Diagnostic Accuracy of Neuroimaging to Delineate Diffuse Gliomas within the Brain: A Meta-Analysis

Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas

PURPOSE

Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images.

METHODS

We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of vision transformer (ViT) layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2-fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N = 400), validation (N = 50), and test (N = 51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models with the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE).

RESULTS

The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT-PSNR: 31.0 ± 2.1, MSE: 0.009 ± 0.0005, SSIM: 0.950 ± 0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR-ViT model performs favorably against the ground truth data.

CONCLUSION

We show that high-quality ADC maps can be synthesized from structural MRI using a MPR-ViT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable.

Glioblastoma stem cell metabolism and immunity

Despite enormous efforts being invested in the development of novel therapies for brain malignancies, there remains a dire need for effective treatments, particularly for pediatric glioblastomas. Their poor prognosis has been attributed to the fact that conventional therapies target tumoral cells, but not glioblastoma stem cells (GSCs). GSCs are characterized by self-renewal, tumorigenicity, poor differentiation, and resistance to therapy. These characteristics represent the fundamental tools needed to recapitulate the tumor and result in a relapse. The mechanisms by which GSCs alter metabolic cues and escape elimination by immune cells are discussed in this article, along with potential strategies to harness effector immune cells against GSCs. As cellular immunotherapy is making significant advances in a variety of cancers, leveraging this underexplored reservoir may result in significant improvements in the treatment options for brain malignancies.

Glioma Type Prediction with Dynamic Contrast-Enhanced MR Imaging and Diffusion Kurtosis Imaging-A Standardized Multicenter Study

The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2-4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900-1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702-0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700-0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization's (WHO) classification.

Qualitative MR features to identify non-enhancing tumors within glioblastoma's T2-FLAIR hyperintense lesions

PURPOSE

To identify qualitative MRI features of non-(contrast)-enhancing tumor (nCET) in glioblastoma's T2-FLAIR hyperintense lesion.

METHODS

Thirty-three histologically confirmed glioblastoma patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and 11C-methionine positron emission tomography (Met-PET) were available were included in this study. Met-PET was utilized as a surrogate for tumor burden. Imaging features for identifying nCET were searched by qualitative examination of 156 targets. A new scoring system to identify nCET was established and validated by two independent observers.

RESULTS

Three imaging features were found helpful for identifying nCET; "Bulky gray matter involvement", "Around the rim of contrast-enhancement (Around-rim)," and "High-intensity on T1WI and low-intensity on T2WI (HighT1LowT2)" resulting in an nCET score = 2 × Bulky gray matter involvement - 2 × Around-rim + HighT1LowT2 + 2. The nCET score's classification performances of two independent observers measured by AUC were 0.78 and 0.80, with sensitivities and specificities using a threshold of four being 0.443 and 0.771, and 0.916 and 0.768, respectively. The weighted kappa coefficient for the nCET score was 0.946.

CONCLUSION

The current investigation demonstrated that qualitative assessments of glioblastoma's MRI might help identify nCET in T2/FLAIR high-intensity lesions. The novel nCET score is expected to aid in expanding treatment targets within the T2/FLAIR high-intensity lesions.

Application of 7T MRS to High-Grade Gliomas

MRS, including single-voxel spectroscopy and MR spectroscopic imaging, captures metabolites in high-grade gliomas. Emerging evidence indicates that 7T MRS may be more sensitive to aberrant metabolic activity than lower-field strength MRS. However, the literature on the use of 7T MRS to visualize high-grade gliomas has not been summarized. We aimed to identify metabolic information provided by 7T MRS, optimal spectroscopic sequences, and areas for improvement in and new applications for 7T MRS. Literature was found on PubMed using "high-grade glioma," "malignant glioma," "glioblastoma," "anaplastic astrocytoma," "7T," "MR spectroscopy," and "MR spectroscopic imaging." 7T MRS offers higher SNR, modestly improved spatial resolution, and better resolution of overlapping resonances. 7T MRS also yields reduced Cramér-Rao lower bound values. These features help to quantify D-2-hydroxyglutarate in isocitrate dehydrogenase 1 and 2 gliomas and to isolate variable glutamate, increased glutamine, and increased glycine with higher sensitivity and specificity. 7T MRS may better characterize tumor infiltration and treatment effect in high-grade gliomas, though further study is necessary. 7T MRS will benefit from increased sample size; reductions in field inhomogeneity, specific absorption rate, and acquisition time; and advanced editing techniques. These findings suggest that 7T MRS may advance understanding of high-grade glioma metabolism, with reduced Cramér-Rao lower bound values and better measurement of smaller metabolite signals. Nevertheless, 7T is not widely used clinically, and technical improvements are necessary. 7T MRS isolates metabolites that may be valuable therapeutic targets in high-grade gliomas, potentially resulting in wider ranging neuro-oncologic applications.

Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning

Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximize tumor removal while preserving healthy brain tissue. However, the lack of a clear tumor boundary hampers the neurosurgeon's ability to accurately detect and resect infiltrating tumor tissue. Nonlinear multiphoton microscopy, in particular higher harmonic generation, enables label-free imaging of excised brain tissue, revealing histological hallmarks within seconds. Here, we demonstrate a real-time deep learning-based pipeline for automated glioma image analysis, matching video-rate image acquisition. We used a custom noise detection scheme, and a fully-convolutional classification network, to achieve on average 79% binary accuracy, 0.77 AUC and 0.83 mean average precision compared to the consensus of three pathologists, on a preliminary dataset. We conclude that the combination of real-time imaging and image analysis shows great potential for intraoperative assessment of brain tissue during tumor surgery.

The Extent of Resection in Gliomas-Evidence-Based Recommendations on Methodological Aspects of Research Design

OBJECTIVE

Modern neurosurgery has established maximal safe resection as a cornerstone in the management of diffuse gliomas. Evaluation of the extent of resection (EOR), and its association with certain outcomes or interventions, heavily depends on an adequate methodology to draw strong conclusions. We aim to identify weaknesses and limitations that may threaten the internal validity and generalizability of studies involving the EOR in patients with glioma and to suggest methodological recommendations that may help mitigate these threats.

METHODS

A systematic search was performed by querying PubMed, Web of Science, and Scopus since inception to April 30, 2021 using PICOS/PRISMA guidelines. Articles were then screened to identify high-impact studies evaluating the EOR in patients diagnosed with diffuse gliomas in accordance with predefined criteria. We identify common weakness and limitations during the evaluation of the EOR in the selected studies and then delineate potential methodological recommendations for future endeavors dealing with the EOR.

