TTF-1 immunohistochemistry in primary CNS tumors: A systematic review.

Thyroid transcription factor-1 (TTF-1) is a nuclear protein primarily recognized for its role in the development and differentiation of thyroid, lung, and certain diencephalic tissues. Although well-established as an immunohistochemical marker in thyroid and lung cancers, recent studies have explored its expression and diagnostic value in primary central nervous system (CNS) tumors. This systematic review aims to consolidate current knowledge on TTF-1 immunohistochemistry in primary CNS tumors, assessing its prevalence, diagnostic utility, and clinical implications. The review encompasses various CNS tumor types, including subependymal giant cell astrocytoma, chordoid glioma, pituicytoma, ependymomas, astrocytomas, glioblastomas, medulloblastomas, and choroid plexus tumors, highlighting the potential role of TTF-1 in differentiating these neoplasms from other CNS and metastatic tumors. By synthesizing findings from multiple studies, this review underscores the diagnostic value of TTF-1 in the neuropathological evaluation of CNS tumors and suggests directions for future research to refine its clinical application.

Primary intracranial dedifferentiated liposarcoma: An extremely rare site with unusual histopathological findings.

Primary intracranial sarcomas constitute a rare group of tumors, with the most common types described in the literature being chondrosarcoma and fibrosarcoma. Dedifferentiated liposarcoma (DDLS) is a high-grade sarcoma that sometimes metastasizes to the brain. However, a primary intracranial DDLS is exceedingly rare. A 45-year-old patient from the Middle East came to India for treatment. His magnetic resonance imaging (MRI) scans revealed a space-occupying lesion at the level of the lateral ventricle T2/fluid attenuated inversion recovery hyperintensity with peripheral edema. A T1 perfusion map showed high relative cerebral blood volume values in the peripheral part, suggesting a high-grade neoplasm. Gross total resection was performed, and histopathology showed a high-grade tumor composed of sheets of pleomorphic lipoblasts and epithelioid tumor cells arranged in nests and cords. Immunohistochemistry showed diffuse immunopositivity for MDM2, CDK4, and p16, while GFAP and OLIG2 were negative. Fluorescence in situ hybridization showed MDM2 amplification. Final diagnosis of DDLS was rendered. The patient had no systemic lesions elsewhere on positron emission tomography computed tomography scan.

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.

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.

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.

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.

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.

Advanced and amplified BOLD fluctuations in high-grade gliomas.

BACKGROUND: Glioma grade along with patient's age and general health are used for treatment planning and prognosis. PURPOSE: To characterize and quantify the spontaneous blood oxygen level-dependent (BOLD) fluctuations in gliomas using measures based on T2*-weighted signal time-series and to distinguish between high- and low-grade gliomas. STUDY TYPE: Retrospective. SUBJECTS: Twenty-one patients with high-grade and 13 patients with low-grade gliomas confirmed on histology were investigated. FIELD STRENGTH/SEQUENCE: Dynamic T2*-weighted (multislice single-shot echo-planar-imaging) magnetic resonance imaging (MRI) was performed on a 3T system with an 8-element receive-only head coil to measure the BOLD fluctuations. In addition, a dynamic T1 -weighted (3D fast field echo) dynamic contrast-enhanced (DCE) perfusion scan was performed. ASSESSMENT: Three BOLD measures were determined: the temporal shift (TS), amplitude of low frequency fluctuations (ALFF), and regional homogeneity (ReHo). DCE perfusion-based cerebral blood volume (CBV) and time-to-peak (TTP) maps were concurrently evaluated for comparison. STATISTICAL TESTS: An analysis-of-variance test was first used. When the test appeared significant, post-hoc analysis was performed using analysis-of-covariance with age as covariate. Logistic regression and receiver-operator characteristic curve analysis were also performed. RESULTS: TS was significantly advanced in high-grade gliomas compared to the contralateral cortex (P = 0.01) and low-grade gliomas (P = 0.009). In high-grade gliomas, ALFF and CBV were significantly higher than the contralateral cortex (P = 0.041 and P = 0.008, respectively) and low-grade gliomas (P = 0.036 and P = 0.01, respectively). ReHo and TTP did not show significant differences between high- and low-grade gliomas (P = 0.46 and P = 0.42, respectively). The area-under-curve was above 0.7 only for the TS, ALFF, and CBV measures. DATA CONCLUSION: Advanced and amplified hemodynamic fluctuations manifest in high-grade gliomas, but not in low-grade gliomas, and can be assessed using BOLD measures. Preliminary results showed that quantification of spontaneous fluctuations has potential for hemodynamic characterization of gliomas and distinguishing between high- and low-grade gliomas. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:1616-1625.

Differentiation of grade II/III and grade IV glioma by combining "T1 contrast-enhanced brain perfusion imaging" and susceptibility-weighted quantitative imaging.

