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Toxoplasma gondii is one the most prevalent opportunist infections worldwide and can cause a severe and potentially fatal encephalitis. Toxoplasma encephalitis (TE) often presents in the setting of acquired immunodeficiency syndrome (AIDS), however, diagnosing TE can be challenging in patients with no signs of being immunocompromised. Here we report unusual presentations of TE in an immunocompetent patient, a patient four years post orthotopic liver transplantation, and two patients with undiagnosed AIDS that underwent prolonged workup for lesions that mimicked intracranial neoplasm. This report highlights the difficulties in diagnosing TE in patients with atypical presentations and unknown risk factors. A high clinical suspicion should be maintained to allow rapid treatment and to avoid significant morbidity and mortality.

The need to identify biomarkers to predict immunotherapy response for rare cancers has been long overdue. We aimed to study this in our paper, ‘Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers’. In this response to the Letter to the Editor by Cunha et al, we explain and discuss the reasons behind choosing LASSO (Least Absolute Shrinkage and Selection Operator) and XGBoost (eXtreme Gradient Boosting) with LOOCV (Leave-One-Out Cross-Validation) as the feature selection and classifier method, respectively for our radiomics models. Also, we highlight what care was taken to avoid any overfitting on the models. Further, we checked for the multicollinearity of the features. Additionally, we performed 10-fold cross-validation instead of LOOCV to see the predictive performance of our radiomics models.

MRI-guided laser interstitial thermal therapy (LITT) is the selective ablation of a lesion or a tissue using heat emitted from a laser device. LITT is considered a less invasive technique compared to open surgery that provides a nonsurgical solution for patients who cannot tolerate surgery. Although laser ablation has been used to treat brain lesions for decades, recent advances in MRI have improved lesion targeting and enabled real-time accurate monitoring of the thermal ablation process. These advances have led to a plethora of research involving the technique, safety, and potential applications of LITT. LITT is a minimally invasive treatment modality that shows promising results and is associated with decreased morbidity. It has various applications, such as treatment of glioma, brain metastases, radiation necrosis, and epilepsy. It can provide a safer alternative treatment option for patients in whom the lesion is not accessible by surgery, who are not surgical candidates, or in whom other standard treatment options have failed. Our aim is to review the current literature on LITT and provide a descriptive review of the technique, imaging findings, and clinical applications for neurosurgery.

Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps., MRI scans of glioblastoma patients can be misleading and some patients appear to show features of progressive disease although they respond to treatment. Here, the authors use MRI images of progressive disease or pseudoprogression and build a classifier using machine learning to distinguish the two.

Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (IDH) and Telomerase reverse transcriptase (TERT) promoter mutations and predicting LGG associated seizures). Radiogenomics refers to the linkage of imaging findings with the tumor/tissue genomics. Numerous applications of radiomics and radiogenomics have been described in the clinical context and management of LGGs. In this review, we describe the recently published studies discussing the potential application of radiomics and radiogenomics in LGGs. We also highlight the potential pitfalls of the above-mentioned high throughput computerized techniques and, most excitingly, explore the use of machine learning artificial intelligence technologies as standalone and adjunct imaging tools en route to enhance a personalized MRI-based tumor diagnosis and management plan design.

BackgroundWe present a radiomics-based model for predicting response to pembrolizumab in patients with advanced rare cancers.MethodsThe study included 57 patients with advanced rare cancers who were enrolled in our phase II clinical trial of pembrolizumab. Tumor response was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and immune-related RECIST (irRECIST). Patients were categorized as 20 “controlled disease” (stable disease, partial response, or complete response) or 37 progressive disease). We used 3D-slicer to segment target lesions on standard-of-care, pretreatment contrast enhanced CT scans. We extracted 610 features (10 histogram-based features and 600 second-order texture features) from each volume of interest. Least absolute shrinkage and selection operator logistic regression was used to detect the most discriminatory features. Selected features were used to create a classification model, using XGBoost, for the prediction of tumor response to pembrolizumab. Leave-one-out cross-validation was performed to assess model performance.FindingsThe 10 most relevant radiomics features were selected; XGBoost-based classification successfully differentiated between controlled disease (complete response, partial response, stable disease) and progressive disease with high accuracy, sensitivity, and specificity in patients assessed by RECIST (94.7%, 97.3%, and 90%, respectively; pConclusionOur radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.InterpretationOur radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.

