Pediatric high grade gliomas: A comprehensive histopathological, immunohistochemical and molecular integrated approach in routine practice.

Pediatric high grade gliomas have undergone remarkable changes in recent time with discovery of new molecular pathways. They have been added separately in current WHO 2021 blue book. All the entities show characteristic morphology and immunohistochemistry. Methylation data correctly identifies these entities into particular group of clusters. The pediatric group high grade glioma comprises- Diffuse midline glioma, H3K27-altered; Diffuse hemispheric glioma, H3G34-mutant; Diffuse pediatric-type high-grade glioma, H3-wild type & IDH-wild type; Infant hemispheric glioma and Epithelioid glioblastoma/Grade 3 pleomorphic xanthoastrocytoma and very rare IDH-mutant astrocytoma. However it is not always feasible to perform these molecular tests where cost-effective diagnosis is a major concern. Here we discuss the major entities with their characteristic histopathology, immunohistochemistry and molecular findings that may help to reach to suggest the diagnosis and help the clinician for appropriate treatment strategies. We have also made a simple algorithmic flow chart integrated with histopathology, immunohistochemistry and molecular characteristics for better understanding.

Tension Pneumocranium Following Transsphenoidal Surgeries-A Case Report and Systematic Review of Literature with Analysis of Predisposing Factors and Treatment Regimens: Is Early Skull Base Repair Better than Conservative Treatment?

BACKGROUND: Conclusive evidence describing the outcomes following different treatment strategies for tension pneumocranium (TP) is lacking. Impact of predisposing conditions like multiple transnasal transsphenoidal (TNTS) procedures, intraoperative cerebrospinal fluid leak, obstructive sleep apnea, continuous positive airway pressure, violent coughing, nose blowing, positive pressure ventilation on TP outcomes is also unknown. METHODS: PubMed, Embase, Cochrane, and Google Scholar were searched for articles using Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Multivariate logistic regression analysis was done using STATA/ BE ver 17.0. RESULTS: Thirty-five studies with 49 cases of endoscopic TNTS surgeries were included. Tension pneumocephalus was seen in 77.5% (n = 38), tension pneumosella in 7 (14.28%), and tension pneumoventricle in 4 (8.16%). Nonfunctional pituitary adenomas (40.81%) were most common lesions associated with TP. The need of mechanical ventilation was significantly higher in patients who received conservative management (odds ratio, 1.34; confidence interval, 0.65-2.74) (P < 0.01). However, incidence of meningitis or mortality were not influenced by factors like age, gender, pathological diagnosis, initial conservative management or early skull base repair, use of adjuvant radiation, intraoperative cerebrospinal fluid leak, multiple TNTS explorations, or presence of precipitating factors. CONCLUSIONS: Nonfunctional pituitary adenomas were the most common lesions associated with TP. Multiple TNTS procedures did not increase incidence of meningitis or mortality. Conservative management increased the need for mechanical ventilation but did not worsen the mortality outcomes.

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis.

The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0-T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1-dynamic contrast-enhanced MR imaging. Statistically significant (< 0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried out using one-way analysis of variance. Further, a random forest (RF)-based machine learning model was designed to classify the PVE of GB and BM. Texture-based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future.

Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI.

BACKGROUND AND PURPOSE: Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T1 perfusion MRI parameters combined under a machine learning framework. MATERIALS AND METHODS: This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T1 perfusion and DWI data along with conventional MRI images. Analysis of T1 perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T1 perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T1 perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter. RESULTS: T1 perfusion parameter values were significantly different between the two groups. The combination of T1 perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error. CONCLUSION: The approach of using quantitative T1 perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC.

Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma.

