The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update.

TARGET POPULATION: These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven glioblastoma (GBM). QUESTION: What are the optimal imaging techniques to be used in the management of a suspected glioblastoma (GBM), specifically: which imaging sequences are critical for most accurately identifying or diagnosing a GBM and distinguishing this tumor from other tumor types? RECOMMENDATIONS: Critical Imaging for the Identification and Diagnosis of Glioblastoma Level II: In patients with a suspected GBM, it is recommended that the minimum magnetic resonance imaging (MRI) exam should be an anatomic exam with both T2 weighted, FLAIR and pre- and post-gadolinium contrast enhanced T1 weighted imaging. The addition of diffusion and perfusion weighted MR imaging can assist in the assessment of suspected GBM for the purposes of distinguishing GBM from other tumor types. Computed tomography (CT) can provide additional information regarding calcification or hemorrhage and also can be useful for subjects who are unable to undergo MR imaging. At a minimum, these anatomic sequences can help identify a lesion as well as its location, and potential for surgical intervention. Improvement of diagnostic specificity with the addition of non-anatomic (physiologic imaging) to anatomic imaging Level II: One blinded prospective study and a significant number of case series support the addition of diffusion and perfusion weighted MR imaging in the assessment of suspected GBM, for the purposes of distinguishing GBM from other tumor types (e.g., primary CNS lymphoma or metastases). Level III: It is suggested that magnetic resonance spectroscopy (MRS) and nuclear medicine imaging (PET 18F-FDG and 11C-MET) be used to provide additional support for the diagnosis of GBM.

Preoperative relative cerebral blood volume analysis in gliomas predicts survival and mitigates risk of biopsy sampling error.

Appropriate management of adult gliomas requires an accurate histopathological diagnosis. However, the heterogeneity of gliomas can lead to misdiagnosis and undergrading, especially with biopsy. We evaluated the role of preoperative relative cerebral blood volume (rCBV) analysis in conjunction with histopathological analysis as a predictor of overall survival and risk of undergrading. We retrospectively identified 146 patients with newly diagnosed gliomas (WHO grade II-IV) that had undergone preoperative MRI with rCBV analysis. We compared overall survival by histopathologically determined WHO tumor grade and by rCBV using Kaplan-Meier survival curves and the Cox proportional hazards model. We also compared preoperative imaging findings and initial histopathological diagnosis in 13 patients who underwent biopsy followed by subsequent resection. Survival curves by WHO grade and rCBV tier similarly separated patients into low, intermediate, and high-risk groups with shorter survival corresponding to higher grade or rCBV tier. The hazard ratio for WHO grade III versus II was 3.91 (p = 0.018) and for grade IV versus II was 11.26 (p < 0.0001) and the hazard ratio for each increase in 1.0 rCBV units was 1.12 (p < 0.002). Additionally, 3 of 13 (23%) patients initially diagnosed by biopsy were upgraded on subsequent resection. Preoperative rCBV was elevated at least one standard deviation above the mean in the 3 upgraded patients, suggestive of undergrading, but not in the ten concordant diagnoses. In conclusion, rCBV can predict overall survival similarly to pathologically determined WHO grade in patients with gliomas. Discordant rCBV analysis and histopathology may help identify patients at higher risk for undergrading.

The role of imaging in the management of adults with diffuse low grade glioma: A systematic review and evidence-based clinical practice guideline.