Preoperative mapping of language areas using fMRI greatly depends on the paradigms used, as different tasks harness distinct capabilities to activate speech processing areas. In this study, we compared the ability of 3 covert speech paradigms: Silent Sentence Completion (SSC), category naming (CAT) and verbal fluency (FAS), in localizing the Wernicke’s area and studied the association between genomic markers and functional activation. Fifteen right-handed healthy volunteers and 35 mixed-handed patients were included. We focused on the anatomical areas of posterosuperior, middle temporal and angular gyri corresponding to Wernicke’s area. Activity was deemed significant in a region of interest if P

Background Radiological assessments of biologically relevant regions in glioblastoma have been associated with genotypic characteristics, implying a potential role in personalized medicine. Here, we assess the reproducibility and association with survival of two volumetric segmentation platforms and explore how methodology could impact subsequent interpretation and analysis. Methods Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients were segmented into five distinct compartments (necrosis, contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal tumor volumes) by two quantitative image segmentation platforms - 3D Slicer and a method based on Velocity AI and FSL. We investigated the internal consistency of each platform by correlation statistics, association with survival, and concordance with consensus neuroradiologist ratings using ordinal logistic regression. Results We found high correlations between the two platforms for FLAIR, post contrast abnormal, and total abnormal tumor volumes (spearman's r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement was observed for necrosis and contrast-enhancement volumes (r(67) = 0.693 and 0.773 respectively), likely arising from differences in manual and automated segmentation methods of these regions by 3D Slicer and Velocity AI/FSL, respectively. Survival analysis based on AUC revealed significant predictive power of both platforms for the following volumes: contrast-enhancement, post contrast abnormal, and total abnormal tumor volumes. Finally, ordinal logistic regression demonstrated correspondence to manual ratings for several features. Conclusion Tumor volume measurements from both volumetric platforms produced highly concordant and reproducible estimates across platforms for general features. As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement differences between segmentation platforms for more detailed features could influence downstream survival or radio genomic analyses.

This paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features using MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and Fluid-Attenuated Inversion Recovery (FLAIR) MR images. A pathologic area was detected using multithresholding segmentation with morphological operations of MR images. Multiclassifier techniques were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate GBM and normal tissue. GMM features demonstrated the best performance by the comparative study using principal component analysis (PCA) and wavelet based features. For the T1-WI, the accuracy performance was 97.05% (AUC = 92.73%) with 0.00% missed detection and 2.95% false alarm. In the T2-WI, the same accuracy (97.05%, AUC = 91.70%) value was achieved with 2.95% missed detection and 0.00% false alarm. In FLAIR mode the accuracy decreased to 94.11% (AUC = 95.85%) with 0.00% missed detection and 5.89% false alarm. These experimental results are promising to enhance the characteristics of heterogeneity and hence early treatment of GBM.

A noninvasive and reliable surrogate method of determining MGMT status could serve to complement brain tumor biopsy or as an alternative in those patients who have a contraindication to undergo an invasive procedure.The significance of magnetic resonance 3D volumetrics and qualitative imaging features for predicting MGMT methylation status in glioblastoma was evaluated (73.6% accuracy achieved).Our analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were associated with the status of MGMT promoter methylation in glioblastoma.The results of our study provide further evidence of an association between standard preoperative MRI features and MGMT methylation status in glioblastoma. Background and ObjectiveThe O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. However, determination of the MGMT promoter methylation status requires tissue obtained via surgical resection or biopsy. The aim of this study was to assess the ability of quantitative and qualitative imaging variables in predicting MGMT methylation status noninvasively. MethodsA retrospective analysis of MR images from GBM patients was conducted. Multivariate prediction models were obtained by machine-learning methods and tested on data from The Cancer Genome Atlas (TCGA) database. ResultsThe status of MGMT promoter methylation was predicted with an accuracy of up to 73.6%. Experimental analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were the most significant variables in respect to the status of MGMT promoter methylation in GBM. ConclusionsThe obtained results provide further evidence of an association between standard preoperative MRI variables and MGMT methylation status in GBM.

BACKGROUND Dexamethasone is reported to induce both tumor-suppressive and tumor-promoting effects. The purpose of this study was to identify the genomic impact of dexamethasone in glioblastoma stem cell (GSC) lines and its prognostic value; furthermore, to identify drugs that can counter these side effects of dexamethasone exposure. METHODS We utilized 3 independent GSC lines with tumorigenic potential for this study. Whole-genome expression profiling and pathway analyses were done with dexamethasone-exposed and control cells. GSCs were also co-exposed to dexamethasone and temozolomide. Risk scores were calculated for most affected genes, and their associations with survival in The Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data databases. In silico Connectivity Map analysis identified camptothecin as antagonist to dexamethasone-induced negative effects. RESULTS Pathway analyses predicted an activation of dexamethasone network (z-score: 2.908). Top activated canonical pathways included "role of breast cancer 1 in DNA damage response" (P=1.07E-04). GSCs were protected against temozolomide-induced apoptosis when coincubated with dexamethasone. Altered cellular functions included cell movement, cell survival, and apoptosis with z-scores of 2.815, 5.137, and -3.122, respectively. CCAAT/enhancer binding protein beta (CEBPB) was activated in a dose dependent manner specifically in slow-dividing "stem-like" cells. CEBPB was activated in dexamethasone-treated orthotopic tumors. Patients with high risk scores had significantly shorter survival. Camptothecin was validated as potential partial neutralizer of dexamethasone-induced oncogenic effects. CONCLUSIONS Dexamethasone exposure induces a genetic program and CEBPB expression in GSCs that adversely affects key cellular functions and response to therapeutics. High risk scores associated with these genes have negative prognostic value in patients. Our findings further suggest camptothecin as a potential neutralizer of adverse dexamethasone-mediated effects.

