Intraoperative Augmented Reality for Complex Glioma Resection: A Case Report.

Augmented reality (AR) is an emerging technology that can display three-dimensional patient anatomy in the surgeons' field of view. The use of this technology has grown considerably for both presurgical and intraoperative guidance. A patient diagnosed with breast cancer started to experience numbness in the left hand, which progressed to weakness in the left hand and arm. An MRI was performed demonstrating a 2.9 cm X 1.8 cm lesion with extensive surrounding edema in the posterior fronto-parietal lobes. Surgery was recommended for presumed metastatic disease. Preoperatively, an AR system and Brainlab navigation were registered to the patient. AR, traditional navigation, and ultrasound were all used to localize the lesion and determine the craniotomy site and size. The tumor was removed along the direction of the lesion. Intraoperatively, we used AR to reexamine the tumor details and could appreciate that we had to redirect our surgical trajectory anteriorly and laterally in order to follow along the main axis of the tumor. In doing this, we were able to more confidently remain with the tumor, which by this time was poorly defined by 2D navigation and by direct vision. Postoperative MRI confirmed gross total removal of the tumor. The patient had an uneventful postoperative course with resolution of preoperative symptoms and the final surgical pathology was grade 4 glioblastoma. Here, we describe the valuable use of AR for the resection of a glioma. The system has a seamless registration process and provides the surgeon with a unique view of 3D anatomy overlaid onto the patient's head. This exciting technology can add tremendous value to complex cranial surgeries.

Integration of Augmented Reality Into Glioma Resection Surgery: A Case Report.

Augmented reality (AR) is an exciting technology that has garnered considerable attention in the field of neurosurgery. Despite this, clinical use of this technology is still in its infancy. An area of great potential for this technology is the ability to display 3D anatomy overlaid with the patient to assist with presurgical and intraoperative decision-making. A 39-year-old woman presented with headaches and was experiencing what was described as a whooshing sound. MRI revealed the presence of a large left frontal mass involving the genu of the corpus callosum, with heterogeneous enhancement and central hemorrhagic necrosis, confirmed to be a glioma. She underwent a craniotomy with intraoperative MRI for resection. An augmented reality system was used to superimpose 3D holographic anatomy onto the patient's head for surgical planning. This report highlights a new AR technology and its immediate application to cranial neurosurgery. It is critical to document new uses of this technology as the field continues to integrate AR as well as other next-generation technologies into practice.

Augmented Reality Registration System for Visualization of Skull Landmarks.

BACKGROUND: Augmented reality (AR) is an emerging technology in neurosurgery with the potential to become a strategic tool in the delivery of care and education for trainees. Advances in technology have demonstrated promising use for improving visualization and spatial awareness of critical neuroanatomic structures. In this report, we employ a novel AR registration system for the visualization and targeting of skull landmarks. METHODS: A markerless AR system was used to register 3-dimensional reconstructions of suture lines onto the head via a head-mounted display. Participants were required to identify craniometric points with and without AR assistance. Targeting error was measured as the Euclidian distance between the user-defined location and the true craniometric point on the subjects' heads. RESULTS: All participants successfully registered 3-dimensional reconstructions onto the subjects' heads. Targeting accuracy was significantly improved with AR (3.59 ± 1.29 mm). Across all target points, AR increased accuracy by an average of 19.96 ± 3.80 mm. Posttest surveys revealed that participants felt the technology increased their confidence in identifying landmarks (4.6/5) and that the technology will be useful for clinical care (4.2/5). CONCLUSIONS: While several areas of improvement and innovation can further enhance the use of AR in neurosurgery, this report demonstrates the feasibility of a markerless headset-based AR system for visualizing craniometric points on the skull. As the technology continues to advance, AR is expected to play an increasingly significant role in neurosurgery, transforming how surgeries are performed and improving patient care.

Detection of tumor-specific DNA methylation markers in the blood of patients with pituitary neuroendocrine tumors.

