Coexisting Biopsy-Diagnosed Dementia and Glioblastoma.

Both glioblastoma (GBM) and dementia are devastating diseases with limited treatments that are usually not curative. Having clinically diagnosed dementia with an associated biopsy-proven etiology and a coexisting GBM diagnosis is a rare occurrence. The relationship between the development of neurodegenerative dementia and GBM is unclear, as there are conflicting reports in the literature. We present two cases of simultaneous biopsy-proven dementia, one with Alzheimer's disease (AD) and GBM, and one with cerebral amyloid angiopathy (CAA) and GBM. We discuss how these diseases may be associated. Whether one pathologic process begins first or develops concurrently is unknown, but certain molecular pathways of dementia and GBM appear directly related while others inversely related. Further investigations of these close molecular relationships between dementia and GBM could lead to development of improved diagnostic tools and therapeutic interventions for both diseases.

Effects of gene replacement therapy with resamirigene bilparvovec (AT132) on skeletal muscle pathology in X-linked myotubular myopathy: results from a substudy of the ASPIRO open-label clinical trial.

BACKGROUND: X-linked myotubular myopathy (XLMTM) is a rare, life-threatening congenital muscle disease caused by mutations in the MTM1 gene that result in profound muscle weakness, significant respiratory insufficiency, and high infant mortality. There is no approved disease-modifying therapy for XLMTM. Resamirigene bilparvovec (AT132; rAAV8-Des-hMTM1) is an investigational adeno-associated virus (AAV8)-mediated gene replacement therapy designed to deliver MTM1 to skeletal muscle cells and achieve long-term correction of XLMTM-related muscle pathology. The clinical trial ASPIRO (NCT03199469) investigating resamirigene bilparvovec in XLMTM is currently paused while the risk:benefit balance associated with this gene therapy is further investigated. METHODS: Muscle biopsies were taken before treatment and 24 and 48 weeks after treatment from ten boys with XLMTM in a clinical trial of resamirigene bilparvovec (ASPIRO; NCT03199469). Comprehensive histopathological analysis was performed. FINDINGS: Baseline biopsies uniformly showed findings characteristic of XLMTM, including small myofibres, increased internal or central nucleation, and central aggregates of organelles. Biopsies taken at 24 weeks post-treatment showed marked improvement of organelle localisation, without apparent increases in myofibre size in most participants. Biopsies taken at 48 weeks, however, did show statistically significant increases in myofibre size in all nine biopsies evaluated at this timepoint. Histopathological endpoints that did not demonstrate statistically significant changes with treatment included the degree of internal/central nucleation, numbers of triad structures, fibre type distributions, and numbers of satellite cells. Limited (predominantly mild) treatment-associated inflammatory changes were seen in biopsy specimens from five participants. INTERPRETATION: Muscle biopsies from individuals with XLMTM treated with resamirigene bilparvovec display statistically significant improvement in organelle localisation and myofibre size during a period of substantial improvements in muscle strength and respiratory function. This study identifies valuable histological endpoints for tracking treatment-related gains with resamirigene bilparvovec, as well as endpoints that did not show strong correlation with clinical improvement in this human study. FUNDING: Astellas Gene Therapies (formerly Audentes Therapeutics, Inc.).

Magnetic Resonance Imaging of Iron Metabolism with T2* Mapping Predicts an Enhanced Clinical Response to Pharmacologic Ascorbate in Patients with GBM.