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.

TO THE EDITOR Despite consensus that intense, intermittent ultraviolet exposure is a major melanoma risk factor, the role of DNA repair in melanoma pathogenesis remains controversial. UV-characteristic cyclobutane–pyrimidine dimers and pyrimidine–pyrimidone 6,4-photoproducts are absent in most melanomas, and many frequently encountered mutations in melanoma, such as BRAF c.1799 T->A, are not characteristic UV signature mutations(Davies et al., 2002). We set out to explore the role of DNA repair in melanoma via microarray analysis of expression of DNA repair genes in melanoma and non-melanoma skin cancer (NMSC). Our study population included 16 primary cutaneous melanomas (PCMs), 11 squamous cell carcinomas (SCCs), and 15 basal cell carcinomas (BCCs). PCMs included 2 melanomas in situ (MIS), 2 thin ( 4 mm). 40 metastatic melanoma (MM) samples were also analyzed (Supplemental Table S1). The Fanconi Anemia (FA) repair pathway is the primary cellular mechanism for addressing DNA damage related to cross-links, alkylation, and stalled replication forks(Kee and D'Andrea, 2010). We found that expression of FA genes is significantly elevated in MM compared to normal skin. The most melanoma-specific signature was provided by the aggregate expression of FANCL and FANCD2. Combined FANCD2-FANCL expression was at least 50% greater than normal skin in 35 of 40 MM, but in none of the BCCs or SCCs (Figure 1a, Supplemental Table S3). In contrast, no nucleotide excision repair (NER) genes were significantly upregulated in MM. Expression of XPA, XPE, XPG, and RAD23B were significantly decreased in MM (Figure 1a, Supplemental Tables S2, S4). This observation is consistent with the high incidence of melanoma in xeroderma pigmentosum patients with defects in the NER pathway. Figure 1 Fanconi anemia, but not NER, genes are transcriptionally upregulated in melanoma compared to normal skin and NMSC We next examined expression levels of DNA repair genes in PCM. When segregated according to Breslow depth, a stepwise increase in FA gene expression was noted with increasing melanoma thickness (Figure 1b, Supplemental Tables S5 and S6). In MIS, only one FA gene, FANCN, was over-expressed; in intermediate thickness melanomas, three genes (FANCB, G, L) were over-expressed. In thick melanomas, nine FA genes were broadly over-expressed, including FANCA, B, C, D1, D2, G, I, L, N. NER expression was not significantly increased in PCM. We then sought to confirm our DNA microarray findings via quantitative RT-PCR (qRT-PCR) analysis of randomly selected PCM and NMSC. Consistent with the microarray results, normalized mRNA levels of the key downstream mediator FANCD2 were significantly higher in 8 PCM than in 9 NMSC (p=.0001, Figure 1c). Furthermore, there was a trend toward association between Breslow depth and FANCD2 expression level detected by qRT-PCR (data not shown). To assess changes in FA expression at the protein level, we analyzed 4 randomly selected melanomas and 4 NMSCs by immunohistochemistry for expression of FANCD2. These included two thin and two intermediate thickness PCMs, as well as 3 SCCs and 1 BCC (Supplemental Table S7). When comparing PCMs to NMSCs, 45% of melanoma cells had intense homogenous to granular nuclear staining (Figure 2), versus 4.8% in NMSC, (p=.029). When comparing PCMs of differing depth, we found increased FANCD2 staining with increased Breslow thickness. The two thin PCM displayed nuclear staining of FANCD2 in 13% and 31% of cells, respectively, compared to 60 and 75% in intermediate thickness melanomas. No significant elevation of markers of S-phase progression including Ki-67 labeling index (data not shown), or transcript levels of cyclin A, cyclin E, or histone H3, were found in melanoma compared to NMSC (Supplemental Table S8). Thus, we believe that increased FA gene expression in melanoma was not simply due to increased proliferative index. Figure 2 Strong nuclear FANCD2 protein expression is observed in primary melanoma but not NMSC While increased gene expression does not necessarily imply FA pathway hyperactivation, three lines of evidence suggest this is likely the case. First, given that FANCD2-FANCI nuclear complexes are an established hallmark of FA pathway activation(Kee and D'Andrea, 2010), the increased homogenous and granular nuclear staining we observed for FANCD2 protein in melanoma appears consistent with FA activation as well. Second, FA pathway activity correlates closely with level of FA gene transcripts (Hoskins et al., 2008; Taniguchi et al., 2002; Vaughn et al., 1996). Finally, FA activation through over-expression of FA pathway constituents has been reported to confer cellular resistance to DNA damage(Chen et al., 2007; Hazlehurst et al., 2003). These reports suggest that the FA pathway may function in oncogenesis in addition to its previously implicated role of tumor suppressor (Condie et al., 2002). Consistent with this hypothesis, FANCC knockout mice are deficient in STAT-1 mediated IFN-γ signaling (Pang et al., 2000), a pathway that may promote UVB-mediated melanomagenesis (Zaidi et al., 2011). Additionally, activation of the FA pathway could provide resistance to increased endogenous DNA damage typically seen in oncogenic states(Nitta et al., 2010) and confer survival advantage to melanoma cells. Further supporting this hypothesis, activation of the FA pathway has been associated with resistance to a variety of DNA damaging agents including BCNU and temozolomide (Chen et al., 2007), the tumoricidal effect of which is synergistically increased by bortezomib, a FA pathway inhibitor(Jacquemont and Taniguchi, 2007). Recent studies have shed light on possible mechanisms of FA gene upregulation during melanoma pathogenesis. For instance, E2F activates the FANCD2 gene by binding consensus promoter response elements (Hoskins et al., 2008). Similarly, NF-κB binds and activates the FANCD2 promoter (Yarde et al., 2009) as well as mediates nuclear localization of FANCD2 (Ma et al., 2009). Finally, p53 has been shown to bind the FANCC promoter and up-regulate FANCC transcription(Liebetrau et al., 1997). Aberrant regulation of such transcription factors during melanomagenesis may account for increased FA gene transcription that is disassociated from cell-cycle dependent regulation. In summary, we have found that FA DNA repair genes are transcriptionally upregulated in melanoma. Furthermore, increased FA gene expression correlates with increased Breslow thickness. In the context of the broader literature, our results suggest that activation of the FA pathway could contribute to melanomagenesis and resistance to chemotherapy. As such, the FA genes constitute potential targets in the development of novel melanoma therapeutics.

