the high-grade glioma subgroup of patients. Dynamic ¹¹C-methionine PET-CT can be a useful diagnostic tool, in patients with suspicion of glioma recurrence, able to produce significant prognostic indices.

Abstract

Purpose: The prognostic evaluation of glioma recurrence patients is important in the therapeutic management. We investigated the prognostic value of ¹¹C-methionine PET-CT (MET-PET) dynamic and semiquantitative parameters in patients with suspected glioma recurrence. Methods: Sixty-seven consecutive patients who underwent MET-PET for suspected glioma recurrence at MR were retrospectively included. Twenty-one patients underwent static MET-PET; 46/67 underwent dynamic MET-PET. In all patients, SUVmax, SUVmean and tumour-to-background ratio (T/B) were calculated. From dynamic acquisition, the shape and slope of time-activity curves, time-to-peak and its SUVmax (SUVmax_TTP) were extrapolated. The prognostic value of PET parameters on progression-free (PFS) and overall survival (OS) was evaluated using Kaplan–Meier survival estimates and Cox regression. Results: The overall median follow-up was 19 months from MET-PET. Recurrence patients (38/67) had higher SUVmax (p = 0.001), SUVmean (p = 0.002) and T/B (p < 0.001); deceased patients (16/67) showed higher SUVmax (p = 0.03), SUVmean (p = 0.03) and T/B (p = 0.006). All static parameters were associated with PFS (all p < 0.001); T/B was associated with OS (p = 0.031). Regarding kinetic analyses, recurrence (27/46) and deceased (14/46) patients had higher SUVmax_TTP (p = 0.02, p = 0.01, respectively). SUVmax_TTP was the only dynamic parameter associated with PFS (p = 0.02) and OS (p = 0.006). At univariate analysis, SUVmax, SUVmean, T/B and SUVmax_TTP were predictive for PFS (all p < 0.05); SUVmax_TTP was predictive for OS (p = 0.02). At multivariate analysis, SUVmax_TTP remained significant for PFS (p = 0.03). Conclusion: Semiquantitative parameters and SUVmax_TTP were associated with clinical outcomes in patients with suspected glioma recurrence. Dynamic PET-CT acquisition, with static and kinetic parameters, can be a valuable non-invasive prognostic marker, identifying patients with worse prognosis who require personalised therapy.

The Prognostic Role of Peritumoral Edema in Patients with Newly Diagnosed Glioblastoma: A Retrospective Analysis

OBJECTIVE

Previous studies on glioblastomas (GBMs) have not reached a consensus on peritumoral edema (PTE)'s influence on survival. This study evaluated the PTE index's prognostic role in newly diagnosed GBMs using a well-designed method.

METHODS

Selected patients were reviewed after a rigorous screening process. Their general information was obtained from electronic medical records. The imaging metrics (MTD, TTM, TTE) representing tumor diameter, laterality, and PTE extent were obtained by manual measurement in Syngo FastView software. The PTE index was a ratio of TTE to MTD. Multiple variables were evaluated using analysis of variance and Cox regression model.

RESULTS

Of 143 patients, 62 were included in this study. MGMT promoter methylation and tumor laterality were both independent prognostic factors (p = 0.020, 0.042; HR = 0.272, 2.630). The lateral tumors' index was higher than that of the medial tumors (57.7% vs. 42.6%, p = 0.027). Low-index tumors were located in relatively medial positions compared with high-index tumors (TTM, 4.9 vs. 12.8, p = 0.032). This finding indicated that the PTE index tended to increase with tumor laterality. Moreover, the patients with low-index tumors had a significant survival disadvantage in the univariate analysis but not in the multivariate analysis (p = 0.023, 0.220). However, further analysis found that the combination of tumor laterality and PTE statistically stratified the survival outcome. The patients with lateral high-index tumors survived significantly longer (p = 0.022, HR = 1.927).

CONCLUSIONS

In contrast with the previous studies, this study recommends combining PTE and tumor laterality for survival stratification in newly diagnosed GBMs.

On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI

Primary glioblastoma (GBM), IDH-wildtype, especially with multifocal appearance/growth (mGBM), is associated with very poor prognosis. Several clinical parameters have been identified to provide prognostic value in both unifocal GBM (uGBM) and mGBM, but information about the influence of radiological parameters on survival for mGBM cohorts is scarce. This study evaluated the prognostic value of several volumetric parameters derived from magnetic resonance imaging (MRI). Data from the Department of Neurosurgery, Leipzig University Hospital, were retrospectively analyzed. Patients treated between 2014 and 2019, aged older than 18 years and with adequate peri-operative MRI were included. Volumetric assessment was performed manually. One hundred and eighty-three patients were included. Survival of patients with mGBM was significantly shorter (p < 0.0001). Univariate analysis revealed extent of resection, adjuvant therapy regimen, residual tumor volume, tumor necrosis volume and ratio of tumor necrosis to initial volume as statistically significant for overall survival. In multivariate Cox regression, however, only EOR (for uGBM and the entire cohort) and adjuvant therapy were independently significant for survival. Decreased ratio of tumor necrosis to initial tumor volume and extent of resection were associated with prolonged survival in mGBM but failed to achieve statistical significance in multivariate analysis.

Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

BACKGROUND

Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network-assisted segmentation is correlated with survival.

METHODS

Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival.

RESULTS

Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82-0.98) compared to 0.91 (IQR, 0.83-0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63-0.95) compared to 0.81 (IQR, 0.69-0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74-0.98) compared to 0.83 (IQR, 0.78-0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47-0.97) compared to 0.67 (IQR, 0.42-0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67-13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06-32.12], corrected p = 0.011) were independently associated with overall survival.

CONCLUSION

Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance.

Partial resection offers an overall survival benefit over biopsy in MGMT-unmethylated IDH-wildtype glioblastoma patients

Background Isocitrate dehydrogenase (IDH)-wildtype glioblastoma patients with O⁶-methylguanine-DNA-methyltransferase (MGMT)-unmethylated tumors have the worst outcome of all glioblastoma patients. The overall survival (OS) benefit of partial resection of glioblastoma compared to biopsy only remains controversial specifically in relation to molecular factors. In this report, we analyzed the effect of incomplete resection on OS compared to biopsy only in a cohort of IDH-wildtype glioblastoma patients who were uniformly treated with temozolomide-based chemoradiotherapy (TMZ-CR) after surgery. Material & Methods A retrospective study was conducted including only glioblastoma patients who were treated with TMZ-CR after surgery from two centers. Surgical groups were defined as biopsy only, partial resection (PR) or gross total resection depending on the presence of contrast-enhancing tumor on postoperative imaging. IDH-mutation was determined using next generation sequencing technique and MGMT-methylation was analyzed with semi-quantitative methylation-specific polymerase chain reaction. Next to descriptive statistics, univariate and multivariate survival analyses were performed using Kaplan-Meier estimates and Cox regression models. Results In total, 159 patients were included. 37 patients underwent biopsy only and 73 partial resections. 99 patients (62.3%) harbored unmethylated tumors. Median OS for the whole patient group was 13.4 months. In the subgroup of patients with unmethylated tumors, PR yielded a median OS of 12.2 months vs 7.6 months for biopsy patients (P = 0.003). PR proved an independent beneficial prognostic factor in multivariate Cox regression model, together with age, Karnofsky Performance Score and MGMT-methylation. Conclusion In IDH-wildtype glioblastoma patients with MGMT-unmethylated tumors, treated with chemoradiotherapy after surgery, PR yields a significant OS benefit compared to biopsy.

Intraoperative MRI for newly diagnosed supratentorial glioblastoma: a multicenter-registry comparative study to conventional surgery

OBJECTIVE

Intraoperative MRI (iMRI) is used in the surgical treatment of glioblastoma, with uncertain effects on outcomes. The authors evaluated the impact of iMRI on extent of resection (EOR) and overall survival (OS) while controlling for other known and suspected predictors.

METHODS

A multicenter retrospective cohort of 640 adult patients with newly diagnosed supratentorial glioblastoma who underwent resection was evaluated. iMRI was performed in 332/640 cases (51.9%). Reviews of MRI features and tumor volumetric analysis were performed on a subsample of cases (n = 286; 110 non-iMRI, 176 iMRI) from a single institution.

RESULTS

The median age was 60.0 years (mean 58.5 years, range 20.5-86.3 years). The median OS was 17.0 months (95% CI 15.6-18.4 months). Gross-total resection (GTR) was achieved in 403/640 cases (63.0%). Kaplan-Meier analysis of 286 cases with volumetric analysis for EOR (grouped into 100%, 95%-99%, 80%-94%, and 50%-79%) showed longer OS for 100% EOR compared to all other groups (p < 0.01). Additional resection after iMRI was performed in 104/122 cases (85.2%) with initial subtotal resection (STR), leading to a 6.3% mean increase in EOR and a 2.2-cm3 mean decrease in tumor volume. For iMRI cases with volumetric analysis, the GTR rate increased from 54/176 (30.7%) on iMRI to 126/176 (71.5%) postoperatively. The EOR was significantly higher in the iMRI group for intended GTR and STR groups (p = 0.02 and p < 0.01, respectively). Predictors of GTR on multivariate logistic regression included iMRI use and intended GTR. Predictors of shorter OS on multivariate Cox regression included older age, STR, isocitrate dehydrogenase 1 (IDH1) wild type, no O 6-methylguanine DNA methyltransferase (MGMT) methylation, and no Stupp therapy. iMRI was a significant predictor of OS on univariate (HR 0.82, 95% CI 0.69-0.98; p = 0.03) but not multivariate analyses. Use of iMRI was not associated with an increased rate of new permanent neurological deficits.

