Postcontrast Magnetic Resonance Imaging Research Articles

Whole-tumor histogram analysis of synthetic magnetic resonance imaging predicts isocitrate dehydrogenase mutation status in gliomas.

An accurate assessment of isocitrate dehydrogenase (IDH) status in patients with glioma is crucial for treatment planning and is a key factor in predicting patient outcomes. In this study, we investigated the potential value of whole-tumor histogram metrics derived from synthetic magnetic resonance imaging (MRI) in distinguishing IDH mutation status between astrocytoma and glioblastoma. In this prospective study, 80 glioma patients were enrolled from September 2019 to June 2022. All patients underwent pre- and post-contrast synthetic MRI scan protocol. Immunohistochemistry (IHC) staining or gene sequencing were used to assess IDH mutation status in tumor tissue samples. Whole-tumor histogram metrics, including T1, T2, proton density (PD), etc., were extracted from the quantitative maps, while radiological features were assessed by synthetic contrast-weighted maps. Basic clinical features of the patients were also evaluated. Differences in clinical, radiological, and histogram metrics between IDH-mutant astrocytoma and IDH-wildtype glioblastoma were analyzed using univariate analyses. Variables with statistical significance in univariate analysis were included in multivariate logistic regression analysis to develop the combined model. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to assess the diagnostic performance of metrics and models. The histopathologic analysis revealed that of the 80 cases, 41 were classified as IDH-mutant astrocytoma and 39 as IDH-wildtype glioblastoma. Compared to IDH-wildtype glioblastoma, IDH-mutant astrocytoma showed significantly lower T1 [10th percentile (10th), mean, and median] and post-contrast PD (10th, 90th percentile, mean, median, and maximum) values as well as higher post-contrast T1 (cT1) (10th, mean, median, and minimum) values (all P<0.05). The combined model (T1-10th + cT1-10th + age) was developed by integrating the independent influencing factors of IDH-mutant astrocytoma using the multivariate logistic regression. The diagnostic performance of this model [AUC =0.872 (0.778-0.936), sensitivity =75.61%, and specificity =89.74%] was superior to the clinicoradiological model, which was constructed using age and enhancement degree (AUC =0.822 (0.870-0.898), P=0.035). The combined model constructed using histogram metrics derived from synthetic MRI could be a valuable preoperative tool to distinguish IDH mutation status between astrocytoma and glioblastoma, and subsequently, could assist in the decision-making process of pretreatment.

Transient ischemic attack in the vertebrobasilar vascular territory as a cause of isolated vertigo

Transient ischemic attack (TIA) in the vertebrobasilar vascular territory (VB) causes difficulties in diagnosis when it manifests only with vestibular symptoms. Issues relating to the differential diagnosis of TIA are discussed, awareness of which is necessary for the selection of informative methods of examination and the prescription of effective stroke prevention in patients with an episode of isolated dizziness. The likelihood of TIA as the cause of dizziness is increased by the patients' high cardiovascular risk, the presence of atrial fibrillation, severe instability during an attack, and head and/or neck pain. If a TIA in VB is suspected, it is advisable to perform a minimal instrumental examination, including computed tomography (CT) of the brain and CT angiography or diffusion-weighted magnetic resonance imaging (MRI) and MRI angiography. In case of doubt, additional information can be obtained by a perfusion CT or MRI as well as a post-contrast MRI. When interpreting the results of these methods of examination, their limitations in terms of application time and resolution should be taken into account.

Comparing the Efficacy of a Combination of Diffusion-Weighted Imaging and T2-STIR (Short Tau Inversion Recovery) Imaging With Contrast-Enhanced MRI in the Evaluation of Perianal Fistula.