A precise clinically relevant characterization of glioblastoma (GBM) heterogeneity and underlying genomic variability is urgently needed. To evaluate the true potential of radiogenomics, an emerging field for GBM research, we have tested its scope in a pre-clinical setting. First, glioblastoma stem cells (GSCs) are altered to express decreased levels of Periostin (POSTN), previously identified in a radiogenomic screen as a key pro-invasive gene. Second, orthotopic tumors arising from these GSCs, were scanned and analyzed to identify gene specific MRI texture feature (MRTF) signatures, which can simultaneously predict POSTN status in GBM patients. Doxycycline inducible short hairpin RNA mediated knockdown of POSTN in GSC lines were established. 5X10^6 cells were stereotactically implanted in nude mice frontal cortex and randomized into POSTN knockdown and control groups. Monitored tumor growth longitudinally by MRI (n = 3-4 per group). Region of interest (ROI) methods were applied to the rigid registration images based on axial T1-weighted imaging and corresponding fluid-attenuated inversion recovery sequences. After pre-processing, gray level co-occurrence matrices (GLCMs) were generated in 4 angular directions and 3 invariant measures (mean, range and angular variance) of each texture feature is calculated. A matrix of 3x20 invariant features is generated for each gray level. Mean values of normalized MRTFs from edema T1 post and T2, subgroup tumors arising from independent GSCs (p

BACKGROUND: To assess the efficacy of texture analysis (TA) in differentiating Low Grade Gliomas (LGGs) from Normal Appearing White Matter (NAWM) in a cohort of brain cancer patients using T2-FLAIR MR images. Also the impact of the number of gray-levels employed was investigated. METHODS: T2-FLAIR and post-gadolinium (GD) contrast T1-weighted MR images of 11 TCGA patients (mean age 40.8 years, range 27-59.5 years) were retrospectively analyzed. All patients were diagnosed with LGGs (grades II and III). Four different volumes of interest (VOI)s were segmented for each patient by an experienced radiologist: i) tumor, ii)peritumoral WM (pWM), iii) far ipsi-lateral WM (iWM) and iv) contra-lateral WM(cWM). Following VOI segmentation, TA was performed using FLAIR images on MatLab environment. A complete list of invariant texture features derived from the gray-level co-occurrence matrix (GLCM) was calculated. Because the ideal number of gray levels is unknown, we used various gray levels (8, 16, 32, and 64 levels). To assess the efficacy of the calculated features we constructed a decision tree. The model was validated using a 10-fold cross validation of the training data. The accuracy, sensitivity and specificity were calculated. Impact of the number of gray levels on the performance indicators was investigated. RESULTS: For discrimination of LGG from NAWM, TA achieved 100% accuracy. High accuracy (80-87.5% for different gray levels) was achieved for discrimination of all four VOIs. Off note, the peritumoral WM was identified with 86.7% to 93% accuracy from the two remaining NAWM regions combined. Also, from the performance indicators it is evident that the number of gray levels does not affect the calcification. CONCLUSIONS: TA provided excellent accuracy for discrimination between LGG and NAWM. A major strength of the presented work is that it showed high performance in discriminating among normal WM regions depending on their proximity to the tumor.