RESULTS

We identified 31 high-impact studies and found several research design issues including inconsistencies regarding EOR terminology, measurement, data collection, analysis, and reporting. Although some of these issues were related to now outdated reporting standards, many were still present in recent publications and deserve attention in contemporary and future research.

CONCLUSIONS

There is a current need to focus more attention to the methodological aspects of glioma research. Methodological inconsistencies may introduce weaknesses into the internal validity of the studies and hamper comparative analysis of cohorts from different institutions. We hope our recommendations will eventually help develop stronger methodological designs in future research endeavors.

What Does PET Imaging Bring to Neuro-Oncology in 2022? A Review

Simple Summary

Positron emission tomography (PET) imaging is increasingly used to supplement MRI in the management of patient with brain tumors. In this article, we provide a review of the current place and perspectives of PET imaging for the diagnosis and follow-up of from primary brain tumors such as gliomas, meningiomas and central nervous system lymphomas, as well as brain metastases. Different PET radiotracers targeting different biological processes are used to accurately depict these brain tumors and provide unique metabolic and biologic information. Radiolabeled amino acids such as [¹⁸F]FDOPA or [¹⁸F]FET are used for imaging of gliomas while both [¹⁸F]FDG and amino acids can be used for brain metastases. Meningiomas can be seen with a high contrast using radiolabeled ligands of somatostatin receptors, which they usually carry. Unconventional tracers that allow the study of other biological processes such as cell proliferation, hypoxia, or neo-angiogenesis are currently being studied for brain tumors imaging.

Abstract

PET imaging is being increasingly used to supplement MRI in the clinical management of brain tumors. The main radiotracers implemented in clinical practice include [¹⁸F]FDG, radiolabeled amino acids ([¹¹C]MET, [¹⁸F]FDOPA, [¹⁸F]FET) and [⁶⁸Ga]Ga-DOTA-SSTR, targeting glucose metabolism, L-amino-acid transport and somatostatin receptors expression, respectively. This review aims at addressing the current place and perspectives of brain PET imaging for patients who suffer from primary or secondary brain tumors, at diagnosis and during follow-up. A special focus is given to the following: radiolabeled amino acids PET imaging for tumor characterization and follow-up in gliomas; the role of amino acid PET and [¹⁸F]FDG PET for detecting brain metastases recurrence; [⁶⁸Ga]Ga-DOTA-SSTR PET for guiding treatment in meningioma and particularly before targeted radiotherapy.

Prediction and Visualization of Non-Enhancing Tumor in Glioblastoma via T1w/T2w-Ratio Map

One of the challenges in glioblastoma (GBM) imaging is to visualize non-enhancing tumor (NET) lesions. The ratio of T1- and T2-weighted images (rT1/T2) is reported as a helpful imaging surrogate of microstructures of the brain. This research study investigated the possibility of using rT1/T2 as a surrogate for the T1- and T2-relaxation time of GBM to visualize NET effectively. The data of thirty-four histologically confirmed GBM patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and ¹¹C-methionine positron emission tomography (Met-PET) were available were collected for analysis. Two of them also underwent MR relaxometry with rT1/T2 reconstructed for all cases. Met-PET was used as ground truth with T2-FLAIR hyperintense lesion, with >1.5 in tumor-to-normal tissue ratio being NET. rT1/T2 values were compared with MR relaxometry and Met-PET. rT1/T2 values significantly correlated with both T1- and T2-relaxation times in a logarithmic manner (p < 0.05 for both cases). The distributions of rT1/T2 from Met-PET high and low T2-FLAIR hyperintense lesions were different and a novel metric named Likeliness of Methionine PET high (LMPH) deriving from rT1/T2 was statistically significant for detecting Met-PET high T2-FLAIR hyperintense lesions (mean AUC = 0.556 ± 0.117; p = 0.01). In conclusion, this research study supported the hypothesis that rT1/T2 could be a promising imaging marker for NET identification.

Lessons learned from drug trials in neurofibromatosis: A systematic review

Neurofibromatosis (NF) is the umbrella term for neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2) and schwannomatosis (SWN). EU-PEARL aims to create a framework for platform trials in NF. The aim of this systematic review is to create an overview of recent clinical drug trials in NF, to identify learning points to guide development of the framework. We searched Embase, Medline and Cochrane register of trials on October 1, 2020 for publications of clinical drug trials in NF patients. We excluded publications published before 2010, systematic reviews, secondary analyses and studies with <10 patients. Data was extracted on manifestations studied, study design, phase, number of participating centres and population size. Full-text review resulted in 42 articles: 31 for NF1, 11 for NF2, none for SWN. Most NF1 trials focused on plexiform neurofibromas (32%). Trials in NF2 solely studied vestibular schwannomas. In NF1, single-arm trials (58%) were most common, and the majority was phase II (74%). For NF2 most trials were single-arm (55%) and exclusively phase II. For both diseases, trials were predominantly single-country and included five centres or less. Study population sizes were small, with the majority including ≤50 patients (74%). In conclusion, NF research is dominated by studies on a limited number out of the wide range of manifestations. We need more trials for cutaneous manifestations and high-grade gliomas in NF1, manifestations other than vestibular schwannoma in NF2 and trials for SWN. Drug development in NF may profit from innovative trials on multiple interventions and increased international collaboration.

Magnetic Resonance Relaxometry for Tumor Cell Density Imaging for Glioma: An Exploratory Study via 11C-Methionine PET and Its Validation via Stereotactic Tissue Sampling

Simple Summary

To test the hypothesis that quantitative magnetic resonance relaxometry reflects glioma tumor load within tissue and that it can be an imaging surrogate for visualizing non-contrast-enhancing tumors, we investigated the correlation between T1- and T2-weighted relaxation times, apparent diffusion coefficient (ADC) on magnetic resonance imaging, and ¹¹C-methionine (MET) on positron emission tomography (PET). Moreover, we compared T1- and T2-relaxation times and ADC with tumor cell density (TCD) findings obtained via stereotactic image-guided tissue sampling. A T1-relaxation time of >1850 ms but <3200 ms or a T2-relaxation time of >115 ms but <225 ms under 3 T indicated high MET uptake. The stereotactic tissue sampling findings confirmed that the T1-relaxation time of 1850–3200 ms significantly indicated higher TCD while the T2-relaxation time and ADC did not significantly correlate with the stereotactic tissue sampling findings. However, synthetically synthesized tumor load images from the T1- and T2-relaxation maps were able to visualize MET uptake presented on PET.