QUESTION: What is the optimal imaging technique to be used in the diagnosis of a suspected low grade glioma, specifically: which anatomic imaging sequences are critical for most accurately identifying or diagnosing a low grade glioma (LGG) and do non-anatomic imaging methods and/or sequences add to the diagnostic specificity of suspected low grade gliomas? TARGET POPULATION: These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG. LEVEL II: In patients with a suspected brain tumor, the minimum magnetic resonance imaging (MRI) exam should be an anatomic exam with both T2 weighted and pre- and post-gadolinium contrast enhanced T1 weighted imaging. CRITICAL IMAGING FOR THE IDENTIFICATION AND DIAGNOSIS OF LOW GRADE GLIOMA: LEVEL II: In patients with a suspected brain tumor, anatomic imaging sequences should include T1 and T2 weighted and Fluid Attenuation Inversion Recovery (FLAIR) MR sequences and will include T1 weighted imaging after the administration of gadolinium based contrast. Computed tomography (CT) can provide additional information regarding calcification or hemorrhage, which may narrow the differential diagnosis. At a minimum, these anatomic sequences can help identify a lesion as well as its location, and potential for surgical intervention. IMPROVEMENT OF DIAGNOSTIC SPECIFICITY WITH THE ADDITION OF NON-ANATOMIC (PHYSIOLOGIC AND ADVANCED IMAGING) TO ANATOMIC IMAGING: LEVEL II: Class II evidence from multiple studies and a significant number of Class III series support the addition of diffusion and perfusion weighted MR imaging in the assessment of suspected LGGs, for the purposes of discriminating the potential for tumor subtypes and identification of suspicion of higher grade diagnoses. LEVEL III: Multiple series offer Class III evidence to support the potential for magnetic resonance spectroscopy (MRS) and nuclear medicine methods including positron emission tomography and single-photon emission computed tomography imaging to offer additional diagnostic specificity although these are less well defined and their roles in clinical practice are still being defined. QUESTION: Which imaging sequences or parameters best predict the biological behavior or prognosis for patients with LGG? TARGET POPULATION: These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG. RECOMMENDATION: Anatomic and advanced imaging methods and prognostic stratification LEVEL III: Multiple series suggest a role for anatomic and advanced sequences to suggest prognostic stratification among low grade gliomas. Perfusion weighted imaging, particularly when obtained as a part of diagnostic evaluation (as recommended above) can play a role in consideration of prognosis. Other imaging sequences remain investigational in terms of their role in consideration of tumor prognosis as there is insufficient evidence to support more formal recommendations as to their use at this time. QUESTION: What is the optimal imaging technique to be used in the follow-up of a suspected (or biopsy proven) LGG? TARGET POPULATION: This recommendation applies to adults with a newly diagnosed low grade glioma. LEVEL II: In patients with a diagnosis of LGG, anatomic imaging sequences should include T2/FLAIR MR sequences and T1 weighted imaging before and after the administration of gadolinium based contrast. Serial imaging should be performed to identify new areas of contrast enhancement or significant change in tumor size, which may signify transformation to a higher grade. LEVEL III: Advanced imaging utility may depend on tumor subtype. Multicenter clinical trials with larger cohorts are needed. For astrocytic tumors, baseline and longitudinal elevations in tumor perfusion as assessed by dynamic susceptibility contrast perfusion MRI are associated with shorter time to tumor progression, but can be difficult to standardize in clinical practice. For oligodendrogliomas and mixed gliomas, MRS may be helpful for identification of progression.

Biological and therapeutic implications of multisector sequencing in newly diagnosed glioblastoma.

Background: Diagnostic workflows for glioblastoma (GBM) patients increasingly include DNA sequencing-based analysis of a single tumor site following biopsy or resection. We hypothesized that sequencing of multiple sectors within a given tumor would provide a more comprehensive representation of the molecular landscape and potentially inform therapeutic strategies. Methods: Ten newly diagnosed, isocitrate dehydrogenase 1 (IDH1) wildtype GBM tumor samples were obtained from 2 (n = 9) or 4 (n = 1) spatially distinct tumor regions. Tumor and matched blood DNA samples underwent whole-exome sequencing. Results: Across all 10 tumors, 51% of mutations were clonal and 3% were subclonal and shared in different sectors, whereas 46% of mutations were subclonal and private. Two of the 10 tumors exhibited a regional hypermutator state despite being treatment naïve, and remarkably, the high mutational load was predominantly limited to one sector in each tumor. Among the canonical cancer-associated genes, only telomerase reverse transcriptase (TERT) promoter mutations were observed in the founding clone in all tumors. Reconstruction of the clonal architecture in different sectors revealed regionally divergent evolution, and integration of data from 2 sectors increased the resolution of inferred clonal architecture in a given tumor. Predicted therapeutic mutations differed in presence and frequency between tumor regions. Similarly, different sectors exhibited significant divergence in the predicted neoantigen landscape. Conclusions: The substantial spatial heterogeneity observed in different GBM tumor sectors, especially in spatially restricted hypermutator cases, raises important caveats to our current dependence on single-sector molecular information to guide either targeted or immune-based treatments.

Dynamic susceptibility contrast and dynamic contrast-enhanced MRI characteristics to distinguish microcystic meningiomas from traditional Grade I meningiomas and high-grade gliomas.