PURPOSE: Pharmacologic ascorbate (P-AscH-) is hypothesized to be an iron (Fe)-dependent tumor-specific adjuvant to chemoradiation in treating glioblastoma (GBM). This study determined the efficacy of combining P-AscH- with radiation and temozolomide in a phase II clinical trial while simultaneously investigating a mechanism-based, noninvasive biomarker in T2* mapping to predict GBM response to P-AscH- in humans. PATIENTS AND METHODS: The single-arm phase II clinical trial (NCT02344355) enrolled 55 subjects, with analysis performed 12 months following the completion of treatment. Overall survival (OS) and progression-free survival (PFS) were estimated with the Kaplan-Meier method and compared across patient subgroups with log-rank tests. Forty-nine of 55 subjects were evaluated using T2*-based MRI to assess its utility as an Fe-dependent biomarker. RESULTS: Median OS was estimated to be 19.6 months [90% confidence interval (CI), 15.7-26.5 months], a statistically significant increase compared with historic control patients (14.6 months). Subjects with initial T2* relaxation < 50 ms were associated with a significant increase in PFS compared with T2*-high subjects (11.2 months vs. 5.7 months, P < 0.05) and a trend toward increased OS (26.5 months vs. 17.5 months). These results were validated in preclinical in vitro and in vivo model systems. CONCLUSIONS: P-AscH- combined with temozolomide and radiotherapy has the potential to significantly enhance GBM survival. T2*-based MRI assessment of tumor iron content is a prognostic biomarker for GBM clinical outcomes. See related commentary by Nabavizadeh and Bagley, p. 255.

Ventriculus terminalis cyst in an infant: a case report.

BACKGROUND: Filar cysts are frequently found on neonatal ultrasound and are physiologically involuting structures with natural resolution. Hence, there has been no previous histologic correlation. Ventriculus terminalis is a focal central canal dilation in the conus medullaris and usually not clinically significant. Extra-axial cyst at the conus-filum junction connected to ventriculus terminalis is extremely rare, especially when associated with tethered lipomatous filum terminale and with progressive cyst enlargement. CASE PRESENTATION: A Caucasian female neonate with abnormal gluteal cleft had ventriculus terminalis cyst with an extra-axial cyst at the conus-filar junction and taut lipomatous filum on ultrasound examination and magnetic resonance imaging. This persisted at 6-month follow up imaging. In light of the nonresolving extra-axial mass and thick taut lipomatous filum, the child underwent L1-L3 osteoplastic laminectomies. The extra-axial cyst expanded after bony decompression and furthermore on dural opening; visualized on ultrasound. It communicated with the central canal and was documented with intraoperative photomicrographs. It was excised and filum sectioned. Histological immunostaining of the cyst wall showed neuroglial and axonal elements. The child did well without deficits at 4-year follow up with normal urodynamics. CONCLUSION: Progression dilation of ventriculus terminalis and extra-axial conofilar cyst with tethered lipomatous filum will likely progress to clinical significance and require surgical intervention. The embryologic basis for this pathology is discussed, with literature review.

Challenging Neuromuscular Disease Cases.

The diagnosis of neuromuscular disorders requires a thorough history including family history and examination, with the next steps broadened now beyond electromyography and neuropathology to include genetic testing. The challenge in diagnosis can often be putting all the information together. With advances in genetic testing, some diagnoses that adult patients may have received as children deserve a second look and may result in diagnoses better defined or alternative diagnoses made. Clearly defining or redefining a diagnosis can result in understanding of potential other systems involved, prognosis, or potential treatments. This article presents several cases and approach to diagnosis as well as potential treatment and prognostic concerns, including seipinopathy, congenital myasthenic syndrome, central core myopathy, and myotonic dystrophy type 2.

The inflammatory pathology of dysferlinopathy is distinct from calpainopathy, Becker muscular dystrophy, and inflammatory myopathies.

The descriptions of muscle pathology in dysferlinopathy patients have classically included an inflammatory infiltrate that can mimic inflammatory myopathies. Based on over 20 years of institutional experience in evaluating dystrophic and inflammatory myopathy muscle biopsies at the University of Iowa, we hypothesized the inflammatory histopathology of dysferlinopathy is more similar to limb-girdle pattern muscular dystrophies such as calpainopathy and Becker muscular dystrophy, and distinct from true inflammatory myopathies. Muscle biopsies from 32 dysferlinopathy, 30 calpainopathy, 30 Becker muscular dystrophy, and 30 inflammatory myopathies (15 each of dermatomyositis and inclusion body myositis) were analyzed through digital quantitation of CD3, CD4, CD8, CD20, and PU.1 immunostaining. The expression of MHC class I and deposition of complement C5b-9 was also evaluated. Dysferlinopathy, calpainopathy, and Becker muscular dystrophy muscle biopsies had similar numbers of inflammatory cell infiltrates and significantly fewer CD3+ T-lymphocytes than dermatomyositis (p = 0.05) and inclusion body myositis (p < 0.0001) biopsies. There was no statistically significant difference in the number of PU.1+ macrophages identified in any diagnostic group. MHC class I expression was significantly lower in the limb-girdle pattern muscular dystrophies compared to the inflammatory myopathies (p < 0.0001). In contrast, complement C5b-9 deposition was similar among dysferlinopathy, dermatomyositis, and inclusion body myositis biopsies but significantly greater than calpainopathy and Becker muscular dystrophy biopsies (p = 0.05). Compared to calpainopathy, Becker muscular dystrophy, and inflammatory myopathies, the unique profile of minimal inflammatory cell infiltrates, absent to focal MHC class I, and diffuse myofiber complement C5b-9 deposition is the pathologic signature of dysferlinopathy muscle biopsies.