BACKGROUND: Glioblastoma (GB) is among the most devastative brain tumors, which usually comprises sub-regions like enhancing tumor (ET), non-enhancing tumor (NET), edema (ED), and necrosis (NEC) as described on MRI. Semi-automated algorithms to extract these tumor subpart volumes and boundaries have been demonstrated using dynamic contrast-enhanced (DCE) perfusion imaging. We aim to characterize these sub-regions derived from DCE perfusion MRI using routine 3D post-contrast-T1 (T1GD) and FLAIR images with the aid of Radiomics analysis. We also explored the possibility of separating edema from tumor sub-regions by extracting the most influential radiomics features. METHODS: A total of 89 patients with histopathological confirmed IDH wild type GB were considered, who underwent the MR imaging with DCE perfusion-MRI. Perfusion and kinetic indices were computed and further used to segment tumor sub-regions. Radiomics features were extracted from FLAIR and T1GD images with PyRadiomics tool. Statistical analysis of the features was carried out using two approaches as well as machine learning (ML) models were constructed separately, i) within different tumor sub-regions and ii) ED as one category and the remaining sub-regions combined as another category. ML based predictive feature maps was also constructed. RESULTS: Seven features found to be statistically significant to differentiate tumor sub-regions in FLAIR and T1GD images, with p-value < 0.05 and AUC values in the range of 0.72 to 0.93. However, the edema features stood out in the analysis. In the second approach, the ML model was able to categorize the ED from the rest of the tumor sub-regions in FLAIR and T1GD images with AUC of 0.95 and 0.89 respectively. CONCLUSION: Radiomics-based specific feature values and maps help to characterize different tumor sub-regions. However, the GLDM_DependenceNonUniformity feature appears to be most specific for separating edema from the remaining tumor sub-regions using conventional FLAIR images. This may be of value in the segmentation of edema from tumors using conventional MRI in the future.

Role of intra-tumoral vasculature imaging features on susceptibility weighted imaging in differentiating primary central nervous system lymphoma from glioblastoma: a multiparametric comparison with pathological validation.

PURPOSE: Primary objective of this study was to retrospectively evaluate the potential of a range of qualitative and quantitative multiparametric features assessed on T2, post-contrast T1, DWI, DCE-MRI, and susceptibility-weighted-imaging (SWI) in differentiating evenly sampled cohort of primary-central-nervous-system-lymphoma (PCNSL) vs glioblastoma (GB) with pathological validation. METHODS: The study included MRI-data of histopathologically confirmed ninety-five GB and PCNSL patients scanned at 3.0 T MRI. A total of six qualitative features (three from T2 and post-contrast T1, three from SWI: thin-linear-uninterrupted-intra-tumoral-vasculature, broken-intra-tumoral-microvasculature, hemorrhage) were analyzed by three independent radiologists. Ten quantitative features from DWI and DCE-MRI were computed using in-house-developed algorithms. For qualitative features, Cohen's Kappa-interrater-variability-analysis was performed. Z-test and independent t-tests were performed to find significant qualitative and quantitative features respectively. Logistic-regression (LR) classifiers were implemented for evaluating performance of individual and various combinations of features in differentiating PCNSL vs GB. Performance evaluation was done via ROC-analysis. Pathological validation was performed to verify disintegration of vessel walls in GB and rim of viable neoplastic lymphoid cells with angiocentric-pattern in PCNSL. RESULTS: Three qualitative SWI features and four quantitative DCE-MRI features (rCBVcorr, Kep, Ve, and necrosis-volume-percentage) were significantly different (p < 0.05) between PCNSL and GB. Best diagnostic performance was observed with LR classifier using SWI features (AUC-0.99). The inclusion of quantitative features with SWI feature did not improve the differentiation accuracy. CONCLUSIONS: The combination of three qualitative SWI features using LR provided the highest accuracy in differentiating PCNSL and GB. Thin-linear-uninterrupted-intra-tumoral-vasculature in PCNSL and broken-intra-tumoral-microvasculature with hemorrhage in GB are the major contributors to the differentiation.

Concurrent intracranial infarct and intraventricular hemorrhage with spontaneous nontraumatic extradural hemorrhage in follow-up: An enigma of COVID-19-associated intracranial vasculopathy.

Background: Several neurological manifestations have been described in the literature, in patients affected with COVID-19 infection. Some common forms include ischemic stroke, cardioembolic stroke, intraparenchymal hemorrhage, and multicompartmental hemorrhage. Concurrent brain infarct and intraventricular hemorrhage (IVH) have not been described in the literature previously. Case Description: A 35-year hypertensive and COVID-19-positive patient developed sudden-onset spontaneous IVH with concurrent infarct in the left internal capsule. In spite of undergoing an initial CSF drainage procedure, he had persistent worsening sensorium and increasing midline shift on CT imaging, so he underwent a left-sided decompressive craniectomy. One month after discharge, he developed spontaneous extradural hemorrhage at the operative site. In view of impending cerebral herniation, emergency hematoma evacuation was done, which restored his neurological status. Conclusion: This is the first reported detailed case of concurrent intracranial infarct and IVH in a patient affected with COVID-19 infection. We also report a rare phenomenon of nontraumatic noncoagulopathic extradural hemorrhage on the decompressive craniectomy site, in this patient 1 month after surgery.