CONCLUSIONS

GTR increased OS for patients with newly diagnosed glioblastoma after adjusting for other prognostic factors. iMRI increased EOR and GTR rate and was a significant predictor of GTR on multivariate analysis; however, iMRI was not an independent predictor of OS. Additional supporting evidence is needed to determine the clinical benefit of iMRI in the management of glioblastoma.

Assessment of Pre-operative Measurements of Tumor Size by MRI Methods as Survival Predictors in Wild Type IDH Glioblastoma

Objective: We evaluate the performance of three MRI methods to determine non-invasively tumor size, as overall survival (OS) and Progression Free Survival (PFS) predictors, in a cohort of wild type, IDH negative, glioblastoma patients. Investigated protocols included bidimensional (2D) diameter measurements, and three-dimensional (3D) estimations by the ellipsoid or semi-automatic segmentation methods.

Methods: We investigated OS in a cohort of 44 patients diagnosed with wild type IDH glioblastoma (58.2 ± 11.4 years, 1.9/1 male/female) treated with neurosurgical resection followed by adjuvant chemo and radiotherapy. Pre-operative MRI images were evaluated to determine tumor mass area and volume, gadolinium enhancement volume, necrosis volume, and FLAIR-T₂ hyper-intensity area and volume. We implemented then multivariate Cox statistical analysis to select optimal predictors for OS and PFS.

Results: Median OS was 16 months (1–42 months), ranging from 9 ± 2.4 months in patients over 65 years, to 18 ± 1.6 months in younger ones. Patients with tumors carrying O⁶-methylguanin-DNA-methyltransferase (MGMT) methylation survived 30 ± 5.2 vs. 13 ± 2.5 months in non-methylated. Our study evidenced high and positive correlations among the results of the three methods to determine tumor size. FLAIR-T₂ hyper-intensity areas (2D) and volumes (3D) were also similar as determined by the three methods. Cox proportional hazards analysis with the 2D and 3D methods indicated that OS was associated to age ≥ 65 years (HR 2.70, 2.94, and 3.16), MGMT methylation (HR 2.98, 3.07, and 2.90), and FLAIR-T₂ ≥ 2,000 mm² or ≥60 cm³ (HR 4.16, 3.93, and 3.72), respectively. Other variables including necrosis, tumor mass, necrosis/tumor ratio, and FLAIR/tumor ratio were not significantly correlated with OS.

Conclusion: Our results reveal a high correlation among measurements of tumor size performed with the three methods. Pre-operative FLAIR-T₂ hyperintensity area and volumes provided, independently of the measurement method, the optimal neuroimaging features predicting OS in primary glioblastoma patients, followed by age ≥ 65 years and MGMT methylation.

State-of-the-art imaging for glioma surgery

Diffuse gliomas are infiltrative primary brain tumors with a poor prognosis despite multimodal treatment. Maximum safe resection is recommended whenever feasible. The extent of resection (EOR) is positively correlated with survival. Identification of glioma tissue during surgery is difficult due to its diffuse nature. Therefore, glioma resection is imaging-guided, making the choice for imaging technique an important aspect of glioma surgery. The current standard for resection guidance in non-enhancing gliomas is T2 weighted or T2w-fluid attenuation inversion recovery magnetic resonance imaging (MRI), and in enhancing gliomas T1-weighted MRI with a gadolinium-based contrast agent. Other MRI sequences, like magnetic resonance spectroscopy, imaging modalities, such as positron emission tomography, as well as intraoperative imaging techniques, including the use of fluorescence, are also available for the guidance of glioma resection. The neurosurgeon’s goal is to find the balance between maximizing the EOR and preserving brain functions since surgery-induced neurological deficits result in lower quality of life and shortened survival. This requires localization of important brain functions and white matter tracts to aid the pre-operative planning and surgical decision-making. Visualization of brain functions and white matter tracts is possible with functional MRI, diffusion tensor imaging, magnetoencephalography, and navigated transcranial magnetic stimulation. In this review, we discuss the current available imaging techniques for the guidance of glioma resection and the localization of brain functions and white matter tracts.