Catalase Overexpression Drives an Aggressive Phenotype in Glioblastoma.

Glioblastoma remains the deadliest form of brain cancer, largely because these tumors become resistant to standard of care treatment with radiation and chemotherapy. Intracellular production of reactive oxygen species (ROS) is necessary for chemo- and radiotherapy-induced cytotoxicity. Here, we assessed whether antioxidant catalase (CAT) affects glioma cell sensitivity to temozolomide and radiation. Using The Cancer Genome Atlas database, we found that CAT mRNA expression is upregulated in glioma tumor tissue compared with non-tumor tissue, and the level of expression negatively correlates with the overall survival of patients with high-grade glioma. In U251 glioma cells, CAT overexpression substantially decreased the basal level of hydrogen peroxide, enhanced anchorage-independent cell growth, and facilitated resistance to the chemotherapeutic drug temozolomide and ionizing radiation. Importantly, pharmacological inhibition of CAT activity reduced the proliferation of glioma cells isolated from patient biopsy samples. Moreover, U251 cells overexpressing CAT formed neurospheres in neurobasal medium, whereas control cells did not, suggesting that the radio- and chemoresistance conferred by CAT may be due in part to the enrichment of glioma stem cell populations. Finally, CAT overexpression significantly decreased survival in an orthotopic mouse model of glioma. These results demonstrate that CAT regulates chemo- and radioresistance in human glioma.

Vascular Involvement in Neurosarcoidosis: Early Experiences From Intracranial Vessel Wall Imaging.

BACKGROUND AND OBJECTIVES: Cerebrovascular manifestations in neurosarcoidosis (NS) were previously considered rare but are being increasingly recognized. We report our preliminary experience in patients with NS who underwent high-resolution vessel wall imaging (VWI). METHODS: A total of 13 consecutive patients with NS underwent VWI. Images were analyzed by 2 neuroradiologists in consensus. The assessment included segment-wise evaluation of larger- and medium-sized vessels (internal carotid artery, M1-M3 middle cerebral artery; A1-A3 anterior cerebral artery; V4 segments of vertebral arteries; basilar artery; and P1-P3 posterior cerebral artery), lenticulostriate perforator vessels, and medullary and deep cerebral veins. Cortical veins were not assessed due to flow-related artifacts. Brain biopsy findings were available in 6 cases and were also reviewed. RESULTS: Mean patient age was 54.9 years (33-71 years) with an M:F of 8:5. Mean duration between initial diagnosis and VWI study was 18 months. Overall, 9/13 (69%) patients had vascular abnormalities. Circumferential large vessel enhancement was seen in 3/13 (23%) patients, whereas perforator vessel involvement was seen in 6/13 (46%) patients. Medullary and deep vein involvement was also seen in 6/13 patients. In addition, 7/13 (54%) patients had microhemorrhages in susceptibility-weighted imaging, and 4/13 (31%) had chronic infarcts. On biopsy, 5/6 cases showed perivascular granulomas with vessel wall involvement in all 5 cases. DISCUSSION: Our preliminary findings suggest that involvement of intracranial vascular structures may be a common finding in patients with NS and should be routinely looked for. These findings appear concordant with previously reported autopsy literature and need to be validated on a larger scale.

Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading.

Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated with molecular features following the latest WHO criteria. We first propose a novel over-segmentation strategy for region-of-interest (ROI) selection in large histopathology whole slide images (WSIs). A Deep Neural Network (DNN)-based classification method then fuses molecular features with cellularity features to improve tumor classification performance. We evaluate the proposed method with 549 patient cases from The Cancer Genome Atlas (TCGA) dataset for evaluation. The cross validated classification accuracies are 93.81% for lower-grade glioma (LGG) and high-grade glioma (HGG) using a regular DNN, and 73.95% for LGG II and LGG III using a residual neural network (ResNet) DNN, respectively. Our experiments suggest that the type of deep learning has a significant impact on tumor subtype discrimination between LGG II vs. LGG III. These results outperform state-of-the-art methods in classifying LGG II vs. LGG III and offer competitive performance in distinguishing LGG vs. HGG in the literature. In addition, we also investigate molecular subtype classification using pathology images and cellularity information. Finally, for the first time in literature this work shows promise for cellularity quantification to predict brain tumor grading for LGGs with IDH mutations.

A Review of Clinical and Imaging Findings in Tumefactive Demyelination.

OBJECTIVE. Tumefactive demyelination mimics primary brain neoplasms on imaging, often necessitating brain biopsy. This article reviews the literature for the clinical and radiologic findings of tumefactive demyelination in various disease processes to facilitate identification of tumefactive demyelination on imaging. CONCLUSION. Both clinical and radiologic findings must be integrated to distinguish tumefactive demyelinating lesions from similarly appearing lesions on imaging. Further research on the immunopathogenesis of tumefactive demyelination and associated conditions will elucidate their interrelationship.

Teaching Pathology in an Integrated Preclinical Medical School Curriculum and Adaptations to COVID-19 Restrictions.

The coronavirus disease 2019 (COVID-19) pandemic has had a major impact on education at all age levels, including professional schools and health professions programs. We describe the experience of adapting preclinical medical school courses within an integrated curriculum to virtual instruction. A major feature of two of the courses were pathology small groups adapted from pathology courses in the previous medical school curriculum. These small groups were designed to use facilitated groups of 8 to 10 students. With a sudden change to virtual learning, these small groups were shifted to large group virtual sessions. In general, the conversion went well, with ongoing optimization of the format of the large group sessions mainly occurring over the first several sessions. End-of-course student evaluations were generally positive, but with a preference toward returning to live sessions in the future. Scores on 5 multiple choice examinations in the spring 2020 course were essentially identical in mean, standard deviation, and distribution to examinations in the previous 2 years of the course that had similar layout and topic organization. We discuss the challenges and successes of the switch to virtual instruction and of teaching pathology content within an integrated medical school curriculum.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.

OBJECTIVE: H3K27M mutation in gliomas has prognostic implications. Previous magnetic resonance imaging (MRI) studies have reported variable rates of tumoral enhancement, necrotic changes, and peritumoral edema in H3K27M-mutant gliomas, with no distinguishing imaging features compared with wild-type gliomas. We aimed to construct an MRI machine learning (ML)-based radiomic model to predict H3K27M mutation in midline gliomas. METHODS: A total of 109 patients from 3 academic centers were included in this study. Fifty patients had H3K27M mutation and 59 were wild-type. Conventional MRI sequences (T1-weighted, T2-weighted, T2-fluid-attenuated inversion recovery, postcontrast T1-weighted, and apparent diffusion coefficient maps) were used for feature extraction. A total of 651 radiomic features per each sequence were extracted. Patients were randomly selected with a 7:3 ratio to create training (n = 76) and test (n = 33) data sets. An extreme gradient boosting algorithm (XGBoost) was used in ML-based model development. Performance of the model was assessed by area under the receiver operating characteristic curve. RESULTS: Pediatric patients accounted for a larger proportion of the study cohort (60 pediatric [55%] vs. 49 adult [45%] patients). XGBoost with additional feature selection had an area under the receiver operating characteristic curve of 0.791 and 0.737 in the training and test data sets, respectively. The model achieved accuracy, precision (positive predictive value), recall (sensitivity), and F1 (harmonic mean of precision and recall) measures of 72.7%, 76.5%, 72.2%, and 74.3%, respectively, in the test set. CONCLUSIONS: Our multi-institutional study suggests that ML-based radiomic analysis of multiparametric MRI can be a promising noninvasive technique to predict H3K27M mutation status in midline gliomas.

Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging.

OBJECTIVE: Invasion of brain parenchyma by meningioma can be a critical factor in surgical planning. The aim of this study was to determine the diagnostic utility of first-order texture parameters derived from both whole tumor and single largest slice of T1-contrast enhanced (T1-CE) images in differentiating meningiomas with and without brain invasion based on histopathology demonstration. METHODS: T1-CE images of a total of 56 cases of grade II meningiomas with brain invasion (BI) and 52 meningiomas (37 grade I and 15 grade II) with no brain invasion (NBI) were analyzed. Filtration-based first-order histogram derived texture parameters were calculated both for whole tumor volume and largest axial cross-section. Random forest models were constructed both for whole tumor volume and largest axial cross-section individually and were assessed using a 5-fold cross validation with 100 repeats. RESULTS: In detection of brain invasion, random forest model based on whole tumor segmentation had an AUC of 0.988 (95 % CI 0.976-1.00) with a cross validated value of 0.74 (95 % CI 0.45-0.96). For differentiation of grade I meningiomas from grade II meningiomas with brain invasion, the AUC was 0.999 (95 % CI 0.995-1.00) and 0.81 (95 % CI 0.61-0.99) in the training and validation cohorts, respectively. Similarly, when using only the single largest slice, the cross-validated AUC to distinguish BI versus NBI and BI versus grade I meningiomas was 0.67 (95 % CI 0.47, 0.92 and 0.78 (95 % CI 0.52, 0.95) respectively. CONCLUSION: Radiomics based feature analysis applied on routine MRI post-contrast images may be helpful to predict presence of brain invasion in meningioma, possibly with better performance when comparing BI versus grade I meningiomas.

Liver metastases from pituitary carcinomas mimicking visceral well-differentiated neuroendocrine tumors: a series of four cases.

BACKGROUND: Pathologists frequently encounter neuroendocrine tumors (NETs) presenting as multiple liver masses in routine practice. Most often, these are well-differentiated tumors with characteristic histologic features. In contrast, pituitary carcinoma is very rare, and there is limited data on its natural history and pathologic characterization. METHODS: The aim of this study was to describe clinical characteristics, histomorphology, immunophenotype and follow-up of pituitary carcinoma involving the liver and mimicking well-differentiated NETs of visceral origin. We selected a group of well-differentiated NETs of the pancreas to use as immunophenotypic controls. We identified 4 patients (age range, 51 to 73) with pituitary corticotroph carcinoma with liver metastases. Three patients presented with Cushing syndrome. RESULTS: All cases histologically resembled well-differentiated NETs of visceral origin with Ki-67 proliferation indices of 5-42% and expression of T-PIT; metastatic tumors were not immunoreactive with CDX2, Islet 1 or TTF-1. CONCLUSIONS: Frequently, these cases display adrenocorticotropic hormone (ACTH) secretion and pituitary-specific transcription factor immunohistochemistry may be used as a reliable marker to distinguish metastatic pituitary carcinoma from NETs of visceral origin in addition to delineating a corticotroph carcinoma from somatotroph, lactotroph, thyrotroph, and gonadotroph lineage. Although rare, the differential diagnosis of pituitary carcinoma should be considered in metastatic well-differentiated NETs in which the site of origin is uncertain. In summary, pituitary corticotroph carcinoma can metastasize to the liver and mimic well-differentiated NET.

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media.

Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org . We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

Survival after reoperation for recurrent glioblastoma.