Abstract

One of the most crucial yet challenging issues for glioma patient care is visualizing non-contrast-enhancing tumor regions. In this study, to test the hypothesis that quantitative magnetic resonance relaxometry reflects glioma tumor load within tissue and that it can be an imaging surrogate for visualizing non-contrast-enhancing tumors, we investigated the correlation between T1- and T2-weighted relaxation times, apparent diffusion coefficient (ADC) on magnetic resonance imaging, and ¹¹C-methionine (MET) on positron emission tomography (PET). Moreover, we compared the T1- and T2-relaxation times and ADC with tumor cell density (TCD) findings obtained via stereotactic image-guided tissue sampling. Regions that presented a T1-relaxation time of >1850 ms but <3200 ms or a T2-relaxation time of >115 ms but <225 ms under 3 T indicated a high MET uptake. In addition, the stereotactic tissue sampling findings confirmed that the T1-relaxation time of 1850–3200 ms significantly indicated a higher TCD (p = 0.04). However, ADC was unable to show a significant correlation with MET uptake or with TCD. Finally, synthetically synthesized tumor load images from the T1- and T2-relaxation maps were able to visualize MET uptake presented on PET.

On the cutting edge of glioblastoma surgery: where neurosurgeons agree and disagree on surgical decisions

OBJECTIVE

The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity. Standards are lacking for surgical decision-making, and previous studies indicate treatment variations. These shortcomings reflect the need to evaluate larger populations from different care teams. In this study, the authors used probability maps to quantify and compare surgical decision-making throughout the brain by 12 neurosurgical teams for patients with glioblastoma.

METHODS

The study included all adult patients who underwent first-time glioblastoma surgery in 2012-2013 and were treated by 1 of the 12 participating neurosurgical teams. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to identify and compare patient treatment variations. Brain regions with different biopsy and resection results between teams were identified and analyzed for patient functional outcome and survival.

RESULTS

The study cohort consisted of 1087 patients, of whom 363 underwent a biopsy and 724 a resection. Biopsy and resection decisions were generally comparable between teams, providing benchmarks for probability maps of resections and biopsies for glioblastoma. Differences in biopsy rates were identified for the right superior frontal gyrus and indicated variation in biopsy decisions. Differences in resection rates were identified for the left superior parietal lobule, indicating variations in resection decisions.

CONCLUSIONS

Probability maps of glioblastoma surgery enabled capture of clinical practice decisions and indicated that teams generally agreed on which region to biopsy or to resect. However, treatment variations reflecting clinical dilemmas were observed and pinpointed by using the probability maps, which could therefore be useful for quality-of-care discussions between surgical teams for patients with glioblastoma.

Identifying challenges in neurofibromatosis: a modified Delphi procedure

Neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2) and schwannomatosis (SWN) are rare conditions with pronounced variability of clinical expression. We aimed to reach consensus on the most important manifestations meriting the development of drug trials. The five-staged modified Delphi procedure consisted of two questionnaires and a consensus meeting for 40 NF experts, a survey for 63 patient representatives, and a final workshop. In the questionnaires, manifestations were scored on multiple items on a 4-point Likert scale. The highest average scores for NF experts deciding the ‘need for new treatment’ were for malignant peripheral nerve sheath tumour (MPNST) (4,0) and high grade glioma (HGG) (3,9) for NF1; meningioma (3,9) for NF2 and pain (3,9) for SWN. The patient representatives assigned high scores to all manifestations, with plexiform neurofibroma being highest in NF1 (4,0), vestibular schwannoma in NF2 (4,0), and pain in SWN (3,9). Twelve experts participated in the consensus meeting and prioritised manifestations. MPNST was ranked the highest for NF1, followed by benign peripheral nerve sheath tumours. Tumour manifestations received highest ranking in NF2, and pain was the most prominent problem for SWN. Patient representative ratings for NF1 were similar to the experts’ opinions, except that they ranked HGG as the most important manifestation. For NF2 and SWN, the patient representatives agreed with the experts. We conclude that NF experts and patient representatives consent to prioritise development of drug trials for MPNST, benign peripheral nerve sheath tumours, cutaneous manifestations and HGG for NF1; tumours for NF2; and pain for SWN.

Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

The aim of this study was to systematically review the literature concerning the integration of multimodality imaging with artificial intelligence methods for visualization of tumor cell infiltration in glioma patients. The review was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. The literature search was conducted in PubMed, Embase, The Cochrane Library and Web of Science and yielded 1304 results. 14 studies were included in the qualitative analysis. The reference standard for tumor infiltration was either histopathology or recurrence on image follow-up. Critical assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS2). All studies concluded their findings to be of significant value for future clinical practice. Diagnostic test accuracy reached an area under the curve of 0.74–0.91 reported in six studies. There was no consensus with regard to included image modalities, models or training and test strategies. The integration of artificial intelligence with multiparametric imaging shows promise for visualizing tumor cell infiltration in glioma patients. This approach can possibly optimize surgical resection margins and help provide personalized radiotherapy planning.

Improved detection of diffuse glioma infiltration with imaging combinations: a diagnostic accuracy study

Background

Surgical resection and irradiation of diffuse glioma are guided by standard MRI: T2/fluid attenuated inversion recovery (FLAIR)–weighted MRI for non-enhancing and T1-weighted gadolinium-enhanced (T1G) MRI for enhancing gliomas. Amino acid PET has been suggested as the new standard. Imaging combinations may improve standard MRI and amino acid PET. The aim of the study was to determine the accuracy of imaging combinations to detect glioma infiltration.

Methods

We included 20 consecutive adults with newly diagnosed non-enhancing glioma (7 diffuse astrocytomas, isocitrate dehydrogenase [IDH] mutant; 1 oligodendroglioma, IDH mutant and 1p/19q codeleted; 1 glioblastoma IDH wildtype) or enhancing glioma (glioblastoma, 9 IDH wildtype and 2 IDH mutant). Standardized preoperative imaging (T1-, T2-, FLAIR-weighted, and T1G MRI, perfusion and diffusion MRI, MR spectroscopy and O-(2-[18F]-fluoroethyl)-L-tyrosine ([¹⁸F]FET) PET) was co-localized with multiregion stereotactic biopsies preceding resection. Tumor presence in the biopsies was assessed by 2 neuropathologists. Diagnostic accuracy was determined using receiver operating characteristic analysis.

Results

A total of 174 biopsies were obtained (63 from 9 non-enhancing and 111 from 11 enhancing gliomas), of which 129 contained tumor (50 from non-enhancing and 79 from enhancing gliomas). In enhancing gliomas, the combination of apparent diffusion coefficient (ADC) with [¹⁸F]FET PET (area under the curve [AUC], 95% CI: 0.89, 0.79‒0.99) detected tumor better than T1G MRI (0.56, 0.39‒0.72; P < 0.001) and [¹⁸F]FET PET (0.76, 0.66‒0.86; P = 0.001). In non-enhancing gliomas, no imaging combination detected tumor significantly better than standard MRI. FLAIR-weighted MRI had an AUC of 0.81 (0.65–0.98) compared with 0.69 (0.56–0.81; P = 0.019) for [¹⁸F]FET PET.