18F-Labeled WBC PET/CT Scan in a Case of Recurrent Glioblastoma Multiforme, Presented as Pyrexia of Unknown Origin.

ABSTRACT: Neoplastic causes account for approximately 10% to 20% cases of PUO (pyrexia of unknown origin). The mechanisms by which malignancies induce fever are not fully understood. The release of pyrogenic cytokines either directly from tumor cells or from macrophages responding to tumor are likely to play a major role, which acts on the hypothalamus, causing a change in the thermostatic set point. We present a case of recurrent glioblastoma multiforme, who presented with PUO. 18F-FDG-labeled leukocyte PET/CT scan done for localization of infective focus demonstrated significant tracer accumulation at the periphery of the recurrent brain lesion. Subsequent excisional biopsy from the lesion was suggestive of noninfected recurrent glioblastoma multiforme.

Endoscopic evacuation of septated chronic subdural hemorrhage - Technical considerations, results, and outcome.

BACKGROUND: Chronic subdural hematoma (cSDH) is a common entity in the elderly. Homogeneous or well-liquefied CSDH has a standard line of treatment through burr hole and irrigation. However, the management of septated chronic subdural hematoma (sCSDH) with multiple membranes does not have a well-defined surgical approach. The neomembranes forming septations prevent evacuation of clots through burr holes, and the small remaining loculi with clots will enlarge overtime to cause recurrence. METHODS: Patients with sCSDH were operated through a minicraniotomy (2.5 cm × 2.5 cm) using rigid endoscopes for visualization of the subdural space. Using endoscope, the entire subdural space can be visualized. The neomembranes are removed with standard neurosurgical microinstruments. The entire cavity is irrigated under vision to remove all clots and ensures hemostasis. RESULTS: Eighty-three endoscope-assisted evacuations were done in 68 patients from January 2016 to April 2020. Fifty (73.5%) patients had unilateral and 18 (26.5%) had bilateral subdural. Only 1 patient (1.47%) had a clinically significant recollection of subdural bleeding 1 month after the procedure. Over a mean follow-up period of 25.3 months (range 1-53 months), rest of patients did not show any recollection. CONCLUSION: Endoscopic evacuation of sCSDH is a safe and effective method and can be used to improve clot evacuation, and remove neomembranes under direct vision to reduce the rates of recollection. This method also obviates the need for larger craniotomies to remove membranes.

Medulloblastoma outcomes in tertiary care set-up in India using contemporary treatment protocols - A retrospective study.

Background: Medulloblastoma is the commonest embryonal brain tumor in children. It has shown improved outcomes with combined modality treatment. We aimed to study patient characteristics and survival outcomes of patients with this disease across two tertiary care centers in India. Methods: We analyzed data of patients with histological diagnosis of medulloblastoma treated from January 2010 to January 2016. Patient characteristics and follow-up data were retrieved from hospital records. Descriptive statistics were used to describe clinical and pathological characteristics. Overall survival (OS) was calculated from date of diagnosis to death due to any cause. Relapse-free survival (RFS) was calculated from date of diagnosis to occurrence of relapse or death. Result: Out of 26 patients treated, 24 were children and 2 were adults. Median age was 10 years (range = 0.8-22 years). Twenty (76.9%) patients were male. Fifteen (57.7%) patients were stratified as high-risk (HR), rest 11 (42.3%) were categorized as average risk (AR). Histopathology showed classical variety in majority of patients except for 4 (15%) cases, 3 with desmoplastic and 1 with anaplastic subtype. Median follow-up was 49.7 months (range= 4.2-102.5 months). Overall, eight (30.8%) patients relapsed and six (23%) deaths occurred. Five (33.3%) patients in HR category and 3 (27.3%) patients in AR group showed relapse. Median RFS and OS were not yet reached. Five-year RFS was 69.2% whereas five-year OS was 76.9%. Conclusion: This study highlighted patient characteristics and treatment outcomes in Indian patients. With adherence to standard treatment, high remission rates and improvement in mortality rates were achieved.

Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging.

Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2-FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics-based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1-contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first-order and gray-level co-occurrence matrix features are crucial in detecting local recurrence, while multimodal gray-level difference matrix and first-order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics-based phenotypic changes that may potentially serve as MR-based biomarkers for customized therapeutic intervention.

A Rare Case of Isolated Intraventricular Primary Central Nervous System Lymphoma in an 85-Year-Old Man.

Primary CNS lymphoma (PCNSL) is rare malignant B cell lymphoid tumor of brain which predominantly occurs in supratentorial region in periventricular location. Majority of PCNSL are of DLBCL type and idiopathic in etiology. Here we are reporting a case of primary CNS lymphoma, DLBCL involving extremely uncommon intraventricular location. Central neurocytoma, subependymal giant cell astrocytoma, choroid plexus tumors and meningiomas are the common diagnosis at this site. Aim of reporting this case is to bring awareness of unusual intraventricular location of primary CNS lymphoma which should be kept in mind before considering gross total excision of lesion.

A comprehensive evaluation and impact of normalization of generalized tracer kinetic model parameters to characterize blood-brain-barrier permeability in normal-appearing and tumor tissue regions of patients with glioma.

RATIONALE AND OBJECTIVES: To comprehensively evaluate robustness and variations of DCE-MRI derived generalized-tracer-kinetic-model (GTKM) parameters in healthy and tumor tissues and impact of normalization in mitigating these variations on application to glioma. MATERIALS (PATIENTS) AND METHODS: A retrospective study included pre-operative 31 high-grade-glioma(HGG), 22 low-grade-glioma(LGG) and 33 follow-up data from 10 patients a prospective study with 4 HGG subjects. Voxel-wise GTKM was fitted to DCE-MRI data to estimate Ktrans, ve, vb. Simulations were used to evaluate noise sensitivity. Variation of parameters with-respect-to arterial-input-function (AIF) variation and data length were studied. Normalization of parameters with-respect-to mean values in gray-matter (GM) and white-matter (WM) regions (GM-Type-2, WM-Type-2) and mean curves (GM-Type-1, WM-Type-1) were also evaluated. Co-efficient-of-variation(CoV), relative-percentage-error (RPE), Box-Whisker plots, bar graphs and t-test were used for comparison. RESULTS: GTKM was fitted well in all tissue regions. Ktrans and ve in contrast-enhancing (CE) has shown improved noise sensitivity in longer data. vb was reliable in all tissues. Mean AIF and C(t) peaks showed ~38% and ~35% variations. During simulation, normalizations have mitigated variations due to changes in AIF amplitude in Ktrans and vb.. ve was less sensitive to normalizations. CoV of Ktrans and vb has reduced ~70% after GM-Type-1 normalization and ~80% after GM-Type-2 normalization, respectively. GM-Type-1 (p = 0.003) and GM-Type-2 (p = 0.006) normalizations have significantly improved differentiation of HGG and LGG using Ktrans. CONCLUSION: Ktrans and vb can be reliably estimated in normal-appearing brain tissues and can be used for normalization of corresponding parameters in tumor tissues for mitigating inter-subject variability due to errors in AIF. Normalized Ktrans and vb provided improved differentiation of HGG and LGG.

Negative Pressure Assisted Microenvironment Surgical Hood: A Novel Cost-Effective Device to Minimize Aerosol Contamination During Neurosurgical Procedures in Times of COVID-19.