Determining which patients will benefit from reoperation for recurrent glioblastoma remains difficult and the impact of the volume of FLAIR signal hyperintensity is not well known. The primary purpose of this study is to analyze the impact of preoperative volume of FLAIR hyperintensity on prognosis. 37 patients who underwent a reoperation for recurrent glioblastoma after initial gross total resection followed by standard chemoradiation were retrospectively reviewed. Volumetric analysis of preoperative MR images from the initial and second surgery was performed and correlated with clinical data. Survival probabilities were estimated using the Kaplan-Meier method and Cox regression to assess the effect of risk factors on time to reoperation (TTR), progression-free survival (PFS) after reoperation, and overall survival (OS). The volumes of FLAIR signal hyperintensity prior to the initial surgery and reoperation were not associated with prognosis. TTR and OS were significantly affected by the preoperative enhancement volume at the initial surgery, with increasing volumes yielding poorer prognosis. Patients with tumor in critical/eloquent areas were found to have a worse prognosis. Median TTR was 11 months, median PFS after reoperation was 3 months, and OS in patients undergoing a reoperation was 21 months. The results suggest FLAIR signal change seen in patients with glioblastoma does not influence time to reoperation, progression-free survival, or overall survival. These findings suggest the amount of FLAIR signal change should not greatly influence a surgeon's decision to perform a second surgical resection compare to other factors, and when appropriate, aggressive surgical intervention should be considered.

First-in-Human Phase I Clinical Trial of Pharmacologic Ascorbate Combined with Radiation and Temozolomide for Newly Diagnosed Glioblastoma.

PURPOSE: Standard treatment for glioblastoma (GBM) includes surgery, radiation therapy (RT), and temozolomide (TMZ), yielding a median overall survival (OS) of approximately 14 months. Preclinical models suggest that pharmacologic ascorbate (P-AscH-) enhances RT/TMZ antitumor effect in GBM. We evaluated the safety of adding P-AscH- to standard RT/TMZ therapy. PATIENTS AND METHODS: This first-in-human trial was divided into an RT phase (concurrent RT/TMZ/P-AscH-) and an adjuvant (ADJ) phase (post RT/TMZ/P-AscH- phase). Eight P-AscH- dose cohorts were evaluated in the RT phase until targeted plasma ascorbate levels were achieved (≥20 mmol/L). In the ADJ phase, P-AscH- doses were escalated in each subject at each cycle until plasma concentrations were ≥20 mmol/L. P-AscH- was infused 3 times weekly during the RT phase and 2 times weekly during the ADJ phase continuing for six cycles or until disease progression. Adverse events were quantified by CTCAE (v4.03). RESULTS: Eleven subjects were evaluable. No dose-limiting toxicities occurred. Observed toxicities were consistent with historical controls. Adverse events related to study drug were dry mouth and chills. Targeted ascorbate plasma levels of 20 mmol/L were achieved in the 87.5 g cohort; diminishing returns were realized in higher dose cohorts. Median progression-free survival (PFS) was 9.4 months and median OS was 18 months. In subjects with undetectable MGMT promoter methylation (n = 8), median PFS was 10 months and median OS was 23 months. CONCLUSIONS: P-AscH-/RT/TMZ is safe with promising clinical outcomes warranting further investigation.

Glioma grading using structural magnetic resonance imaging and molecular data.

A glioma grading method using conventional structural magnetic resonance image (MRI) and molecular data from patients is proposed. The noninvasive grading of glioma tumors is obtained using multiple radiomic texture features including dynamic texture analysis, multifractal detrended fluctuation analysis, and multiresolution fractal Brownian motion in structural MRI. The proposed method is evaluated using two multicenter MRI datasets: (1) the brain tumor segmentation (BRATS-2017) challenge for high-grade versus low-grade (LG) and (2) the cancer imaging archive (TCIA) repository for glioblastoma (GBM) versus LG glioma grading. The grading performance using MRI is compared with that of digital pathology (DP) images in the cancer genome atlas (TCGA) data repository. The results show that the mean area under the receiver operating characteristic curve (AUC) is 0.88 for the BRATS dataset. The classification of tumor grades using MRI and DP images in TCIA/TCGA yields mean AUC of 0.90 and 0.93, respectively. This work further proposes and compares tumor grading performance using molecular alterations (IDH1/2 mutations) along with MRI and DP data, following the most recent World Health Organization grading criteria, respectively. The overall grading performance demonstrates the efficacy of the proposed noninvasive glioma grading approach using structural MRI.