BACKGROUND: DNA methylation abnormalities are pervasive in pituitary neuroendocrine tumors (PitNETs). The feasibility to detect methylome alterations in circulating cell-free DNA (cfDNA) has been reported for several central nervous system (CNS) tumors but not across PitNETs. The aim of the study was to use the liquid biopsy (LB) approach to detect PitNET-specific methylation signatures to differentiate these tumors from other sellar diseases. METHODS: We profiled the cfDNA methylome (EPIC array) of 59 serum and 41 plasma LB specimens from patients with PitNETs and other CNS diseases (sellar tumors and other pituitary non-neoplastic diseases, lower-grade gliomas, and skull-base meningiomas) or nontumor conditions, grouped as non-PitNET. RESULTS: Our results indicated that despite quantitative and qualitative differences between serum and plasma cfDNA composition, both sources of LB showed that patients with PitNETs presented a distinct methylome landscape compared to non-PitNETs. In addition, LB methylomes captured epigenetic features reported in PitNET tissue and provided information about cell-type composition. Using LB-derived PitNETs-specific signatures as input to develop machine-learning predictive models, we generated scores that distinguished PitNETs from non-PitNETs conditions, including sellar tumor and non-neoplastic pituitary diseases, with accuracies above ~93% in independent cohort sets. CONCLUSIONS: Our results underpin the potential application of methylation-based LB profiling as a noninvasive approach to identify clinically relevant epigenetic markers to diagnose and potentially impact the prognostication and management of patients with PitNETs.

Adapting to Space Limitations During Prone Real-Time Magnetic Resonance Imaging-Guided Stereotaxic Laser Ablation: Technical Pearls.

BACKGROUND: New techniques of intraoperative magnetic resonance imaging (MRI)-guided stereotaxy enable minimally invasive approaches to intracranial pathology. Laser interstitial thermal therapy (LITT), convection-enhanced drug delivery, and stereotactic biopsy can be performed with a real-time confirmation of location and the ability to adjust for intracranial shift during the procedure. However, these procedures are constrained by patient positioning and the need for trajectories that avoid collision between stereotactic elements and the small MRI bore. To our knowledge, this is the first report to outline the technical details of safe intraoperative MRI (iMRI)-guided stereotaxy, performed with prone positioning. OBJECTIVE: To present technical pearls to guide the safe conduction of iMRI-guided stereotaxy and LITT while in the prone position. METHODS: The details of the positioning and trajectories for a series of patients who underwent Clearpoint® (MRI Interventions Inc) frameless real-time MRI-guided stereotaxis using a posterior approach were reviewed. RESULTS: In this series, 5 patients underwent selective amygdalohippocampectomy, and 2 underwent tumor biopsy/ablation while in the prone position without any complications. CONCLUSION: Prone iMRI procedures can be performed safely even in a 60-cm MRI bore.

MR-guided laser interstitial thermal therapy in the treatment of recurrent intracranial meningiomas.

OBJECTIVE: Recurrent meningiomas can prove problematic for treatment, especially if anaplastic, as options are limited primarily to surgery and radiation therapy. Laser interstitial thermal therapy (LITT) is a minimally invasive technique for achieving immediate cytoreduction. This study seeks to determine the utility of LITT in the setting of recurrent meningiomas. MATERIALS AND METHODS: Patients undergoing LITT for tumor treatment at our institution between November 2014 and February 2016 were identified. Those with biopsy-confirmed meningiomas were reviewed with attention to ablation volume, survival, demographic data, and complications. Data from imaging performed at set intervals post-operatively were available for all. RESULTS: Four patients were identified, three of whom had successful treatment with a total of four ablations. The one case that did not result in a successful ablation was due to problems with stereotactic placing of the laser catheter. One patient had a grade 1 meningioma, with the other two being Grade 3. Immediate ablation volumes averaged 75% of preoperative tumor volume and increased to 97% at 2 weeks before dropping to 65% at 3 months. One patient had acute hemiparesis with speech difficulty, which resolved after 6 months. At date of last follow-up, two of three had progression at an average of nine weeks, and one had no progression at 28 weeks. CONCLUSION: LITT appeared to be a potentially viable treatment for recurrent meningiomas. Ablation volumes increased over time, but not beyond the initial meningioma volume. Larger studies are needed to better determine complications and outcomes. Lasers Surg. Med. 51:245-250, 2019. © 2018 Wiley Periodicals, Inc.

The safety of magnetic resonance imaging-guided laser interstitial thermal therapy for cerebral radiation necrosis.