Perianal fistula is clinically diagnosed and commonly characterized using magnetic resonance imaging (MRI). Diffusion-weighted imaging (DWI) and T2-weighted imaging are emerging techniques that can obviate the need for contrast injection in cases where contrast administration is not feasible or contraindicated. The main objective of our study was to compare the efficacy of the combination of DWI and T2 STIR (short tau inversion recovery) imaging with contrast-enhanced MRI for the diagnosis and characterization of perianal fistula. Sixty-nine patients with clinical perianal fistula with at least one external opening were evaluated with DWI, T2 STIR, and contrast MRI. A comparative cross-sectional study was conducted in the Department of Radiodiagnosis and Imaging, All India Institute of Medical Sciences, Bhopal, India. The chi-square test was done to find the association between categorical variables. The Kappa test was done to estimate the agreement between two different tests in measuring the outcome. The validity of tests was measured using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. The combination of DWI and T2 STIR is equivalent to contrast-enhanced MRI in the evaluation of primary and complicated perianal fistula. The combination of DWI and T2 STIR is superior to DWI alone in the classification and characterization of perianal fistula. However, DWI is superior to T2 STIR in differentiating perianal inflammation with abscess from perianal inflammation without abscess and can be used as an alternative to post-contrast fat-suppressed T1-WI in the detection of perianal abscesses and disease activity. DWI can be used as an adjunct to T2 STIR, and the combination of DWI and T2 STIR can replace the post-contrast fat-suppressed T1 MRI sequence in the classification and characterization of perianal fistula.

A semi-automatic segmentation method for meningioma developed using a variational approach model.

Meningioma is the commonest primary brain tumour. Volumetric post-contrast magnetic resonance imaging (MRI) is recognised as gold standard for delineation of meningioma volume but is hindered by manual processing times. We aimed to investigate the utility of a model-based variational approach in segmenting meningioma. A database of patients with a meningioma (2007-2015) was queried for patients with a contrast-enhanced volumetric MRI, who had consented to a research tissue biobank. Manual segmentation by a neuroradiologist was performed and results were compared to the mathematical model, using a battery of tests including the Sørensen-Dice coefficient (DICE) and JACCARD index. A publicly available meningioma dataset (708 segmented T1 contrast-enhanced slices) was also used to test the reliability of the model. 49 meningioma cases were included. The most common meningioma location was convexity (n = 15, 30.6%). The mathematical model segmented all but one incidental meningioma, which failed due to the lack of contrast uptake. The median meningioma volume by manual segmentation was 19.0cm3 (IQR 4.9-31.2). The median meningioma volume using the mathematical model was 16.9cm3 (IQR 4.6-28.34). The mean DICE score was 0.90 (SD = 0.04). The mean JACCARD index was 0.82 (SD = 0.07). For the publicly available dataset, the mean DICE and JACCARD scores were 0.90 (SD = 0.06) and 0.82 (SD = 0.10), respectively. Segmentation of meningioma volume using the proposed mathematical model was possible with accurate results. Application of this model on contrast-enhanced volumetric imaging may help reduce work burden on neuroradiologists with the increasing number in meningioma diagnoses.

Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network.

Stereotactic radiosurgery (SRS) treatment planning requires accurate delineation of brain metastases, a task that can be tedious and time-consuming. Although studies have explored the use of convolutional neural networks (CNNs) in magnetic resonance imaging (MRI) for automatic brain metastases delineation, none of these studies have performed clinical evaluation, raising concerns about clinical applicability. This study aimed to develop an artificial intelligence (AI) tool for the automatic delineation of single brain metastasis that could be integrated into clinical practice. Data from 426 patients with postcontrast T1-weighted MRIs who underwent SRS between March 2007 and August 2019 were retrospectively collected and divided into training, validation, and testing cohorts of 299, 42, and 85 patients, respectively. Two Gamma Knife (GK) surgeons contoured the brain metastases as the ground truth. A novel 2.5D CNN network was developed for single brain metastasis delineation. The mean Dice similarity coefficient (DSC) and average surface distance (ASD) were used to assess the performance of this method. The mean DSC and ASD values were 88.34%±5.00% and 0.35±0.21 mm, respectively, for the contours generated with the AI tool based on the testing set. The DSC measure of the AI tool's performance was dependent on metastatic shape, reinforcement shape, and the existence of peritumoral edema (all P values <0.05). The clinical experts' subjective assessments showed that 415 out of 572 slices (72.6%) in the testing cohort were acceptable for clinical usage without revision. The average time spent editing an AI-generated contour compared with time spent with manual contouring was 74 vs. 196 seconds, respectively (P<0.01). The contours delineated with the AI tool for single brain metastasis were in close agreement with the ground truth. The developed AI tool can effectively reduce contouring time and aid in GK treatment planning of single brain metastasis in clinical practice.