BACKGROUND: To identify clinically relevant glioblastoma (GBM) sub-classification/subtypes based on radiomic analysis. Integrated genomic analysis has already identified genomic subtypes of GBM as characterized by specific genomic aberrations and mutations. In our study, we seek to demonstrate a GBM sub-classification based on radiomic texture analysis that is both predictive and prognostic. METHODS: We retrospectively identified 80 GBM treatment naive patients from the Cancer Genome Atlas with corresponding MRI data in the Cancer Imaging Archive. We applied texture analysis (528 features) to each tumor. We extracted texture features and performed feature selection. Univariate and multivariate analysis were also performed. Clustering identified radiomic subclasses. RESULTS: Multiple texture features were associated with GBM and we were able to classify GBM into 4 subtypes. However, subclassification into 3 subclasses were associated with differences in patient progression free and overall survival. CONCLUSIONS: GBM subtypes based on radiomic texture signatures were identified that were both predictive and prognostic. These findings are important as this can help determine patient outcome based on imaging (obtained at the time of diagnosis before treatment); this can also help determine response to therapy at the pre-treatment stage to stratify patients and serve as imaging endpoints for clinical trials.

BACKGROUND: FMRI maps commonly show multiple areas of false positive activity. We aimed to identify areas of true fMRI activation by using complex MRI texture analysis (TA) of raw EPI data, thus enhancing the identification of true activity. METHODS: 12 right handed healthy volunteers were recruited (IRB approved). All scans were performed on a 3.0T GE MR scanner. FMRI consisted of a high resolution 3D T1 weighted structural scan and a gradient echo EPI functional scan. FMRI data analysis was performed using SPM8 software for a sentence completion block design task. T map for each individual was displayed using xjview toolbox and thresholded individually to optimize visualization of language area. Each activity cluster was selected and saved. Board-certified neuroradiologists and neuroscientists classified different clusters into two separate groups Expected (E) and Non Expected (NE) based on their anatomical locations. TA was performed using the mean EPI volume for each individual, and 20 invariant texture features were obtained. Efficacy of TA in fMRI was assessed by decision tree and validated using a 4 fold cross validation. RESULTS: fMRI areas of activity in individual scans were identified based on their texture features with 95.3% accuracy, sensitivity 95.3% and specificity 95.3%. Sum of Variance, Cluster shade, sum of average and information measure of correlation were the most important texture features. These features had higher texture values in the NE ROIs indicating more homogeneous areas. Texture features were able to significantly differentiate areas of true versus false activity. CONCLUSIONS: Extracted features indicated that true activity has more heterogeneous texture compared to non-expected activity Quantitative TA of functional MRI using texture feature can enhance the accuracy and sensitivity of the test to true activity and help eliminate false activation

Dexamethasone is routinely administered to manage cerebral edema in glioblastoma (GBM). Its impact on molecular mechanisms driving GBM invasion is unknown. Utilizing radiogenomic strategies, we previously introduced Periostin as key mediator of GBM associated edema and cellular invasion. We observed that multiple molecular targets of Dexamethasone are significantly upregulated and overlap with Periostin correlated genes, including CEBPB. We hypothesized that Dexamethasone, by regulating Periostin correlated genes, promotes a mesenchymal program of tumor cells including Glioblastoma Stem Cells (GSCs). A panel of GSCs were profiled for Periostin correlated genes after Dexamethasone treatment, using microarray, qPCR and Western blotting. Tests for self-renewal (limiting dilution assay), differentiation (neural lineage marker analysis), invasion (Boyden chamber) and proliferation (BrdU) were performed after Dexamethasone exposure. Three independent GSC lines were used to generate orthotopic tumors and mice were then treated with Dexamethasone (intraperitoneal). Differentially expressed genes in GSC derived tumors (total RNA from cells of human origin were isolated using human specific Anti- HLA Class I ABC antibody by FACS) upon Dexamethasone exposure were evaluated with connectivity map (cmap) to screen for specific therapeutics. Cells treated with Dexamethasone showed an increased CEBPB expression, indicating a more aggressive mesenchymal gene signature. Histopathologic validation of orthotopic tumors supported this effect of Dexamethasone. Treatment with specific, FDA approved therapeutics in combination with Dexamethasone suppressed Periostin correlated gene levels and subsequently reduced this mesenchymal shift. In this preclinical study we showed that Dexamethasone promotes a molecular mesenchymal shift by elevating CEBPB expression levels in both, in vitro and in vivo. These results warrant an evaluation of Dexamethasone for managing edema in glioblastoma patients. Neutralizing side effects of Dexamethasone by adding our shortlisted drugs may improve GBM patient care.