Conclusion

Combining ADC and [¹⁸F]FET PET detects glioma infiltration better than standard MRI and [¹⁸F]FET PET in enhancing gliomas, potentially enabling better guidance of local therapy.

The diagnostic performance of 99mTc-methionine single-photon emission tomography in grading glioma preoperatively: a comparison with histopathology and Ki-67 indices

OBJECTIVE

To characterize glioma preoperatively using quantitative 99mTc-methionine SPECT and comparison with MR-perfusion/spectroscopy and histopatholgical/Ki-67 scoring.

METHODS

Twenty-nine patients (21M: 8F; mean age 42.3 ± 10.5 years) with clinical and radiological suspicion of glioma assessed by 99mTc-MDM/SPECT and ceMRI. Additionally, 12/29 patients underwent dynamic susceptibility contrast-enhanced (DSCE) MRI and magnetic resonance spectroscopy (MRS) examination. Three patients with benign pathologies were recruited as controls. Histopathological tumor analysis was done in all (n = 29) the patients, and the Ki-67 index was evaluated in 20/29 patients. The target-to-nontarget (T/NT) methionine tumor uptake ratios, normalized cerebral blood volume (nCBV) and metabolites [choline/N-acetyl aspartate (Cho/NAA), Cho/creatine (Cr), Cr/NAA and Cr/Cho) ratios were measured in tumor areas.

RESULTS

On histopathological analysis, 26/29 patients had glioma (G IV-13; G III-04; G II-09). The mean T/NT ratio in G-II was significantly lower (2.46 ± 2.3) than in G-III (7.13 ± 2.2) and G-IV (5.16 ± 1.2). However, the mean ratio was highest (15.9 ± 6.8) in meningioma (n=3). The T/NT cutoff ratio of 3.08 provided 100% sensitivity, 87.5% specificity for discriminating high-grade glioma (HGG) from low-grade glioma (LGG) disease. Likewise, the nCBV cutoff of 2.43 offered 100% sensitivity and 80% specificity. Only the Cho/NAA cutoff value of greater than 3.34 provided reasonable sensitivity and specificity of 85.7% and 80.0% respectively for this differentiation. T/NT ratio correlated significantly with nCBV and Cho/NAA, Cho/Cr ratios but not with Ki-67.

CONCLUSION

Quantitative 99mTc-MDM -SPECT provided high sensitivity and specificity to differentiate HGG versus LGG preoperatively and demonstrated a potential role for the differential diagnosis of glial versus nonglial tumors.

[99mTc]-Bis-Methionine-DTPA Single-Photon Emission Computed Tomography Impacting Glioma Management: A Sensitive Indicator for Postsurgical/Chemoradiotherapy Response Assessment

Background: The present study evaluated the prognostic value of [^99mTc]MDM (bis-methionine-DTPA) follow-up single-photon emission computed tomography (SPECT) imaging for response assessment to chemoradiotherapy in glioma postoperatively. Materials and Methods: One hundred fourteen glioma patients (80 M:34 F) were followed postoperatively by sequential [^99mTc]MDM SPECT, dynamic susceptibility contrast-enhanced (DSCE)-MRI, and magnetic resonance spectroscopy (MRS) at baseline, 6, 12, and 22.5 months postchemoradiotherapy. The quantitative imaging results and the clinical outcome were used for response assessment and for the final diagnosis. The quantitative parameter of [^99mTc]MDM SPECT were also used for survival analysis. Results: A significantly (p = 0.001) lower target to nontarget (T/NT) ratio was observed in responders than in nonresponders. The sensitivity and specificity of [^99mTc]MDM-SPECT for identifying tumor recurrence from radiation necrosis at a cutoff ratio of 1.90 were estimated at 97.9% and 92%. Whereas, the sensitivity and specificity of DSCE-MRI with the normalized cerebral blood volume (nCBV) cutoff of 3.32 for this differentiation was found to be 84.6% and 93.0%. MRS intensity ratios of Cho/NAA and Cho/Cr provided comparatively lower sensitivity of 81.0% and 85.3% and specificity of 73.0% and 73.7%. T/NT ratios correlated with nCBV (r = 0.775, p < 0.001) and to a moderate extent with Cho/NAA ratios (r = 0.467, p = 0.001). [^99mTc]MDM SPECT and DSCE-MRI provided comparable results for predicting response assessment to chemoradiotherapy. There was a final diagnosis in 72 patients, of which 47 cases were tumor recurrence and 25 were radiation necrosis. The Kaplan-Meier analysis indicated that patients with T/NT ratio <1.9 showed prolonged survival (53.8 months) as compared (37.2 months) with those who demonstrated T/NT ratio >1.9 (p = 0.0001). Conclusion: Thus, this low-cost SPECT technique in combination with DSCE-MRI can be used accurately for mapping the disease activity, response assessment, and survival in glioma. [^99mTc]MDM SPECT and DSCE-MRI had the same diagnostic efficacy to detect recurrent/residual tumor and radiation necrosis while MRS was inferior to both the techniques.

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.

Radiology Profile as a Potential Instrument to Differentiate Between Posterior Fossa Ependymoma (PF-EPN) Group A and B

OBJECTIVE

Posterior fossa ependymoma (PF-EPN) was categorized into PF-EPN-A and PF-EPN-B subgroups based on the DNA methylation profiling. PF-EPN-A was reported to have poorer prognosis compared with PF-EPN-B. In this study, we particularly evaluated preoperative imaging to distinguish PF-EPN-A from PF-EPN-B.

METHODS

Sixteen cases of PF-EPN were treated in our institution from 1999 to 2018. The patients were divided into PF-EPN-A and PF-EPN-B groups based on H3K27me3 immunostaining positivity. We evaluated progression-free survival, overall survival, as well as preoperative magnetic resonance imaging and computed tomography scan images in both groups. Based on T1WI and Gd-T1WI magnetic resonance images, the tumor contrast rate was determined from dividing the volume of gadolinium enhanced tumor by the overall tumor volume.

RESULTS

Nine cases (4 male, 5 female) were grouped as PF-EPN-A, and 7 (4 male, 3 female) as PF-EPN-B. The median age of PF-EPN-A and PF-EPN-B were 4 and 43 years old, respectively. In the PF-EPN-A group, the progression-free survival median value was 32.6 months, and the overall survival median was 96.9 months. In contrast, PFS in PF-EPN-B did not reach a median value (P < 0.05) and all the patients were alive (P < 0.05) at the end of the study. With imaging, tumor contrast rate in PF-EPN-B was more than 50% and significantly different from PF-EPN-A (P = 0.0294). Calcification was mainly observed in PF-EPN-A, whereas cystic formation was only seen in PF-EPN-B.