OBJECTIVE Microcystic meningioma (MM) is a meningioma variant with a multicystic appearance that may mimic intrinsic primary brain tumors and other nonmeningiomatous tumor types. Dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI techniques provide imaging parameters that can differentiate these tumors according to hemodynamic and permeability characteristics with the potential to aid in preoperative identification of tumor type. METHODS The medical data of 18 patients with a histopathological diagnosis of MM were identified through a retrospective review of procedures performed between 2008 and 2012; DSC imaging data were available for 12 patients and DCE imaging data for 6. A subcohort of 12 patients with Grade I meningiomas (i.e., of meningoepithelial subtype) and 54 patients with Grade IV primary gliomas (i.e., astrocytomas) was also included, and all preoperative imaging sequences were analyzed. Clinical variables including patient sex, age, and surgical blood loss were also included in the analysis. Images were acquired at both 1.5 and 3.0 T. The DSC images were acquired at a temporal resolution of either 1500 msec (3.0 T) or 2000 msec (1.5 T). In all cases, parameters including normalized cerebral blood volume (CBV) and transfer coefficient (kTrans) were calculated with region-of-interest analysis of enhancing tumor volume. The normalized CBV and kTrans data from the patient groups were analyzed with 1-way ANOVA, and post hoc statistical comparisons among groups were conducted with the Bonferroni adjustment. RESULTS Preoperative DSC imaging indicated mean (± SD) normalized CBVs of 5.7 ± 2.2 ml for WHO Grade I meningiomas of the meningoepithelial subtype (n = 12), 4.8 ± 1.8 ml for Grade IV astrocytomas (n = 54), and 12.3 ± 3.8 ml for Grade I meningiomas of the MM subtype (n = 12). The normalized CBV measured within the enhancing portion of the tumor was significantly higher in the MM subtype than in typical meningiomas and Grade IV astrocytomas (p < 0.001 for both). Preoperative DCE imaging indicated mean kTrans values of 0.49 ± 0.20 min-1 in Grade I meningiomas of the meningoepithelial subtype (n = 12), 0.27 ± 0.12 min-1 for Grade IV astrocytomas (n = 54), and 1.35 ± 0.74 min-1 for Grade I meningiomas of the MM subtype (n = 6). The kTrans was significantly higher in the MM variants than in the corresponding nonmicrocystic Grade 1 meningiomas and Grade IV astrocytomas (p < 0.001 for both). Intraoperative blood loss tended to increase with increased normalized CBV (R = 0.45, p = 0.085). CONCLUSIONS An enhancing cystic lesion with a normalized CBV greater than 10.3 ml or a kTrans greater than 0.88 min-1 should prompt radiologists and surgeons to consider the diagnosis of MM rather than traditional Grade I meningioma or high-grade glioma in planning surgical care. Higher normalized CBVs tend to be associated with increased intraoperative blood loss.

Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research.

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.

Aerobic Glycolysis as a Marker of Tumor Aggressiveness: Preliminary Data in High Grade Human Brain Tumors.

OBJECTIVES: Glucose metabolism outside of oxidative phosphorylation, or aerobic glycolysis (AG), is a hallmark of active cancer cells that is not directly measured with standard (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET). In this study, we characterized tumor regions with elevated AG defined based on PET measurements of glucose and oxygen metabolism. METHODS: Fourteen individuals with high-grade brain tumors underwent structural MR scans and PET measurements of cerebral blood flow (CBF), oxygen (CMRO2) and glucose (CMRGlu) metabolism, and AG, using (15)O-labeled CO, O2 and H2O, and FDG, and were compared to a normative cohort of 20 age-matched individuals. RESULTS: Elevated AG was observed in most high-grade brain tumors and it was associated with decreased CMRO2 and CBF, but not with significant changes in CMRGlu. Elevated AG was a dramatic and early sign of tumor growth associated with decreased survival. AG changes associated with tumor growth were differentiated from the effects of nonneoplastic processes such as epileptic seizures. CONCLUSIONS: Our findings demonstrate that high-grade brain tumors exhibit elevated AG as a marker of tumor growth and aggressiveness. AG may detect areas of active tumor growth that are not evident on conventional FDG PET.

Attrition rates in neurosurgery residency: analysis of 1361 consecutive residents matched from 1990 to 1999.

OBJECT: The objective of this study is to determine neurosurgery residency attrition rates by sex of matched applicant and by type and rank of medical school attended. METHODS: The study follows a cohort of 1361 individuals who matched into a neurosurgery residency program through the SF Match Fellowship and Residency Matching Service from 1990 to 1999. The main outcome measure was achievement of board certification as documented in the American Board of Neurological Surgery Directory of Diplomats. A secondary outcome measure was documentation of practicing medicine as verified by the American Medical Association DoctorFinder and National Provider Identifier websites. Overall, 10.7% (n=146) of these individuals were women. Twenty percent (n=266) graduated from a top 10 medical school (24% of women [35/146] and 19% of men [232/1215], p=0.19). Forty-five percent (n=618) were graduates of a public medical school, 50% (n=680) of a private medical school, and 5% (n=63) of an international medical school. At the end of the study, 0.2% of subjects (n=3) were deceased and 0.3% (n=4) were lost to follow-up. RESULTS: The total residency completion rate was 86.0% (n=1171) overall, with 76.0% (n=111/146) of women and 87.2% (n=1059/1215) of men completing residency. Board certification was obtained by 79.4% (n=1081) of all individuals matching into residency between 1990 and 1999. Overall, 63.0% (92/146) of women and 81.3% (989/1215) of men were board certified. Women were found to be significantly more at risk (p<0.005) of not completing residency or becoming board certified than men. Public medical school alumni had significantly higher board certification rates than private and international alumni (82.2% for public [508/618]; 77.1% for private [524/680]; 77.8% for international [49/63]; p<0.05). There was no significant difference in attrition for graduates of top 10-ranked institutions versus other institutions. There was no difference in number of years to achieve neurosurgical board certification for men versus women. CONCLUSIONS: Overall, neurosurgery training attrition rates are low. Women have had greater attrition than men during and after neurosurgery residency training. International and private medical school alumni had higher attrition than public medical school alumni.