Cerebral radiation necrosis (CRN) is a known complication of radiation therapy. Treatment options are limited and include steroids, bevacizumab, and surgery. This study seeks to determine the safety of laser interstitial thermal therapy (LITT) for CRN and identify the pattern of post-ablation volume change over time. Patients undergoing LITT for tumor treatment at Henry Ford Hospital between November 2013 and January 2016 with biopsy-confirmed CRN were prospectively collected and retrospectively reviewed with attention to ablation volume, survival, demographic data, steroid dose, and complications. Imaging occurred at set intervals beginning pre-ablation. Ten patients with 11 ablations were evaluated. Four patients had a primary diagnosis of high-grade glioma, while six had metastatic lesions. An average of 86% of CRN volume was ablated. Ablation volume increased to 430% of initial CRN volume at 1-2 weeks before decreasing to 69% after 6 months. No patient had a decline in baseline neurological examination while in the hospital. Four patients developed delayed neurological deficits likely due to post-operative edema, of which three improved back to baseline. The 6-month survival was 77.8% and the 1-year survival was 64.8% based on Kaplan-Meier curve estimates. In this study, LITT was a relatively safe treatment for CRN, providing both a diagnostic and therapeutic solution for refractory patients. Significant increase in ablation volume was noted at 1-2 months, gradually decreasing in size to less than the original volume by 6 months. Further studies are needed to better define the role of LITT in the treatment of CRN.

Intraoperative Raman Spectroscopy.

Surgical excision of brain tumors provides a means of cytoreduction and diagnosis while minimizing neurologic deficit and improving overall survival. Despite advances in functional and three-dimensional stereotactic navigation and intraoperative MRI, delineating tissue in real time with physiologic confirmation is challenging. Raman spectroscopy has potential to be an important modality in the intraoperative evaluation of tissue during surgical resection. In vitro experimental studies have shown that this technique can be used to differentiate normal brain tissue from tissue with infiltrating cancer cells and dense cancerous masses with high specificity, indicating the feasibility of this method for in vivo application.

Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment.

We leveraged IDH wild-type glioblastomas, derivative neurospheres, and single-cell gene expression profiles to define three tumor-intrinsic transcriptional subtypes designated as proneural, mesenchymal, and classical. Transcriptomic subtype multiplicity correlated with increased intratumoral heterogeneity and presence of tumor microenvironment. In silico cell sorting identified macrophages/microglia, CD4+ T lymphocytes, and neutrophils in the glioma microenvironment. NF1 deficiency resulted in increased tumor-associated macrophages/microglia infiltration. Longitudinal transcriptome analysis showed that expression subtype is retained in 55% of cases. Gene signature-based tumor microenvironment inference revealed a decrease in invading monocytes and a subtype-dependent increase in macrophages/microglia cells upon disease recurrence. Hypermutation at diagnosis or at recurrence associated with CD8+ T cell enrichment. Frequency of M2 macrophages detection associated with short-term relapse after radiation therapy.

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution.

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ∼ 7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to reconstruct the genomic profile of residual, therapy resistant tumor initiating cells. We found that genetic alteration of the p53 pathway is a primary molecular event predictive of a high number of subclonal mutations in glioblastoma. The genomic road leading to recurrence is highly idiosyncratic but can be broadly classified into linear recurrences that share extensive genetic similarity with the primary tumor and can be directly traced to one of its specific sectors, and divergent recurrences that share few genetic alterations with the primary tumor and originate from cells that branched off early during tumorigenesis. Our study provides mechanistic insights into how genetic alterations in primary tumors impact the ensuing evolution of tumor cells and the emergence of subclonal heterogeneity.

Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.

PURPOSE: The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. METHODS: The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. RESULTS: The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679±0.068, Akaike's information criterion 566.7, P<0.001). CONCLUSION: A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.

Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor.

PURPOSE: To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. MATERIALS AND METHODS: An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. RESULTS: Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). CONCLUSION: Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.

Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates.

Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human review has limitations that can result in low reproducibility and inter-observer agreement. Computerized image analysis can partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure histologic structures on a large-scale. In this paper, we present an end-to-end image analysis and data integration pipeline for large-scale morphologic analysis of pathology images and demonstrate the ability to correlate phenotypic groups with molecular data and clinical outcomes. We demonstrate our method in the context of glioblastoma (GBM), with specific focus on the degree of the oligodendroglioma component. Over 200 million nuclei in digitized pathology slides from 117 GBMs in the Cancer Genome Atlas were quantitatively analyzed, followed by multiplatform correlation of nuclear features with molecular and clinical data. For each nucleus, a Nuclear Score (NS) was calculated based on the degree of oligodendroglioma appearance, using a regression model trained from the optimal feature set. Using the frequencies of neoplastic nuclei in low and high NS intervals, we were able to cluster patients into three well-separated disease groups that contained low, medium, or high Oligodendroglioma Component (OC). We showed that machine-based classification of GBMs with high oligodendroglioma component uncovered a set of tumors with strong associations with PDGFRA amplification, proneural transcriptional class, and expression of the oligodendrocyte signature genes MBP, HOXD1, PLP1, MOBP and PDGFRA. Quantitative morphologic features within the GBMs that correlated most strongly with oligodendrocyte gene expression were high nuclear circularity and low eccentricity. These findings highlight the potential of high throughput morphologic analysis to complement and inform human-based pathologic review.