OBJECTIVE: Present guidelines on reducing aerosol generation during neurosurgical procedures are futile. The aim of this article was to describe a novel device to contain aerosol within a small localized environment around the operative field-the negative pressure assisted microenvironment surgical hood (NEPA-MESH). METHODS: This device can be assembled using easily available materials-steel wires, image intensifier cover, surgical drape, and three-dimensional-printed self-locking copolyester double hoops. Large-bore pipes in continuity with a high-volume suction apparatus create a constant negative pressure microenvironment around the operative field. The CEM DT-9880 particle counter was used to estimate particle concentration inside the NEPA-MESH during various stages of a neurosurgical procedure as well as outside. The NEPA-MESH was tested in different craniotomies and endoscopic procedures. RESULTS: Mean particle concentration inside the NEPA-MESH and outside during drilling in various procedures was calculated and compared using unpaired t test. Significant reduction in particle concentrations was recorded for particles sized 0.3 µm (t = 17.55, P < 0.0001), 0.5 µm (t = 11.39, P < 0.0001), 1 µm (t = 6.36, P = 0.0002), 2.5 µm (t = 2.04, P = 0.074), 5.0 µm (t = 7.026, P = 0.0008), and 10 µm (t = 4.39, P = 0.0023). CONCLUSIONS: As definitive evidence demonstrating the presence of coronavirus disease 2019 (COVID-19) in aerosol particles is awaited, we describe a cost-effective strategy to reduce aerosol contamination. Significant reduction in particle concentrations was seen outside the NEPA-MESH compared with inside it during various stages of neurosurgical procedures.

Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI.

PURPOSE: This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation. METHODS: Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features. RESULTS: Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma. CONCLUSION: We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma.

Perfusion magnetic resonance imaging in contouring of glioblastoma patients: Preliminary experience from a single institution.

PURPOSE: T1-contrast and T2-flair images of magnetic resonance imaging (MRI) are commonly fused with computed tomography (CT) and used for delineation of postoperative residual tumor and bed after surgery in patients with glioblastoma multiforme (GBM). Our prospective study was aimed to see the feasibility of incorporating perfusion MRI in delineation of brain tumor for radiotherapy planning and its implication on treatment volumes. METHODS: Twenty-four patients with histopathologically proven GBM were included in the study. All patients underwent radiotherapy planning with a contrast CT scan. In addition to radiotherapy (RT) planning protocol, T1-perfusion MRI was also done in all patients in the same sitting. Perfusion imaging was processed on the in-house-developed JAVA-based software. The images of CT and MRI were sent to the iPlan planning system (Brainlab AG, GmbH) using a Digital Imaging and Communications in Medicine - Radiation Therapy (DICOM-RT) protocol. A structure of gross tumor volume (GTV)-perfusion (GTV-P) was delineated based only on the MRI perfusion images. Subsequently, GTV-P and GTV were fused together to make GTV-summated (GTV-S). Using existing guidelines, GTV-S was expanded to form clinical target volume-summated (CTV-S) and planning target volume-summated (PTV-S). The increment in each of the summated volumes as compared to baseline volume was noted. The common overlap volume (GTVO) between GTV and GTV-P was calculated using intersection theory (GTV n GTV-P = GTVO [Overlap]). RESULTS: Mean ± standard deviation (cc) for GTV, GTV-P, and GTVO was 46.3 ± 33.4 cc (range: 5.2 cc-108.0 cc), 26.0 ± 26.2 (range: 6.6 cc-10.3.0 cc), and 17.5 ± 22.3 cc (range: 10.0 cc-92 cc), respectively. Median volume (cc) for GTV, GTV-P, and GTVO was 40.8 cc, 17.2 cc, and 8.0 cc, respectively. Mean absolute and relative increments from GTV to that of GTV-S were 8.5 ± 8.2 cc and 27.2 ± 30.9%, respectively. Average CTV volume (cc) was 230.4 ± 115.3 (range: 80.8 cc-442.0 cc). Mean and median CTV-S volumes were 262.0 ± 126.3 cc (range: 80.8 cc-483.0 cc) and 221.0 cc, respectively. The increment in the mean CTV volume (with respect to CTV created from GTV-S) was 15.2 ± 15.9%. Mean and median PTV volumes created on the summated CTV were 287.1 ± 134.0 cc (range: 118.9 cc-576.0 cc) and 258.0 cc, respectively. Absolute and relative increments in PTV volume, while incorporating the perfusion volume, were 31.3 ± 28.9 cc and 12.5 ± 13.3%, respectively. Out of the total of 24 patients, perfusion scanning did not do any increment in GTV in five patients. CONCLUSIONS: Our study is the first to present the feasibility and the outcome of contouring on perfusion imaging and its overlay on regular MRI images. The implications of this on long-term outcome and control rates of glioblastoma patients need to be seen in future studies.