Comparison of perfusion- and diffusion-weighted imaging parameters in brain tumor studies processed using different software platforms.

RATIONALE AND OBJECTIVES: To compare quantitative imaging parameter measures from diffusion- and perfusion-weighted imaging magnetic resonance imaging (MRI) sequences in subjects with brain tumors that have been processed with different software platforms. MATERIALS AND METHODS: Scans from 20 subjects with primary brain tumors were selected from the Comprehensive Neuro-oncology Data Repository at Washington University School of Medicine (WUSM) and the Swedish Neuroscience Institute. MR images were coregistered, and each subject's data set was processed by three software packages: 1) vendor-specific scanner software, 2) research software developed at WUSM, and 3) a commercially available, Food and Drug Administration-approved, processing platform (Nordic Ice). Regions of interest (ROIs) were chosen within the brain tumor and normal nontumor tissue. The results obtained using these methods were compared. RESULTS: For diffusion parameters, including mean diffusivity and fractional anisotropy, concordance was high when comparing different processing methods. For perfusion-imaging parameters, a significant variance in cerebral blood volume, cerebral blood flow, and mean transit time (MTT) values was seen when comparing the same raw data processed using different software platforms. Correlation was better with larger ROIs (radii ≥ 5 mm). Greatest variance was observed in MTT. CONCLUSIONS: Diffusion parameter values were consistent across different software processing platforms. Perfusion parameter values were more variable and were influenced by the software used. Variation in the MTT was especially large suggesting that MTT estimation may be unreliable in tumor tissues using current MRI perfusion methods.

The comprehensive neuro-oncology data repository (CONDR): a research infrastructure to develop and validate imaging biomarkers.

BACKGROUND: Advanced imaging methods have the potential to serve as quantitative biomarkers in neuro-oncology research. However, a lack of standardization of image acquisition, processing, and analysis limits their application in clinical research. Standardization of these methods and an organized archival platform are required to better validate and apply these markers in research settings and, ultimately, in clinical practice. OBJECTIVE: The primary objective of the Comprehensive Neuro-oncology Data Repository (CONDR) is to develop a data set for assessing and validating advanced imaging methods in patients diagnosed with brain tumors. As a secondary objective, informatics resources will be developed to facilitate the integrated collection, processing, and analysis of imaging, tissue, and clinical data in multicenter clinical trials. Finally, CONDR data and informatics resources will be shared with the research community for further analysis. METHODS: CONDR will enroll 200 patients diagnosed with primary brain tumors. Clinical, imaging, and tissue-based data are obtained from patients serially, beginning with diagnosis and continuing over the course of their treatment. The CONDR imaging protocol includes structural and functional sequences, including diffusion- and perfusion-weighted imaging. All data are managed within an XNAT-based informatics platform. Imaging markers are assessed by correlating image and spatially aligned pathological markers and a variety of clinical markers. EXPECTED OUTCOMES: CONDR will generate data for developing and validating imaging markers of primary brain tumors, including multispectral and probabilistic maps. DISCUSSION: CONDR implements a novel, open-research model that will provide the research community with both open-access data and open-source informatics resources.

Predicting a multi-parametric probability map of active tumor extent using random forests.

Glioblastoma Mulitforme is highly infiltrative, making precise delineation of tumor margin difficult. Multimodality or multi-parametric MR imaging sequences promise an advantage over anatomic sequences such as post contrast enhancement as methods for determining the spatial extent of tumor involvement. In considering multi-parametric imaging sequences however, manual image segmentation and classification is time-consuming and prone to error. As a preliminary step toward integration of multi-parametric imaging into clinical assessments of primary brain tumors, we propose a machine-learning based multi-parametric approach that uses radiologist generated labels to train a classifier that is able to classify tissue on a voxel-wise basis and automatically generate a tumor segmentation. A random forests classifier was trained using a leave-one-out experimental paradigm. A simple linear classifier was also trained for comparison. The random forests classifier accurately predicted radiologist generated segmentations and tumor extent.