Tumor-infiltrating lymphocytes in glioblastoma are associated with specific genomic alterations and related to transcriptional class.

PURPOSE: Tumor-infiltrating lymphocytes (TIL) have prognostic significance in many cancers, yet their roles in glioblastoma have not been fully defined. We hypothesized that TILs in glioblastoma are associated with molecular alterations, histologies, and survival. EXPERIMENTAL DESIGN: We used data from The Cancer Genome Atlas (TCGA) to investigate molecular, histologic, and clinical correlates of TILs in glioblastomas. Lymphocytes were categorized as absent, present, or abundant in histopathologic images from 171 TCGA glioblastomas. Associations were examined between lymphocytes and histologic features, mutations, copy number alterations, CpG island methylator phenotype, transcriptional class, and survival. We validated histologic findings using CD3G gene expression. RESULTS: We found a positive correlation between TILs and glioblastomas with gemistocytes, sarcomatous cells, epithelioid cells, and giant cells. Lymphocytes were enriched in the mesenchymal transcriptional class and strongly associated with mutations in NF1 and RB1. These mutations are frequent in the mesenchymal class and characteristic of gemistocytic, sarcomatous, epithelioid, and giant cell histologies. Conversely, TILs were rare in glioblastomas with small cells and oligodendroglioma components. Lymphocytes were depleted in the classical transcriptional class and in EGF receptor (EGFR)-amplified and homozygous PTEN-deleted glioblastomas. These alterations are characteristic of glioblastomas with small cells and glioblastomas of the classical transcriptional class. No association with survival was shown. CONCLUSIONS: TILs were enriched in glioblastomas of the mesenchymal class, strongly associated with mutations in NF1 and RB1 and typical of histologies characterized by these mutations. Conversely, TILs were depleted in the classical class, EGFR-amplified, and homozygous PTEN-deleted tumors and rare in histologies characterized by these alterations.

The Corpus Callosum Wallerian Degeneration in the Unilateral Brain Tumors: Evaluation with Diffusion Tensor Imaging (DTI).

PURPOSE: The purpose of this study was to evaluate whether DTI could demonstrate the water diffusivity changes in the corpus callosum (CC), which were not visible on the morphologic imaging in patients with glioblastoma multiforme (GBM) and brain metastases with no midline CC infiltration. MATERIALS AND METHODS: Twenty-seven patients with treatment naïve unilateral GBM and eleven patients with a solitary brain metastasis with no midline CC infiltration underwent DTI. Ten controls with normal brain MRI were also included. Based on the tensors, the principal diffusion directions, the anisotropy values, and the prior information about the diffusivity pattern in CC, a similarity measure was proposed. Subsequently, the CC was automatically divided into the Witelson subdivisions. RESULTS: We observed significantly decreased fractional anisotropy values in all the regions of CC in the patients with GBM and metastases as compared to those in the controls. The mean diffusivity values showed a significant increase in all the regions of CC, except the splenium in patients with GBM and the isthmus in the patients with metastases, as compared to that in the controls respectively. CONCLUSION: In conclusion, DTI is more sensitive than the morphologic MR imaging in the evaluation of changes within the CC, in brain tumours which do not infiltrate the CC. However, these changes of the DTI metrics in the CC are due to a Wallerian degeneration rather than a tumour infiltration, as was shown by our results, as similar changes were seen in the GBM as well as the non-infiltrating metastases patients.

Prognostic vascular imaging biomarkers in high-grade gliomas: tumor permeability as an adjunct to blood volume estimates.