CONCLUSIONS

Contrast rate less than 50%, based on the magnetic resonance images, was characteristic in the PF-EPN-A group. Comparatively, cystic component and absence of calcification were more characteristic in the PF-EPN-B group.

Predictors and early survival outcomes of maximal resection in WHO grade II 1p/19q-codeleted oligodendrogliomas

BACKGROUND

Although surgery plays a crucial diagnostic role in World Health Organization (WHO) grade II 1p/19q-codeleted oligodendrogliomas, the role of maximal tumor surgical resection remains unclear, with early retrospective series limited by lack of molecular classification or appropriate control groups.

METHODS

The characteristics, management, and overall survival (OS) of patients ≥20 years old presenting with histology-proven WHO grade II 1p/19q-codeleted oligodendrogliomas during 2010-2016 were evaluated using the National Cancer Database and validated using multi-institutional data. Patients were stratified by watchful waiting (biopsy only) versus surgical resection. OS was analyzed using Kaplan-Meier methods and risk-adjusted proportional hazards.

RESULTS

Five hundred ninety adults met inclusion criteria, of whom 79.0% (n = 466) underwent surgical resection. Of patient and tumor characteristics, younger patients were more likely to be resected. Achieving gross total resection (GTR; n = 320) was significantly associated with smaller tumors, management at integrated network cancer programs (vs community cancer programs), and Medicare insurance (as compared with no, private, or Medicaid/other government insurance) and independent of other patient or tumor characteristics. In risk-adjusted analyses, GTR, but not subtotal resection (STR), demonstrated improved OS (vs biopsy only: hazard ratio 0.28, 95% CI: 0.09-0.85, P = 0.02).

CONCLUSIONS

WHO grade II 1p/19q-codeleted oligodendrogliomas amenable to resection demonstrated improved OS with GTR, but not STR, compared with biopsy-only watchful waiting. The OS benefits of GTR were independent of age, tumor size, or tumor location. Medicare-insured and integrated network cancer program patients were significantly more likely to have GTR than other patients, suggesting that insurance status and care setting may play important roles in access to timely diagnosis or innovations that improve maximal resection.

Prediction of IDH Status Through MRI Features and Enlightened Reflection on the Delineation of Target Volume in Low-Grade Gliomas

Isocitrate dehydrogenase mutational status defines distinct biologic behavior and clinical outcomes in low-grade gliomas. We sought to determine magnetic resonance imaging characteristics associated with isocitrate dehydrogenase mutational status to evaluate the predictive roles of magnetic resonance imaging features in isocitrate dehydrogenase mutational status and therefore their potential impact on the determination of clinical target volume in radiotherapy. Forty-eight isocitrate dehydrogenase-mutant and 28 isocitrate dehydrogenase–wild-type low-grade gliomas were studied. Isocitrate dehydrogenase mutation was related to more frequency of cortical involvement compared to isocitrate dehydrogenase–wild-type group (34/46 vs 6/24, P = .0001). Peritumoral edema was less frequent in isocitrate dehydrogenase–mutant tumors (32.6% vs 58.3% for isocitrate dehydrogenase–wild-type tumors, P = .0381). Isocitrate dehydrogenase–wild-type tumors were more likely to have a nondefinable border, while isocitrate dehydrogenase–mutant tumors had well-defined borders (66.7% vs 39.1%, P = .0287). Only 8 (17.4%) of 46 of the isocitrate dehydrogenase–mutant tumors demonstrated marked enhancement, while this was 66.7% in isocitrate–wild-type tumors (P < .0001). Choline–creatinine ratio for isocitrate dehydrogenase–wild-type tumors was significantly higher than that for isocitrate dehydrogenase–mutant tumors. In conclusion, frontal location, well-defined border, cortical involvement, less peritumoral edema, lack of enhancement, and low choline–creatinine ratio were predictive for the definition of isocitrate dehydrogenase–mutant low-grade gliomas. Magnetic resonance imaging can provide an advantage in the detection of isocitrate dehydrogenase status indirectly and indicate the need to explore new design for treatment planning in gliomas. Choline–creatinine ratio in magnetic resonance spectroscopy could be a potential more reasonable reference for the new design of delineation of target volume in low-grade gliomas.

The Impact of MRI Features and Observer Confidence on the Treatment Decision-Making for Patients with Untreated Glioma

In a blind, dual-center, multi-observer setting, we here identify the pre-treatment radiologic features by Magnetic Resonance Imaging (MRI) associated with subsequent treatment options in patients with glioma. Study included 220 previously untreated adult patients from two institutions (94 + 126 patients) with a histopathologically confirmed diagnosis of glioma after surgery. Using a blind, cross-institutional and randomized setup, four expert neuroradiologists recorded radiologic features, suggested glioma grade and corresponding confidence. The radiologic features were scored using the Visually AcceSAble Rembrandt Images (VASARI) standard. Results were retrospectively compared to patient treatment outcomes. Our findings show that patients receiving a biopsy or a subtotal resection were more likely to have a tumor with pathological MRI-signal (by T2-weighted Fluid-Attenuated Inversion Recovery) crossing the midline (Hazard Ratio; HR = 1.30 [1.21–1.87], P < 0.001), and those receiving a biopsy sampling more often had multifocal lesions (HR = 1.30 [1.16–1.64], P < 0.001). For low-grade gliomas (N = 50), low observer confidence in the radiographic readings was associated with less chance of a total resection (P = 0.002) and correlated with the use of a more comprehensive adjuvant treatment protocol (Spearman = 0.48, P < 0.001). This study may serve as a guide to the treating physician by identifying the key radiologic determinants most likely to influence the treatment decision-making process.

18F-Fluorocholine PET/CT in the Prediction of Molecular Subtypes and Prognosis for Gliomas

AIM

To study the association of metabolic features of F-fluorocholine in gliomas with histopathological and molecular parameters, progression-free survival (PFS) and overall survival (OS).

METHODS

Prospective multicenter and nonrandomized study (Functional and Metabolic Glioma Analysis). Patients underwent a basal F-fluorocholine PET/CT and were included after histological confirmation of glioma. Histological and molecular profile was assessed: grade, Ki-67, isocitrate dehydrogenase status and 1p/19q codeletion. Patients underwent standard treatment after surgery or biopsy, depending on their clinical situation. Overall survival and PFS were obtained after follow-up. After tumor segmentation of PET images, SUV and volume-based variables, sphericity, surface, coefficient of variation, and multilesionality were obtained. Relations of metabolic variables with histological, molecular profile and prognosis were evaluated using Pearson χ and t test. Receiver operator caracteristic curves were used to obtain the cutoff of PET variables. Survival analysis was performed using Kaplan-Meier and Cox regression analysis.