PURPOSE: MRI is a useful method for discriminating low- and high-grade glioma using perfusion MRI and susceptibility-weighted imaging (SWI). The purpose of this study is to evaluate the usefulness of T1-perfusion MRI and SWI in discriminating among grade II, III, and IV gliomas. METHODS: T1-perfusion MRI was used to measure relative cerebral blood volume (rCBV) in 129 patients with glioma (70 grade IV, 33 grade III, and 26 grade II tumors). SWI was also used to measure the intratumoral susceptibility signal intensity (ITSS) scores for each tumor in these patients. rCBV and ITSS values were compared to seek differences between grade II vs. grade III, grade III vs. grade IV, and grade III+II vs. grade IV tumors. RESULTS: Significant differences in rCBV values of the three grades of the tumors were noted and pairwise comparisons showed significantly higher rCBV values in grade IV tumors as compared to grade III tumors, and similarly increased rCBV was seen in the grade III tumors as compared to grade II tumors (p < 0.001). Grade IV gliomas showed significantly higher ITSS scores on SWI as compared to grade III tumors (p < 0.001) whereas insignificant difference was seen on comparing ITSS scores of grade III with grade II tumors. Combining the rCBV and ITSS resulted in significant improvement in the discrimination of grade III from grade IV tumors. CONCLUSION: The combination of rCBV values derived from T1-perfusion MRI and SWI derived ITSS scores improves the diagnostic accuracy for discrimination of grade III from grade IV gliomas.

Role of Dynamic Contrast-Enhanced Perfusion Magnetic Resonance Imaging in Grading of Pediatric Brain Tumors on 3T.

BACKGROUND/AIMS: Perfusion magnetic resonance imaging (MRI) is useful for preoperative assessment of brain tumors. Dynamic susceptibility contrast perfusion MRI is commonly used for evaluation of brain tumors. Dynamic contrast-enhanced (DCE) MRI is an alternative method that has mainly been used in adult brain tumors. In this preliminary study, we report our initial experience with the DCE perfusion MRI in pediatric brain tumors. METHODS: Sixty-four newly diagnosed pediatric brain tumor patients underwent DCE perfusion MRI on a 3-T scanner. Hemodynamic and kinetic parametric maps were generated and the regions with the highest values were measured on each map. Statistical differences were sought to differentiate between low-grade tumors, high-grade tumors, and medulloblastomas. The perfusion metrics of common posterior fossa tumors were also compared. RESULTS: Relative corrected cerebral blood volume (rCBV) and fractional plasma volume measures differed significantly between high- and low-grade tumors (p < 0.05). High-grade tumors could be differentiated from low-grade tumors, with an rCBV cutoff value of 2.41 and 88.6% sensitivity and 65% specificity. There was no significant difference in Ktrans, Kep, Ve, or λtr between these 2 groups of tumors. rCBV, relative quantification of the cerebral blood flow, and permeability indices were found to be significantly different in various posterior fossa tumors, i.e., pilocytic astrocytoma, ependymoma, and medulloblastoma (p < 0.05). CONCLUSION: DCE-derived perfusion metrics are useful in differentiating high-grade tumors from low-grade ones and discriminating among various posterior fossa tumors in the pediatric age group.

Role of preoperative imaging in the diagnosis of primary diffuse craniospinal leptomeningeal gliomatosis.

We present an unusual case of primary diffuse craniospinal leptomeningeal gliomatosis (PGDL), who was initially diagnosed on the basis of imaging, laboratory findings, and cranial meningeal biopsy as tuberculous meningitis and showed clinical deterioration while on anti-tuberculous treatment for 2 months. The patient was subsequently correctly diagnosed on diffusion weighted and post-contrast T1-weighted imaging of the craniospinal axis along with whole body imaging. The radiological findings were confirmed on histopathology and immunohistochemistry performed from the previous block as well as biopsy from the nodular mass in the lumbosacral meninges. We conclude that peroperative imaging may help in pinpointing the correct diagnosis and assist in guiding the surgeon to the site of biopsy.

Role of whole-body magnetic resonance imaging in the evaluation of extrapulmonary tuberculosis in immunocompetent patients.

OBJECTIVE: The aim of this study was to evaluate the role of whole-body magnetic resonance imaging (WB-MRI) in imaging of extrapulmonary tuberculosis. METHODS: Eighteen patients with single-site extrapulmonary tuberculosis were evaluated with contrast-enhanced dedicated MRI of the clinically symptomatic site followed by WB-MRI using contrast-enhanced 3-dimensional (3D) modified DIXON technique (m-DIXON) and diffusion-weighted WB imaging with background body signal suppression (DWIBS) sequences. Studies were read by 2 experienced radiologists, and additional lesions seen on WB-MRI were separately charted. RESULTS: Of 18 patients, 14 were found to have asymptomatic involvement of other organs on WB-MRI. In 5 patients, the information was helpful in choosing an easily accessible site for biopsy/aspiration. Postcontrast 3D m-DIXON was better in picking up brain and lymph nodal lesions, whereas DWIBS was better in detecting vertebral lesions. CONCLUSIONS: Whole-body MRI may be used for assessing the asymptomatic involvement of other body organs in tuberculosis. The combination of postcontrast 3D m-DIXON and DWIBS is complementary and may provide a road map for biopsy of accessible lesions.

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.

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.

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.

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.

Primary dural follicular lymphoma masquerading as meningioma: a case report.

Patient presented with a dural-based mass lesion and was diagnosed as having meningioma on imaging. Post-resection histological examination revealed a low grade follicular lymphoma. The patient received cranial radiotherapy and is recurrence-free at 6-month follow-up. Primary dural follicular lymphoma is an exceedingly rare entity with only as few as six reported cases. Herein, the clinico-radio-pathological appearances and treatment protocol of this entity are discussed.

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.