Our radiogenomic screen identified Periostin (POSTN) to be highly expressed in glioblastoma (GBM) with high edema/FLAIR. Subsequently, POSTN is implicated in glioblastoma invasion and recruitment of tumor associated macrophages. However, its impact on glioblastoma stem cells (GSC) biology/properties is largely unknown. Here GSC lines with high endogenous POSTN expression levels were used to generate inducible shRNA mediated loss of function (LOF) clones. GSCs with low/undetectable levels of POSTN expression were used to generate Gain of function (GOF) clones. In vitro tests were performed to assess the impact of POSTN LOF and GOF on GSC properties such as self-renewal, BrdU label retention, differentiation potential etc. We show that GSCs with POSTN LOF acquired an increased stem like state with unaltered proliferation rate. When these clones were implanted at orthotopic locations in mouse, POSTN LOF was associated with increased survival of mice. Cells of human origin were isolated from tumors using human specific HLA Class I ABC antibody by FACS. Total RNA from these sorted cells (without cell-culture) were used for expression profile analysis by HTA 2.0 array from affymetrix. Transcriptome analyses of POSTN LOF cells show inhibition of multiple mesenchymal associated networks such as CEBPB (a master regulator of mesenchymal program in GBM), PRDM1, TGFB1, FGF2, WNT3A, HIF1A etc. with concomitant activation of MYCN network, indicating a mesenchymal to proneural switch. We have generated POSTN and CEBPB dual knockdown clones and are currently investigating the molecular link between these two key mesenchymal genes in GSC context. Our results show that POSTN maintains a mesenchymal program in GSC by coopting with intracellular factors such as CEBPB. Therefore, neutralizing extracellular POSTN will have far reaching impact in terms of therapeutics where while blocking GBM invasion and it may simultaneously block a mesenchymal program in stem cell niche in GBM.

BACKGROUND Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM. METHODS We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image. RESULTS Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P < .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm(3) and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively). CONCLUSIONS Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.

Glioblastoma (GBM) is the most aggressive human brain tumor. Although several molecular subtypes of GBM are recognized, a robust molecular prognostic marker has yet to be identified. Here, we report that the stemness regulator Sox2 is a new, clinically important target of microRNA-21 (miR-21) in GBM, with implications for prognosis. Using the MiR-21–Sox2 regulatory axis, approximately half of all GBM tumors present in the Cancer Genome Atlas (TCGA) and in-house patient databases can be mathematically classified into high miR-21/low Sox2 (Class A) or low miR-21/high Sox2 (Class B) subtypes. This classification reflects phenotypically and molecularly distinct characteristics and is not captured by existing classifications. Supporting the distinct nature of the subtypes, gene set enrichment analysis of the TCGA dataset predicted that Class A and Class B tumors were significantly involved in immune/inflammatory response and in chromosome organization and nervous system development, respectively. Patients with Class B tumors had longer overall survival than those with Class A tumors. Analysis of both databases indicated that the Class A/Class B classification is a better predictor of patient survival than currently used parameters. Further, manipulation of MiR-21–Sox2 levels in orthotopic mouse models supported the longer survival of the Class B subtype. The MiR-21–Sox2 association was also found in mouse neural stem cells and in the mouse brain at different developmental stages, suggesting a role in normal development. Therefore, this mechanism-based classification suggests the presence of two distinct populations of GBM patients with distinguishable phenotypic characteristics and clinical outcomes.SIGNIFICANCE STATEMENTMolecular profiling-based classification of glioblastoma (GBM) into four subtypes has substantially increased our understanding of the biology of the disease and has pointed to the heterogeneous nature of GBM. However, this classification is not mechanism based and its prognostic value is limited. Here, we identify a new mechanism in GBM (the miR-21–Sox2 axis) that can classify ∼50% of patients into two subtypes with distinct molecular, radiological, and pathological characteristics. Importantly, this classification can predict patient survival better than the currently used parameters. Further, analysis of the miR-21-Sox2 relationship in mouse neural stem cells and in the mouse brain at different developmental stages indicates that miR-21 and Sox2 are predominantly expressed in mutually exclusive patterns, suggesting a role in normal neural development.

BACKGROUND: Some investigators studied the impact of postoperative infarction, infection and surgical complications on Glioblastoma patient survival using Magnetic Resonance Diffusion-Weighted Imaging (MR-DWI). We attempt to investigate the prognostic role of postoperative DWI hyperintensity in patients with GBM. METHODS AND MATERIALS: We obtained immediate postoperative (24-72 hours post resection) brain MRI images of 60 GBM patients from our institution's imaging archive and The Cancer Imaging Archive (TCIA). Using 3D Slicer 4.3.1 (http://www.slicer.org) 2 trained neuroradiologists experienced in tumor volumetry segmented residual enhancement on post-contrast T1 Weighted Imaging (T1WI) and pericavitarian edema/invasion on T2 FLAIR sequence. Segmentation maps were then overlaid over Diffusion Weighted Images (DWI) and Apparent Diffusion Coefficient (ADC) maps. ADC values around resection cavity were obtained. We stratified patients into two categories; facilitated diffusion group and restricted diffusion group. We included different variables in our analysis such as, age, gender, preoperative KPS status, tumor location, preoperative volumetric analysis and post operative volumetric data (quantitative evaluation of extent of resection). We used Kaplan Meier curves for survival analysis after adjustment for potential confounding factors, such as age, tumor location and extent of resection. RESULTS: We created a new non-invasive imaging biomarker that can predict patient outcome in immediate postoperative setting. CONCLUSION: DWI radiophenotypes and ADC map values can be used as a non-invasive prognostic tool for patient overall survival and progression-free survival.