Microanatomical findings with relevance to trigeminal ganglion enhancement on post-contrast T1-weighted magnetic resonance images in dogs.

Trigeminal ganglion contrast enhancement (TGCE) is reported to be a normal and a common finding on magnetic resonance imaging studies of dogs, cats and humans. The intent of the present study was to describe the anatomical characteristics of the trigeminal ganglion, its surrounding structures, and histological features that are relevant to explain or hypothesize on the reason for TGCE on T1-weighted post-contrast MRI studies of the brain in dogs. Eight dog cadavers were dissected to study the anatomy of the trigeminal ganglion. The presence and anatomy of vessels was studied by dissection and by histological techniques. Two trigeminal ganglia were isolated and stained with hematoxylin-eosin (HE). Two other trigeminal ganglia included in the trigeminal canal and trigeminal cavity were decalcified with formic acid/formalin for 12 weeks and stained with HE to study the related vessels. Additionally, a corrosion cast was obtained from a separate canine specimen. Leptomeninges and a subarachnoid space were identified at the level of the trigeminal nerve roots and the trigeminal ganglion. No subarachnoid space was identified and leptomeninges were no longer present at the level of the three trigeminal nerve branches. Small arterial vessels ran to and supplied the trigeminal ganglion, passing through the dura mater. No venous plexus was visualized at the level of the trigeminal ganglion in the dissections. A complex arterial vascular network was identified within the leptomeningeal covering of the trigeminal ganglion and was best appreciated in the corrosion cast. Histological examination revealed small-to moderate-sized blood vessels located in the epineurium around the ganglion; from there a multitude of arterioles penetrated into the perineurium. Small endoneurial branches and capillaries penetrated the ganglion and the trigeminal nerve branches. Limitations to this study include the limited number of canine specimens included and the lack of electron microscopy to further support current hypotheses included in our discussion. In conclusion, this study provides further support to the theory that TGCE in dogs may be due an incomplete blood-nerve barrier or blood-ganglion barrier at the interface between the central nervous system and the peripheral nervous system.

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer.

The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.

Vertebral artery wall inflammation suspected as the cause of cryptogenic ischemic stroke developing during the recovery period of COVID-19.

Coronavirus disease-2019 (COVID-19) can cause acute ischemic stroke (AIS) due to large vessel occlusion (LVO). Some cases of COVID-19-related LVO are known to be resistant to mechanical thrombectomy and have different characteristics from non-COVID-19-related LVO. Inflammation of the occluded arterial wall is suspected as one of the causes of such differences, but the exact mechanism is not fully understood. A 52-year-old man suffered from AIS due to left vertebral artery (VA) occlusion during the recovery period after mild COVID-19. Successful recanalization of the left VA was achieved with antithrombotic therapy, but a late and reversible edematous lesion appeared in part of the brainstem adjacent to the left VA, with abnormal enhancement in both the left VA wall and medulla oblongata on postcontrast magnetic resonance imaging. We suggest that the left VA wall inflammation, induced by COVID-19, caused the ischemic stroke and extended to the brainstem, and an incidental thrombosed unruptured aneurysm of the left VA accelerated these changes. This case provides the first evidence of LVO after COVID-19 in which the pathological conditions in the brainstem adjacent to the affected artery could be observed with neuroimaging and inflammation of the arterial wall was indirectly confirmed. Physicians should be aware that unconventional ischemic stroke may develop in some patients during the recovery period after COVID-19.