RATIONALE AND OBJECTIVES: Despite recent advances in the treatment of high-grade gliomas, overall survival (OS) remains poor, which underlines the importance of searching for and determining prognostic imaging biomarkers. The purpose of our retrospective study was to correlate patient survival with relative cerebral blood volume (rCBV) and permeability surface area-product (PS) measured using perfusion computed tomography (PCT) in patients with high-grade gliomas. METHODS: This study was composed of 54 patients with high-grade gliomas (World Health Organization [WHO] grade III, n = 14; WHO grade IV, n = 40) who underwent pretreatment PCT. Kaplan-Meier survival estimates were computed to describe OS for patients with high-versus-low PCT parameters, as well as grade III and IV gliomas. RESULTS: Differences in OS between high and low rCBV, PS, and rCBV + PS were significant (P < .001) for all high-grade gliomas. After adjustment for WHO grade, rCBV (P = .041) and rCBV + PS (P = .013) estimates remained significant, whereas PS estimates were not (P = .214). PS estimates showed a statistically significant difference for OS in the grade III glioma group (P = .011), whereas for grade IV gliomas, rCBV estimates were statistically significant (P = .019). rCBV + PS was statistically significant for OS in both grade III (P = .001) and grade IV (P = .004) glioma groups. CONCLUSIONS: Blood volume and permeability estimates measured using PCT can help predict survival in patients with high-grade gliomas. Patients with high PCT parameters showed worse OS compared to the patients with low PCT. Both rCBV and rCBV + PS remained statistically significant even after adjustment for WHO grade, suggesting these may be better predictors of OS than histological grade.

MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.

PURPOSE: To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival. MATERIALS AND METHODS: Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff α statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test. RESULTS: Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01). CONCLUSION: This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.

Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers.

PURPOSE: To correlate tumor blood volume, measured by using dynamic susceptibility contrast material-enhanced T2*-weighted magnetic resonance (MR) perfusion studies, with patient survival and determine its association with molecular subclasses of glioblastoma (GBM). MATERIALS AND METHODS: This HIPAA-compliant retrospective study was approved by institutional review board. Fifty patients underwent dynamic susceptibility contrast-enhanced T2*-weighted MR perfusion studies and had gene expression data available from the Cancer Genome Atlas. Relative cerebral blood volume (rCBV) (maximum rCBV [rCBV(max)] and mean rCBV [rCBV(mean)]) of the contrast-enhanced lesion as well as rCBV of the nonenhanced lesion (rCBV(NEL)) were measured. Patients were subclassified according to the Verhaak and Phillips classification schemas, which are based on similarity to defined genomic expression signature. We correlated rCBV measures with the molecular subclasses as well as with patient overall survival by using Cox regression analysis. RESULTS: No statistically significant differences were noted for rCBV(max), rCBV(mean) of contrast-enhanced lesion or rCBV(NEL) between the four Verhaak classes or the three Phillips classes. However, increased rCBV measures are associated with poor overall survival in GBM. The rCBV(max) (P = .0131) is the strongest predictor of overall survival regardless of potential confounders or molecular classification. Interestingly, including the Verhaak molecular GBM classification in the survival model clarifies the association of rCBV(mean) with patient overall survival (hazard ratio: 1.46, P = .0212) compared with rCBV(mean) alone (hazard ratio: 1.25, P = .1918). Phillips subclasses are not predictive of overall survival nor do they affect the predictive ability of rCBV measures on overall survival. CONCLUSION: The rCBV(max) measurements could be used to predict patient overall survival independent of the molecular subclasses of GBM; however, Verhaak classifiers provided additional information, suggesting that molecular markers could be used in combination with hemodynamic imaging biomarkers in the future.

Prediction of glioblastoma multiform response to bevacizumab treatment using multi-parametric MRI.

Glioblastoma multiform (GBM) is a highly malignant brain tumor. Bevacizumab is a recent therapy for stopping tumor growth and even shrinking tumor through inhibition of vascular development (angiogenesis). This paper presents a non-invasive approach based on image analysis of multi-parametric magnetic resonance images (MRI) to predict response of GBM to this treatment. The resulting prediction system has potential to be used by physicians to optimize treatment plans of the GBM patients. The proposed method applies signal decomposition and histogram analysis methods to extract statistical features from Gd-enhanced regions of tumor that quantify its microstructural characteristics. MRI studies of 12 patients at multiple time points before and up to four months after treatment are used in this work. Changes in the Gd-enhancement as well as necrosis and edema after treatment are used to evaluate the response. Leave-one-out cross validation method is applied to evaluate prediction quality of the models. Predictive models developed in this work have large regression coefficients (maximum R²â€Š= 0.95) indicating their capability to predict response to therapy.