BACKGROUND: Several studies have investigated the prognostic value of pre-operative volumes of abnormal FLAIR signal, tumor enhancement and necrosis in the prediction of patient survival. We seek to explore the role of abnormal FLAIR signal as a prognostic biomarker in immediate post-resection Magnetic Resonance Imaging (MRI) scans. METHODS AND MATERIALS: We obtained pre-operative and immediate post-operative MRI of 60 GBM patients available on The Cancer Imaging Archive (TCIA) and in our institution's imaging archive. We used 3D Slicer 4.3.1 software for volumetric quantification of tumor compartments. Two trained neuroradiologists delineated peritumoral edema/invasion using T2 FLAIR sequence. Pre-operative tumor enhancement and necrosis were segmented using post-contrast T1 Weighted Images (T1WI). Post-operatively, we used immediate post-operative T2 FLAIR sequence (24-72 hours post-resection) for volume quantification of pericavitarian edema/invasion. We used Kaplan-Meier curves for survival analysis. We included the volumes of pre-operative and post-operative scans independently and in groups and subgroups. The association between the quantitative variables and patient survival were performed using multivariate Cox regression analysis. RESULTS: Post-operative absolute volumes and ratios of pre-operative to post-operative volumes are correlated with patient overall survival and progression-free survival. CONCLUSION: Immediate post-operative volume of abnormal FLAIR signal can be used as a prognostic imaging biomarker.

PURPOSE:To create an imaging genomic map, linking MR imaging traits with gene and miRNA expression profiles,in GBM patients to determine genomic correlates of diffusion radiophenotype to find new genomic targets. Decreases in diffusion in GBM,is associated with increased cellular density and higher nuclear to cytoplasm ratio.Here,we present the first study examining in a quantitative way MRI-diffusion genomics in GBM to determine targetable genomic biomarkers in GBM. MATERIALS AND METHODS: We identified 80 treatment-naive GBM patients from The Cancer Genome Atlas(TCGA)who had both gene and miRNA expression profiles and pretreatment MR-neuroimaging specifically ADC maps. Image segmentation analysis was done in Slicer 3.6 using segmentation module,Fluid-Attenuated Inversion Recovery(FLAIR)was used for segmentation of the edema and post-contrast T1 weighted-imaging(T1WI)for segmentation of enhancement and necrosis.Diffusion was analyzed in Olea Sphere 2.3 and Conventional FLAIR/post contrast T1WI was registered to DWI/ADC maps.ADC,FLAIR,T1 Gadolinium enhancement values were measured using the ROI based method,in the peri-tumoral edema/non-enhancing tumor and the enhancing tumor zones,dividing the peri-tumoral edema/non-enhancing tumor into 3 zones each of 1 cm width,3 ROI measurements were taken from each zone.Multiple quantitative imaging features were identified and combined to create the imaging biomarker signature predictive for the invasion and poor prognosis of the tumor. All the genomic data was also analyzed to determine the most upregulated mRNAs/miRNAs using ingenuity pathway analysis(IPA) in those with restricted diffusion. RESULTS: Quantitative ADC values were in high correlation with biologically concordant functional genomic targets predicting the invasiveness and progression. CONCLUSIONS: The diffusion and conventional MR radiophenotype identified genes and miRNAs and corresponding molecular networks that were highly associated with tumor invasion.By these means we were able to identify possible key genes and miRNAs involved in the latter regulation.

PURPOSE: To create a diffusion and conventional MR imaging biomarker signature in order to identify those Glioblastoma (GBM) patients with EGFR mutation status. EGFR is the cell-surface receptor for members of the epidermal growth factor family(EGF-family)of extracellular protein ligands,a subfamily of receptor tyrosine kinases. EGFR gene expression is present in 40% of GBM patients.Identification of EGFR as an oncogene has led to the development of anticancer therapeutics directed against EGFR.Thus,a non-invasive imaging surrogate that predicts EGFR mutation status will help stratify patients into therapy and clinical trials. MATERIALS AND METHODS: We identified 80 treatment-naive patients from TCGA who had both gene and microRNA expression profiles including the EGFR mutation status and pretreatment MRI from The Cancer Imaging Archive (TCIA). Qualitative VASARI imaging features for these 80 patients were assessed by 3 independent neuroradiologists and consensus was reached. Quantitative volumetric analysis was done in the 3D Slicer software 3.6 using segmentation module.Fluid Attenuated Inversion Recovery (FLAIR)was used for segmentation of the edema and post-contrast T1 weighted imaging(T1W1)for segmentation of enhancement and necrosis.Diffusion was analyzed in Olea Sphere 2.3 and Conventional FLAIR/post- contrast T1WI was registered to DWI/ADC maps. ADC, FLAIR, T1 Gadolinium enhancement values were measured using the ROI based method, in the perilesional edema/non-enhancing tumor and the enhancing tumor zones, dividing the perilesional edema/non-enhancing tumor into 3 zones each of 1 cm width, 3 ROI measurements were taken from each zone. Each quantitative feature was correlated to EGFR mutation status to create the imaging biomarker signature predictive of EGFR mutation status. Survival analysis was done in all cases. RESULTS: A diffusion and conventional MR imaging biomarker signature was created that predicted EGFR mutation status. CONCLUSIONS: EGFR mutation status plays an important role in predictive and prognostic stratification of patients with GBM. The identification of a non-invasive biomarker signature as a surrogate for EGFR mutation can help stratify patients in specific therapy and predict response to therapy.