Symptoms: Sudden Sensorineural Hearing Loss and Aural Fullness

Symptoms: Sudden Sensorineural Hearing Loss and Aural Fullness

Does sevoflurane sedation in pediatric patients lead to "pseudo" leptomeningeal enhancement in the brain on 3 Tesla magnetic resonance imaging?

Prominent leptomeningeal contrast enhancement (LMCE) in the brain is observed in some pediatric patients during sedation for imaging. However, based on clinical history and cerebrospinal fluid analysis, the patients are not acutely ill and do not exhibit meningeal signs. Our study determined whether sevoflurane inhalation in pediatric patients led to this pattern of 'pseudo' LMCE (pLMCE) on 3 Tesla magnetic resonance imaging (MRI). To highlight the significance of pLMCE in pediatric patients undergoing enhanced brain MRI under sedation to avoid misinterpretation in reports. A retrospective cross-sectional evaluation of pediatric patients between 0-8 years of age was conducted. The patients underwent enhanced brain MRI under inhaled sevoflurane. The LMCE grade was determined by two radiologists, and interobserver variability of the grade was calculated using Cohen's kappa. The LMCE grade was correlated with duration of sedation, age and weight using the Spearman rho rank correlation. A total of 63 patients were included. Fourteen (22.2%) cases showed mild LMCE, 48 (76.1%) cases showed moderate LMCE, and 1 case (1.6%) showed severe LMCE. We found substantial agreement between the two radiologists in detection of pLMCE on post-contrast T1 imaging (kappa value = 0.61; P < 0.001). Additionally, we found statistically significant inverse and moderate correlations between patient weight and age. There was no correlation between duration of sedation and pLMCE. pLMCE is relatively common on post-contrast spin echo T1-weighted MRI of pediatric patients sedated by sevoflurane due to their fragile and immature vasculature. It should not be misinterpreted for meningeal pathology. Knowing pertinent clinical history of the child is an essential prerequisite to avoid radiological overcalling and the subsequent burden of additional investigations.

Delayed post-contrast MRI in the assessment of Meniere’s disease—initial South Australian experience

Background: The underlying pathology of Meniere’s disease (MD) has long been attributed to endolymphatic hydrops (EH), until recently only detectable on post-mortem studies. In this retrospective cross-sectional study, we utilised heavily T2 weighted post-contrast 3D FLAIR (fluid attenuated inversion recovery) sequences at 3T MRI (magnetic resonance imaging) for detection and grading of EH in patients with clinically suspected MD.

Anti-MOG Antibody-Associated Unilateral Cortical Encephalitis with Bilateral Meningeal Involvement: A Case Report

A 27-year-old Han Chinese woman presented with fever, headache, lethargy, and difficulty in expression. Magnetic resonance imaging (MRI) detected extensive hyperintensity of the left-sided frontoparietal, temporal, occipital, and insular cortices via fluid-attenuated inversion recovery (FLAIR) imaging. Post-contrast MRI revealed linear enhancement in the frontoparietal, temporal, and occipital sulci bilaterally. The detection of anti-myelin oligodendrocyte glycoprotein (MOG) was positive in the cerebrospinal fluid (CSF) and serum. The patient was diagnosed with anti-MOG antibody-associated unilateral cortical encephalitis with bilateral meningeal involvement. The patient received low doses of intravenous dexamethasone followed by oral prednisone, which was tapered until withdrawal. The treatment significantly improved the patient's symptoms. A one-month follow-up showed that the patient gradually resumed her normal lifestyle. No further relapse was recorded after a one-year follow-up. MRI performed almost a year after the initial symptom onset showed that the FLAIR signal had decreased in the left insular lobe, and the abnormal cortical signal of the FLAIR in the original left frontotemporal occipital lobe had disappeared. Thus, we report a rare case of anti-MOG antibody encephalitis (unilateral cortical encephalitis with bilateral meningeal involvement) in an adult patient. This study provides a reference for the clinical diagnosis and treatment of MOG antibody-associated unilateral cortical encephalitis.

Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study

IntroductionBreast cancer is the most common malignancy among women, and its diagnosis relies on medical imaging and the invasive, uncomforted biopsy. Recent advances in quantitative imaging and specifically the application of radiomics has proved to be a very promising technique, facilitating both diagnosis and therapy. The purpose of this study is to assess radiomic features derived from post-contrast T1w Magnetic Resonance Imaging (MRI) sequences and Apparent Diffusion Coefficient (ADC) maps for the evaluation of breast pathologies. MethodsMRI data from 52 women were retrospectively reviewed, involving 54 breast lesions, both malignant and benign. Diffusion Weighted Imaging (DWI) was applied as a standard MRΙ protocol, including dynamic contrast-enhanced (DCE) MRΙ in all cases. All patients were examined on a 1.5T MRI scanner, and 216 features were initially extracted from DCE-MRI images. Histological analysis of the breast lesions was performed, and a comparative analysis of the results was carried out to assess the accuracy of the method. ResultsFollowing surgery and histological analysis, 30 lesions were found to be malignant and 24 benign. Implementation of a Machine Learning (ML) classification algorithm with 5-fold cross-validation resulted in a sensitivity of 70%, specificity of 66%, Negative Predictive Value of 82% and overall accuracy of 67% in differentiating malignancy from benevolence. ConclusionTexture analysis and ML methodology based on the first post-contrast dynamic sequences and ADC maps may be employed to differentiate between malignant and benign breast lesions, offering a promising new tool for diagnostic analysis. Implications for practiceThe results of this study will enhance knowledge around application and performance of radiomics in breast MRI, thus helping MRI radiographers who use AI-enabled technologies to better delineate the pros and cons of these procedures.

Orbital Myositis Mimicking Wall-Eyed Bilateral Internuclear Ophthalmoplegia

A 56-year-old woman reported a 2-week history of diplopia associated with mild pain and periorbital edema. On examination, the patient had a large exotropia (A). While vertical eye movements were intact, there was limited adduction of each eye on right (B) and left (C) gaze. These findings were consistent with wall-eyed bilateral internuclear ophthalmoplegia. Postcontrast axial T1 fat-suppressed magnetic resonance imaging (D) revealed bilateral enlargement and enhancement of the medial rectus muscles indicative of orbital myositis.

Predicting the grade of meningiomas by clinical-radiological features: A comparison of precontrast and postcontrast MRI.

Postcontrast magnetic resonance imaging (MRI) is important for the differentiation between low-grade (WHO I) and high-grade (WHO II/III) meningiomas. However, nephrogenic systemic fibrosis and cerebral gadolinium deposition are major concerns for postcontrast MRI. This study aimed to develop and validate an accessible risk-scoring model for this differential diagnosis using the clinical characteristics and radiological features of precontrast MRI. From January 2019 to October 2021, a total of 231 meningioma patients (development cohort n = 137, low grade/high grade, 85/52; external validation cohort n = 94, low-grade/high-grade, 60/34) were retrospectively included. Fourteen types of demographic and radiological characteristics were evaluated by logistic regression analyses in the development cohort. The selected characteristics were applied to develop two distinguishing models using nomograms, based on full MRI and precontrast MRI. Their distinguishing performances were validated and compared using the external validation cohort. One demographic characteristic (male), three precontrast MRI features (intratumoral cystic changes, lobulated and irregular shape, and peritumoral edema), and one postcontrast MRI feature (absence of a dural tail sign) were independent predictive factors for high-grade meningiomas. The area under the receiver operating characteristic (ROC) curve (AUC) values of the two distinguishing models (precontrast-postcontrast nomogram vs. precontrast nomogram) in the development cohort were 0.919 and 0.898 and in the validation cohort were 0.922 and 0.878. DeLong's test showed no statistical difference between the AUC values of the two distinguishing models (p = 0.101). An accessible risk-scoring model based on the demographic characteristics and radiological features of precontrast MRI is sufficient to distinguish between low-grade and high-grade meningiomas, with a performance equal to that of a full MRI, based on radiological features.