RESULTS

Forty-five patients were assessed; 38 were diagnosed as having high-grade gliomas. Significant differences of SUV-based variables with isocitrate dehydrogenase status, tumor grade, and Ki-67 were found. Tumor grade, Ki-67, SUVmax, and SUVmean were related to progression. Kaplan-Meier analysis revealed significant associations of SUVmax, SUVmean, and multilesionaly with OS and PFS. SUVmean, sphericity, and multilesionality were independent predictors of OS and PFS in Cox regression analysis.

CONCLUSIONS

Metabolic information obtained from F-fluorocholine PET of patients with glioma may be useful in the prediction of tumor biology and patient prognosis.

Segmentation of gliomas in 18F-fluorocholine PET/CT. A multiapproach study

AIM

Our aim was two-fold, to study the interobserver agreement in tumour segmentation and to search for a reliable methodology to segment gliomas using ¹⁸F-fluorocholine PET/CT.

METHODS

25 patients with glioma, from a prospective and non-randomized study (Functional and Metabolic Glioma Analysis), were included.Interobserver variability in tumour segmentation was assessed using fixed thresholds. Different strategies were used to segment the tumours. First, a semi-automatic tumour segmentation was performed, selecting the best SUVmax-% threshold for each lesion. Next we determined a variable SUVmax-% depending on the SUVmax. Finally a segmentation using a fixed SUVmax threshold was performed. To do so, a sampling of 10 regions of interest (ROI of 2.8cm²) located in the normal brain was performed. The upper value of the sample mean SUVmax±3 SD was used as cut-off. All procedures were tested and classified as effective or not for tumour segmentation by two observer's consensus.

RESULTS

In the pilot segmentation, the mean±SD of SUVmax, SUVmean and optimal SUVmax-% threshold were: 3.64±1.77, 1.32±0.57 and 21.32±8.39, respectively. Optimal SUVmax-% threshold showed a significant association with the SUVmax (Pearson=-0.653, p=.002). However, the linear regression model for the total sample was not good, that supported the division in two homogeneous groups, defining two formulas for predicting the optimal SUVmax-% threshold. As to the third procedure, the obtained value for the mean SUVmax background+3 SD was 0.33. This value allowed segmenting correctly a significant fraction of tumours, although not all.

CONCLUSION

A great interobserver variability in the tumour segmentation was found. None of the methods was able to segment correctly all the gliomas, probably explained by the wide tumour heterogeneity on ¹⁸F-fluorocholine PET/CT.

Diagnosing brain tumours by routine blood tests using machine learning

Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for the diagnosis of brain tumours. We validated the model by retrospective analysis of 68 consecutive brain tumour and 215 control patients presenting to the neurological emergency service. Only patients with head imaging and routine blood test data were included in the validation sample. The sensitivity and specificity of the adapted tumour model in the validation group were 96% and 74%, respectively. Our data demonstrate the feasibility of brain tumour diagnosis from routine blood tests using machine learning. The reported diagnostic accuracy is comparable and possibly complementary to that of imaging studies. The presented machine learning approach opens a completely new avenue in the diagnosis of these grave neurological diseases and demonstrates the utility of valuable information obtained from routine blood tests.

Diagnostic Performance and Prognostic Value of PET/CT with Different Tracers for Brain Tumors: A Systematic Review of Published Meta-Analyses

Background: Several meta-analyses reporting data on the diagnostic performance or prognostic value of positron emission tomography (PET) with different tracers in detecting brain tumors have been published so far. This review article was written to summarize the evidence-based data in these settings. Methods: We have performed a comprehensive literature search of meta-analyses published in the Cochrane library and PubMed/Medline databases (from inception through July 2019) about the diagnostic performance or prognostic value of PET with different tracers in patients with brain tumors. Results: We have summarized the results of 24 retrieved meta-analyses on the use of PET or PET/computed tomography (CT) with different tracers in brain tumors. The tracers included were: fluorine-18 fluorodeoxyglucose (¹⁸F-FDG), carbon-11 methionine (¹¹C-methionine), fluorine-18 fluoroethyltyrosine (¹⁸F-FET), fluorine-18 dihydroxyphenylalanine (¹⁸F-FDOPA), fluorine-18 fluorothymidine (¹⁸F-FLT), and carbon-11 choline (¹¹C-choline). Evidence-based data demonstrated good diagnostic performance of PET with different tracers in detecting brain tumors, in particular, radiolabelled amino acid tracers showed the highest diagnostic performance values. All the PET tracers evaluated had significant prognostic value in patients with glioma. Conclusions: Evidence-based data showed a good diagnostic performance for some PET tracers in specific indications and significant prognostic value in brain tumors.

Validation of magnetic resonance imaging-based automatic high-grade glioma segmentation accuracy via 11C-methionine positron emission tomography

Brain Tumor Image Analysis (BraTumIA) is a fully automated segmentation tool dedicated to detecting brain tumors imaged by magnetic resonance imaging (MRI). BraTumIA has recently been applied to several clinical investigations; however, the validity of this novel method has not yet been fully examined. The present study was conducted to validate the quality of tumor segmentation with BraTumIA in comparison with results from ¹¹C-methionine positron emission tomography (MET-PET). A total of 45 consecutive newly diagnosed high-grade gliomas imaged by MRI and MET-PET were analyzed. Automatic tumor segmentation was conducted by BraTumIA and the resulting segmentation images were registered to MET-PET. Three-dimensional conformal association between these two modalities was calculated, considering MET-PET as the gold standard. High underestimation and overestimation errors were observed in tumor segmentation calculated by BraTumIA compared with MET-PET. Furthermore, when the tumor/normal ratio threshold was set at 1.3 from MET-PET, the BraTumIA false-positive fraction was ~0.4 and the false-negative fraction was 0.9. By tightening this threshold to 2.0, the BraTumIA false-positive fraction was 0.6 and the false-negative fraction was 0.6. Following comparison of segmentation performance with BraTumIA with regard to glioblastoma (GBM) and World Health Organization (WHO) grade III glioma, GBM exhibited better segmentation compared with WHO grade III glioma. Although BraTumIA may be able to detect enhanced tumors, non-enhancing tumors and necrosis, the spatial concordance rate with MET-PET was relatively low. Careful interpretation is therefore required when using this technique.

Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging-a comprehensive overview

Gadolinium-based contrast agents (GBCAs) increase lesion detection and improve disease characterization for many cerebral pathologies investigated with MRI. These agents, introduced in the late 1980s, are in wide use today. However, some non-ionic linear GBCAs have been associated with the development of nephrogenic systemic fibrosis in patients with kidney failure. Gadolinium deposition has also been found in deep brain structures, although it is of unclear clinical relevance. Hence, new guidelines from the International Society for Magnetic Resonance in Medicine advocate cautious use of GBCA in clinical and research practice. Some linear GBCAs were restricted from use by the European Medicines Agency (EMA) in 2017.

This review focuses on non-contrast-enhanced MRI techniques that can serve as alternatives for the use of GBCAs. Clinical studies on the diagnostic performance of non-contrast-enhanced as well as contrast-enhanced MRI methods, both well established and newly proposed, were included. Advantages and disadvantages together with the diagnostic performance of each method are detailed. Non-contrast-enhanced MRIs discussed in this review are arterial spin labeling (ASL), time of flight (TOF), phase contrast (PC), diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), susceptibility weighted imaging (SWI), and amide proton transfer (APT) imaging.

Ten common diseases were identified for which studies reported comparisons of non-contrast-enhanced and contrast-enhanced MRI. These specific diseases include primary brain tumors, metastases, abscess, multiple sclerosis, and vascular conditions such as aneurysm, arteriovenous malformation, arteriovenous fistula, intracranial carotid artery occlusive disease, hemorrhagic, and ischemic stroke.

In general, non-contrast-enhanced techniques showed comparable diagnostic performance to contrast-enhanced MRI for specific diagnostic questions. However, some diagnoses still require contrast-enhanced imaging for a complete examination.

Comparison of L-Methyl-11C-Methionine PET With Magnetic Resonance Spectroscopy in Detecting Newly Diagnosed Glioma

AIMS

Amino acid PET and magnetic resonance spectroscopy (MRS) are at the forefront of noninvasive imaging techniques used for detection and subtyping of glioma-suspicious lesions. In this pilot study, we compare L-methyl-C-methionine PET and MRS for their ability to predict glioma subtypes.

METHODS

Nineteen patients with histologically, confirmed newly diagnosed glioma underwent preoperative L-methyl-C-methionine PET and MRS in 1 diagnostic session. According to the molecular portfolio and histopathologic diagnosis, patients were subdivided in isocitrate dehydrogenase (IDH) wild-type glioblastoma, IDH wild-type grade II/III glioma, IDH-mutant grade II/III glioma without 1p/19q codeletion, and with 1p/19q codeletion subgroups. Maximum tumor-to-brain ratio (TBRmax), creatine, choline, and N-acetyl aspartate peaks were correlated with postoperative histopathologic tumor diagnoses.

RESULTS

Maximum tumor-to-brain ratio was highest in glioblastoma patients (4.18) followed by patients with IDH wild-type grade II and III glioma (3.41). The latter TBRmax values were higher compared with those in patients with IDH-mutant grade II/III glioma without 1p/19q codeletion (1.95) and in patients with IDH-mutant 1p/19q codeleted grade II and III glioma (2.79). Magnetic resonance spectroscopy marker distribution showed no clear trend. Receiver operating characteristic analysis revealed TBRmax to be the best performing parameter in identifying IDH status (area under the curve, 0.67) and all spectroscopy markers combined in identifying glioma subgroups (area under the curve, 0.68), respectively.

CONCLUSIONS

L-Methyl-C-methionine PET and MRS bear limited potential in glioma subgrouping. L-Methyl-C-methionine PET appears to be superior in differentiating IDH status, whereas MRS is more helpful in glioma subgrouping.

Direct comparison of [11C] choline and [18F] FET PET to detect glioma infiltration: a diagnostic accuracy study in eight patients

Background

Positron emission tomography (PET) is increasingly used to guide local treatment in glioma. The purpose of this study was a direct comparison of two potential tracers for detecting glioma infiltration, O-(2-[¹⁸F]-fluoroethyl)-l-tyrosine ([¹⁸F] FET) and [¹¹C] choline.

Methods

Eight consecutive patients with newly diagnosed diffuse glioma underwent dynamic [¹¹C] choline and [¹⁸F] FET PET scans. Preceding craniotomy, multiple stereotactic biopsies were obtained from regions inside and outside PET abnormalities. Biopsies were assessed independently for tumour presence by two neuropathologists. Imaging measurements were derived at the biopsy locations from 10 to 40 min [¹¹C] choline and 20–40, 40–60 and 60–90 min [¹⁸F] FET intervals, as standardized uptake value (SUV) and tumour-to-brain ratio (TBR). Diagnostic accuracies of both tracers were compared using receiver operating characteristic analysis and generalized linear mixed modelling with consensus histopathological assessment as reference.

Results

Of the 74 biopsies, 54 (73%) contained tumour. [¹¹C] choline SUV and [¹⁸F] FET SUV and TBR at all intervals were higher in tumour than in normal samples. For [¹⁸F] FET, the diagnostic accuracy of TBR was higher than that of SUV for intervals 40–60 min (area under the curve: 0.88 versus 0.81, p = 0.026) and 60–90 min (0.90 versus 0.81, p = 0.047). The diagnostic accuracy of [¹⁸F] FET TBR 60–90 min was higher than that of [¹¹C] choline SUV 20–40 min (0.87 versus 0.67, p = 0.005).

Conclusions

[¹⁸F] FET was more accurate than [¹¹C] choline for detecting glioma infiltration. Highest accuracy was found for [¹⁸F] FET TBR for the interval 60–90 min post-injection.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0523-8) contains supplementary material, which is available to authorized users.

Inter-rater agreement in glioma segmentations on longitudinal MRI

Background

Tumor segmentation of glioma on MRI is a technique to monitor, quantify and report disease progression. Manual MRI segmentation is the gold standard but very labor intensive. At present the quality of this gold standard is not known for different stages of the disease, and prior work has mainly focused on treatment-naive glioblastoma. In this paper we studied the inter-rater agreement of manual MRI segmentation of glioblastoma and WHO grade II-III glioma for novices and experts at three stages of disease. We also studied the impact of inter-observer variation on extent of resection and growth rate.

Methods

In 20 patients with WHO grade IV glioblastoma and 20 patients with WHO grade II-III glioma (defined as non-glioblastoma) both the enhancing and non-enhancing tumor elements were segmented on MRI, using specialized software, by four novices and four experts before surgery, after surgery and at time of tumor progression. We used the generalized conformity index (GCI) and the intra-class correlation coefficient (ICC) of tumor volume as main outcome measures for inter-rater agreement.