Glioblastoma multiforme (GBM) tumors are the most common malignant primary brain tumors in adults. Although many GBM tumors are believed to be caused by self-renewing, glioblastoma-derived stem-like cells (GSCs), the mechanisms that regulate self-renewal and other oncogenic properties of GSCs are only now being unraveled. Here we showed that GSCs derived from GBM patient specimens express varying levels of the transcriptional repressor repressor element 1 silencing transcription factor (REST), suggesting heterogeneity across different GSC lines. Loss- and gain-of-function experiments indicated that REST maintains self-renewal of GSCs. High REST-expressing GSCs (HR-GSCs) produced tumors histopathologically distinct from those generated by low REST-expressing GSCs (LR-GSCs) in orthotopic mouse brain tumor models. Knockdown of REST in HR-GSCs resulted in increased survival in GSC-transplanted mice and produced tumors with higher apoptotic and lower invasive properties. Conversely, forced expression of exogenous REST in LR-GSCs produced decreased survival in mice and produced tumors with lower apoptotic and higher invasive properties, similar to HR-GSCs. Thus, based on our results, we propose that a novel function of REST is to maintain self-renewal and other oncogenic properties of GSCs and that REST can play a major role in mediating tumorigenicity in GBM. STEM CELLS 2012;30:405-414.

There was a spelling error in the second author's name. The correct spelling is Bhanu Mahajan. The correct citation is: Zinn PO, Mahajan B, Sathyan P, Singh SK, Majumder S, et al. (2011) Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme. PLoS ONE 6(10): e25451. doi:10.1371/journal.pone.0025451

The authors would like to add Maria-del-Mar Inda and Jill Wykosky to the byline. The updated byline is: Masayuki Nitta1#, David Kozono1,2#, Richard Kennedy3, Jayne Stommel4, Kimberly Ng1, Pascal O. Zinn1, Deepa Kushwaha1, Santosh Kesari5, Maria-del-Mar Inda6, Jill Wykosky6, Frank Furnari6, Katherine A. Hoadley7, Lynda Chin4, Ronald A. DePinho4, Webster K. Cavenee6, Alan D'Andrea1, Clark C. Chen1,8* The two new authors should be added to the author contributions "Contributed reagents/materials/analysis tools" section. The section should read "Contributed reagents/materials/analysis tools: JS SK M-d-MI JW FBF LC RAD WKC ADD CCC."

Background Microclip placement during AVM resection is generally accepted to be a safe practice in neurosurgery. Here, we describe an unusual complication involving cranio-spinal clip migration discovered five years after the initial AVM surgery. Case Presentation A 53-year-old man underwent resection of a superior vermian AVM that required the placement of two microclips during the procedure. Five years after surgery, the patient suffered from descending sensory radiculopathy that resolved spontaneously. The workup revealed cranio-spinal migration of one of the previously placed microclips. Conclusions AVM clip migration is a rare phenomenon; however, the diagnosis should be entertained in patients with posterior fossa instrumentation who suffer from unusual neurologic symptoms.

Introduction Posterior reversible leukoencephalopathy syndrome refers to a constellation of neurologic symptoms related to temporary white matter changes. The disease typically presents in the context of an abrupt and drastic elevation in blood pressure (>180/110 mmHg). We report an unusual case of posterior reversible leukoencephalopathy syndrome in a post-operative setting, with a blood pressure parameter generally tolerated by most patients. Case presentation We report the case of a 22-year-old Caucasian man who suffered acute onset visual acuity loss four days after an ileocecal anastomosis. A head magnetic resonance imaging scan revealed findings typically associated with posterior reversible leukoencephalopathy syndrome. His symptoms developed in the context of blood pressure parameters that are typically well tolerated in a post-operative setting (150-160/80-90 mmHg). He did not have a history of renal failure or immunosuppression. His symptoms resolved with aggressive blood pressure management. Conclusions Posterior reversible leukoencephalopathy syndrome can occur in a post-operative setting with blood pressure parameters typically well-tolerated in most post-surgical patients. Timely diagnosis and treatment will minimize the risk of permanent neurologic injury.