Repeated operations for infiltrative low-grade gliomas without intervening therapy

Journal ArticleProgression of infiltrative low-grade gliomas (LGGs) has been reported previously. The limitations of such studies include diverse histological grading systems, intervening therapy, and the lack of histological confirmation of malignant tumor progression. The aim of this study was to determine tumor progression in adult patients with an initial diagnosis of infiltrative LGG who subsequently underwent a repeated operation, but no other intervening therapy. The authors examined factors that may be associated with tumor progression

RARE-30. PEDIATRIC GLIOBLASTOMA IN THE POST-TEMOZOLOMIDE ERA: OUTCOMES AND CHARACTERISTICS

Abstract INTRODUCTION Glioblastoma (GBM) is the most common brain tumor, however, is a rare occurrence in children and is poorly characterized. We evaluated the characteristics and outcomes of pediatric GBM (pGBM). METHODS Retrospective analysis of pediatric (age< 18) patients diagnosed with GBM undergoing first glioblastoma resection at our brain tumor center (2005- 2016). RESULTS From 1457 GBM patients, we identified twenty-four (1.65%) pGBMs (Median Age=9 years, Females=45.8%). Median overall survival (OS) was 32.1 months, while the median progression-free survival was 11.5 months. The commonest symptoms at presentation were headaches (54.2%,n=13) and motor symptoms (50%,n=12). Mean tumor diameter was 4.5 cm and 25% of the cohort underwent gross total resection (GTR) of their tumor. Univariate analysis revealed median OS significantly associated with tumor extent of resection (GTR=56.4 months; STR/Biopsy=13.7 months, p=0.001), age at surgery (>10 years=43.9 months, < 10 years= 17.2 months, p=0.01), tumor size (> 4cm= 9.1 months, < 4cm=56.9 months, p=0.01),motor symptoms at presentation (present=14.9 months, absent=41.04 months, p=0.02) and infratentorial tumors (infratentorial=17.4 vs supratentorial=53.4 months, p=0.02). Multivariate analysis revealed GTR (HR 0.2[95% CI 0.07–0.72]; p=0.03), Age >10 years (HR 0.6[95% CI 0.02–0.64]; p=0.002), tumor >4 cm (HR 2.89[95% CI 1.88–4.11]; p=0.001) and EGFR amplification (HR 3.48[95% CI 0.82–17.4]; p=0.005) to be independent predictors of OS. Comparing patients under and over 10 years, we found that older patients had smaller tumors at presentation (4.9 vs 3.6 cms, p=0.03), greater rates of preoperative temozolomide (n=1,7.7% vs n=6, 54.5%) and bevacizumab (n=1,7.7% vs n=4, 36.4%) treatment, and lower rates of EGFR amplification (66.7% vs 11.1%) that could explain survival disparities between groups. CONCLUSION Motor symptoms, larger tumors at presentation and tumor EGFR amplification may be indictive of poorer outcomes in pGBM. However, maximal tumor resection, aggressive chemoradiation and tumor presentation at age >10 years may confer better prognosis in these patients

The surgical management of diffuse gliomas: Current state of neurosurgical management and future directions.

After recent updates to the World Health Organization pathological criteria for diagnosing and grading diffuse gliomas, all major North American and European neuro-oncology societies recommend a maximal safe resection as the initial management of a diffuse glioma. For neurosurgeons to achieve this goal, the surgical plan for both low- and high-grade gliomas should be to perform a supramaximal resection when feasible based on preoperative imaging and the patients performance status, utilizing every intraoperative adjunct to minimize postoperative neurological deficits. While the surgical approach and technique can vary, every effort must be taken to identify and preserve functional cortical and subcortical regions. In this summary statement on the current state of the field, we describe the tools and technologies that facilitate the safe removal of diffuse gliomas and highlight intraoperative and postoperative management strategies to minimize complications for these patients. Moreover, we discuss how surgical resections can go beyond cytoreduction by facilitating biological discoveries and improving the local delivery of adjuvant chemo- and radiotherapies

An independently validated nomogram for isocitrate dehydrogenase-wild-type glioblastoma patient survival.