Results

For glioblastoma, segmentations by experts and novices were comparable. The inter-rater agreement of enhancing tumor elements was excellent before surgery (GCI 0.79, ICC 0.99) poor after surgery (GCI 0.32, ICC 0.92), and good at progression (GCI 0.65, ICC 0.91). For non-glioblastoma, the inter-rater agreement was generally higher between experts than between novices. The inter-rater agreement was excellent between experts before surgery (GCI 0.77, ICC 0.92), was reasonable after surgery (GCI 0.48, ICC 0.84), and good at progression (GCI 0.60, ICC 0.80). The inter-rater agreement was good between novices before surgery (GCI 0.66, ICC 0.73), was poor after surgery (GCI 0.33, ICC 0.55), and poor at progression (GCI 0.36, ICC 0.73). Further analysis showed that the lower inter-rater agreement of segmentation on postoperative MRI could only partly be explained by the smaller volumes and fragmentation of residual tumor. The median interquartile range of extent of resection between raters was 8.3% and of growth rate was 0.22 mm/year.

Conclusion

Manual tumor segmentations on MRI have reasonable agreement for use in spatial and volumetric analysis. Agreement in spatial overlap is of concern with segmentation after surgery for glioblastoma and with segmentation of non-glioblastoma by non-experts.

•

Inter-rater agreement for longitudinal glioma segmentation was determined.

•

Agreement between 4 experts was higher than between 4 novices.

•

Three time-points of glioblastoma (WHO IV) and diffuse glioma (WHO II-III) are studied.

•

Impact on extent of resection and growth rate measurements was determined.

High-resolution metabolic mapping of gliomas via patch-based super-resolution magnetic resonance spectroscopic imaging at 7T

OBJECTIVES

To demonstrate the feasibility of 7 T magnetic resonance spectroscopic imaging (MRSI), combined with patch-based super-resolution (PBSR) reconstruction, for high-resolution multi-metabolite mapping of gliomas.

MATERIALS AND METHODS

Ten patients with WHO grade II, III and IV gliomas (6/4, male/female; 45 ± 9 years old) were prospectively measured between 2014 and 2018 on a 7 T whole-body MR imager after routine 3 T magnetic resonance imaging (MRI) and positron emission tomography (PET). Free induction decay MRSI with a 64 × 64-matrix and a nominal voxel size of 3.4 × 3.4 × 8 mm³ was acquired in six minutes, along with standard T1/T2-weighted MRI. Metabolic maps were obtained via spectral LCmodel processing and reconstructed to 0.9 × 0.9 × 8 mm³ resolutions via PBSR.

RESULTS

Metabolite maps obtained from combined 7 T MRSI and PBSR resolved the density of metabolic activity in the gliomas in unprecedented detail. Particularly in the more heterogeneous cases (e.g. post resection), metabolite maps enabled the identification of complex metabolic activities, which were in topographic agreement with PET enhancement.

CONCLUSIONS

PBSR-MRSI combines the benefits of ultra-high-field MR systems, cutting-edge MRSI, and advanced postprocessing to allow millimetric resolution molecular imaging of glioma tissue beyond standard methods. An ideal example is the accurate imaging of glutamine, which is a prime target of modern therapeutic approaches, made possible due to the higher spectral resolution of 7 T systems.

Earliest radiological progression in glioblastoma by multidisciplinary consensus review

BACKGROUND

Detection of glioblastoma progression is important for clinical decision-making on cessation or initiation of therapy, for enrollment in clinical trials, and for response measurement in time and location. The RANO-criteria are considered standard for the timing of progression. To evaluate local treatment, we aim to find the most accurate progression location. We determined the differences in progression free survival (PFS) and in tumor volumes at progression (Vprog) by three definitions of progression.

METHODS

In a consecutive cohort of 73 patients with newly-diagnosed glioblastoma between 1/1/2012 and 31/12/2013, progression was established according to three definitions. We determined (1) earliest radiological progression (ERP) by retrospective multidisciplinary consensus review using all available imaging and follow-up, (2) clinical practice progression (CPP) from multidisciplinary tumor board conclusions, and (3) progression by the RANO-criteria.

RESULTS

ERP was established in 63 (86%), CPP in 64 (88%), RANO progression in 42 (58%). Of the 63 patients who had died, 37 (59%) did with prior RANO-progression, compared to 57 (90%) for both ERP and CPP. The median overall survival was 15.3 months. The median PFS was 8.8 months for ERP, 9.5 months for CPP, and 11.8 months for RANO. The PFS by ERP was shorter than CPP (HR 0.57, 95% CI 0.38-0.84, p = 0.004) and RANO-progression (HR 0.29, 95% CI 0.19-0.43, p < 0.001). The Vprog were significantly smaller for ERP (median 8.8 mL), than for CPP (17 mL) and RANO (22 mL).

CONCLUSION

PFS and Vprog vary considerably between progression definitions. Earliest radiological progression by retrospective consensus review should be considered to accurately localize progression and to address confounding of lead time bias in clinical trial enrollment.

Paediatric gliomas: diagnosis, molecular biology and management

Paediatric gliomas represent the most common brain tumour in children. Early diagnosis and treatment greatly improve survival. Histological grade is the most significant classification system affecting treatment planning and prognosis. Paediatric gliomas depend on pathways and genes responsible for mitotic activity and cell proliferation as well as angiogenesis (MAPK, VEGF, EFGR pathways). Symptoms such as focal neurologic deficit or seizures can facilitate diagnosis, but they are not always present and therefore diagnosis is occasionally delayed. Imaging has adequate diagnostic accuracy (surpassing 90%), and novel imaging techniques such as MR spectroscopy and PET increase only slightly this percentage. Low grade gliomas (LGG) can be approached conservatively but most authors suggest surgical excision. High grade gliomas (HGG) are always operated with exception of specific contradictions including butterfly or extensive dominant hemisphere gliomas. Surgical excision is universally followed by radiotherapy and chemotherapy, which slightly increase survival. Inoperable cases can be managed with or without radiosurgery depending on location and size, with adjunctive use of radiotherapy and chemotherapy. Surgical excision must be aggressive and gross total resection (GTR) should be attempted, if possible, since it can triple survival. Radiosurgery is effective on smaller tumours of <2 cm2. Surgical excision is always the treatment of choice, but glioma recurrences, and residual tumours in non-critical locations are candidates for radiosurgery especially if tumour volume is low. Management of recurrences includes surgery, radiosurgery and chemoradiotherapy and it should be individualized according to location and size. In combination with molecular targeted therapeutic schemes, glioma management will be immensely improved in the next years.