External ventricular drain related complications-whether continuous csf drainage via ommaya reservoir is the answer?

BACKGROUND AND AIMS: External ventricular drainage (EVD) is one of the commonest procedures in neurosurgical practice to manage acute hydrocephalus. We evaluated the infectious and non-infectious complications associated with a modified technique for EVD using an Ommaya reservoir. METHODS: Ommaya reservoir was placed in all patients who required EVD placement for CSF drainage. CSF drainage was achieved using a needle placed in a non-coring fashion percutaneously into the Ommaya reservoir to achieve CSF drainage externally. CSF was monitored for signs of infection regularly using CSF biochemistry and cultures. CSF infection was defined by a positive culture or a secondary infection in patients with already infected CSF. RESULTS: 59 patients required continuous CSF drainage during the study period from January 2014 to June 2017. 46 (77.96%) patients had non-infected CSF at time of starting drainage and 13 (22.03%) patients required external CSF drainage for primarily infected CSF. The study period had a total of 793 CSF drainage days (Range 3-64 days) with an average of 13.4 days per patient. The cumulative rate of new infection was 5.08%. No ventricular catheter blockage or dislodgement was seen in any of the patients. CONCLUSIONS: External ventricular drainage through an Ommaya chamber is a safe and effective method and can be used to reduce the catheter related complications like infection, catheter blockage and dislodgement.

Quantitative vs. semiquantitative assessment of intratumoral susceptibility signals in patients with different grades of glioma.

BACKGROUND: Susceptibility weighted imaging (SWI) provides vascular information and plays an important role in improving the diagnostic accuracy of preoperative glioma grading. Intratumoral susceptibility signal intensities (ITSS) obtained from SWI has been used in glioma grading. However, the current method for estimation of ITSS is semiquantitative, manual count-dependent, and includes hemorrhage as well as vasculature. PURPOSE: To develop a quantitative approach that calculates the vasculature volume within tumors by filtering out the hemorrhage from ITSS using R2 * values and connected component analysis-based segmentation algorithm; to evaluate the accuracy of the proposed ITSS vasculature volume (IVV) for differentiating various grades of glioma; and compare it with reported semiquantitative ITSS approach. STUDY TYPE: Retrospective. SUBJECTS: Histopathologically confirmed 41 grade IV, 19 grade III, and 15 grade II glioma patients.Field Strength/Sequence: SWI (four echoes: 5.6, 11.8, 18, 24.2 msec) along with conventional MRI sequences (T2 -weighted, T1 -weighted, 3D-fluid-attenuated inversion recovery [FLAIR], and diffusion-weighted imaging [DWI]) at 3.0T. ASSESSMENT: R2 * relaxation maps were calculated from multiecho SWI. The R2 * cutoff value for hemorrhage ITSS was determined. A segmentation algorithm was designed, based on this R2 * hemorrhage combined with connected component shape analysis, to quantify the IVV from all slices containing tumor by filtering out hemorrhages. Semiquantitative ITSS scoring as well as total ITSS volume (TIV) including hemorrhages were also calculated. STATISTICAL TESTS: One-way analysis of variance (ANOVA) and Tukey-Kramer post-hoc tests were performed to see the difference among the three grades of the tumor (II, III, and IV) in terms of semiquantitative ITSS scoring, TIV, and IVV. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the three methods individually in discriminating between grades of glioma. RESULTS: One-way ANOVA showed that only the proposed IVV significantly differentiated different grades of gliomas having visible ITSS. ROC analysis showed that IVV provided the highest AUC for the discrimination of grade II vs. III (0.93), grade III vs. IV (0.98), and grade II vs. IV glioma (0.94). IVV also provided the highest sensitivity and specificity for differentiating grade II vs. III (87.44, 98.41), grade III vs. IV (97.15, 94.12), and grade II vs. IV (98.72, 92.31). DATA CONCLUSION: The proposed quantitative method segregates hemorrhage from tumor vasculature. It scores above the existing semiquantitative method in terms of ITSS estimation and grading accuracy. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:225-233.