BackgroundIn 2016, the World Health Organization reclassified the definition of glioblastoma (GBM), dividing these tumors into isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant GBM, where the vast majority of GBMs are IDH-wild-type. Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a nomogram for IDH-wild-type patients with newly diagnosed GBM.MethodsData were obtained from newly diagnosed GBM patients from the Ohio Brain Tumor Study (OBTS) and the University of California San Francisco (UCSF) for diagnosis years 2007-2017 with the following variables: age at diagnosis, sex, extent of resection, concurrent radiation/temozolomide (TMZ) status, Karnofsky Performance Status (KPS), O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and IDH mutation status. Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the OBTS data and independently validated using the UCSF data. Models were internally validated using 10-fold cross-validation and externally validated by plotting calibration curves.ResultsA final nomogram was validated for IDH-wild-type newly diagnosed GBM. Factors that increased the probability of survival included younger age at diagnosis, female sex, having gross total resection, having concurrent radiation/TMZ, having a high KPS, and having MGMT methylation.ConclusionsA nomogram that calculates individualized survival probabilities for IDH-wild-type patients with newly diagnosed GBM could be useful to physicians for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: https://gcioffi.shinyapps.io/Nomogram_For_IDH_Wildtype_GBM_H_Gittleman/

A novel federated deep learning scheme for glioma and its subtype classification

Background:\ua0Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals.Method:\ua0We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes.Results:\ua0Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (−1.17%, −0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme.Conclusion:\ua0The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential

A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas

In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (<20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS’17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts’ time annotating tumors and a small drop in segmentation performance

Prediction of glioma‑subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors

Background: For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) isdesirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help theclassification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated datawith ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consumingprocess with high demand on medical personnel. As an alternative automatic segmentation is often used. However, itdoes not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRIacquisition parameters across imaging centers, as segmentation is an ill‑defined problem. Analogous to visual objecttracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas inMR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding boxareas (e.g. ellipse shaped boxes) for classification without a significant drop in performance.Method: In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employ‑ing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments wereconducted on two datasets (US and TCGA) consisting of multi‑modality MRI scans where the US dataset containedpatients with diffuse low‑grade gliomas (dLGG) exclusively.Results: Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and79.50% for IDH mutation/wild‑type on TCGA dataset. Comparisons with that of using annotated GT tumor data fortraining showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype).Conclusion: Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for train‑ing a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With moredata that can be made available, this may be a reasonable trade‑off where decline in performance may be counter‑acted with more data

The automatic gain-matching in the PIBETA CsI calorimeter

Segmented electromagnetic calorimeters are used to determine both the total energy and direction (momentum components) of charged particles and photons. A trade off is involved in selecting the degree of segmentation of the calorimeter as the spatial and energy resolutions are affected differently. Increased number of individual detectors reduces accidental particle pile-up per detector but introduces complications related to ADC pedestals and pedestal variations, exacerbates the effects of electronic noise and ground loops, and requires summing and discrimination of multiple analog signals. Moreover, electromagnetic showers initiated by individual ionizing particles spread over several detectors. This complicates the precise gain-matching of the detector elements which requires an iterative procedure. The PIBETA calorimeter is a 240-module pure CsI non-magnetic detector optimized for detection of photons and electrons in the energy range 5-100 MeV. We present the computer-controlled, automatic, in situ gain-matching procedure that we developed and used routinely in several rare pion and muon decay experiments with the PIBETA detector.Comment: 28 pages, 13 postscript figures, LaTeX, submitted to Nucl. Instrum. Meth.

Concurrent MEK targeted therapy prevents MAPK pathway reactivation during BRAFV600E targeted inhibition in a novel syngeneic murine glioma model.

Inhibitors of BRAFV600E kinase are currently under investigations in preclinical and clinical studies involving BRAFV600E glioma. Studies demonstrated clinical response to such individualized therapy in the majority of patients whereas in some patients tumors continue to grow despite treatment. To study resistance mechanisms, which include feedback activation of mitogen-activated protein kinase (MAPK) signaling in melanoma, we developed a luciferase-modified cell line (2341luc) from a BrafV600E mutant and Cdkn2a- deficient murine high-grade glioma, and analyzed its molecular responses to BRAFV600E- and MAPK kinase (MEK)-targeted inhibition. Immunocompetent, syngeneic FVB/N mice with intracranial grafts of 2341luc were tested for effects of BRAFV600E and MEK inhibitor treatments, with bioluminescence imaging up to 14-days after start of treatment and survival analysis as primary indicators of inhibitor activity. Intracranial injected tumor cells consistently generated high-grade glioma-like tumors in syngeneic mice. Intraperitoneal daily delivery of BRAFV600E inhibitor dabrafenib only transiently suppressed MAPK signaling, and rather increased Akt signaling and failed to extend survival for mice with intracranial 2341luc tumor. MEK inhibitor trametinib delivered by oral gavage daily suppressed MAPK pathway more effectively and had a more durable anti-growth effect than dabrafenib as well as a significant survival benefit. Compared with either agent alone, combined BRAFV600E and MEK inhibitor treatment was more effective in reducing tumor growth and extending animal subject survival, as corresponding to sustained MAPK pathway inhibition. Results derived from the 2341luc engraftment model application have clinical implications for the management of BRAFV600E glioma