BIOM-44. MAGNETIC RESONANCE IMAGING MAPPING OF BRAIN TUMOR BURDEN TO EVALUATE RESPONSE TO PULSED LOW-DOSE-RATE RADIOTHERAPY FOLLOWING RESECTION OF GLIOBLASTOMA MULTIFORME: A 4-PATIENT CASE SERIES

Abstract BACKGROUND Pulsed low-dose-rate radiotherapy (pLDR) is an accepted reirradiation technique for recurrent glioma, but its upfront use, concurrent with temozolomide (TMZ) following resection of high-grade glioma is currently under investigation. Evaluating the response to upfront radiation can be challenging and there is limited data on expected rates of pseudoprogression with pLDR. Standard magnetic resonance imaging (MRI) has limitations in differentiating pseudoprogression from tumor progression, sometimes necessitating surgery for pathologic confirmation. Advanced MRI can be used to create fractional tumor burden (FTB) maps that spatially distinguish active tumor from treatment-related effect (pseudoprogression), perhaps providing a more reliable imaging biomarker in the absence of additional surgery. METHODS We performed a retrospective chart review to report the responses of four patients with glioblastoma to upfront pLDR and TMZ following resection. Each patient received advanced surveillance MRI and redo surgery. Tumor pathology included IDH-wild type (n = 4) and O6-methylguanine-DNA methyltransferase (MGMT) methylated (n = 1) tumors. RESULTS The median age of patients was 57.5 years (range 55-60 years) and all were male. One patient experienced mortality and another was transitioned to hospice. In all four cases, there were concerns of tumor progression in postcontrast MRI. Pathologic diagnosis revealed either treatment effect (n = 2) or tumor (n = 2). FTB maps were predominantly indicative of lesion volumes being comprised of treatment effect (n = 3) and tumor (n = 1). From the three FTB maps in the former category, the median fraction of the enhancing tumor volume comprised of vascular tumor was 6.4% (range 1.8-6.8%). CONCLUSION This case series provides insight into response to upfront pLDR and TMZ following resection of glioblastoma and demonstrates the capacity for FTB mapping to spatially distinguish tumor progression from treatment effect in this patient population.

Temporal radiographic and histological study of necrosis development in a mouse glioblastoma model.

Tumor necrosis is a poor prognostic marker in glioblastoma (GBM) and a variety of other solid cancers. Accumulating evidence supports that necrosis could facilitate tumor progression and resistance to therapeutics. GBM necrosis is typically first detected by magnetic resonance imaging (MRI), after prominent necrosis has already formed. Therefore, radiological appearances of early necrosis formation and the temporal-spatial development of necrosis alongside tumor progression remain poorly understood. This knowledge gap leads to a lack of reliable radiographic diagnostic/prognostic markers in early GBM progression to detect necrosis. Recently, we reported an orthotopic xenograft GBM murine model driven by hyperactivation of the Hippo pathway transcriptional coactivator with PDZ-binding motif (TAZ) which recapitulates the extent of GBM necrosis seen among patients. In this study, we utilized this model to perform a temporal radiographic and histological study of necrosis development. We observed tumor tissue actively undergoing necrosis first appears more brightly enhancing in the early stages of progression in comparison to the rest of the tumor tissue. Later stages of tumor progression lead to loss of enhancement and unenhancing signals in the necrotic central portion of tumors on T1-weighted post-contrast MRI. This central unenhancing portion coincides with the radiographic and clinical definition of necrosis among GBM patients. Moreover, as necrosis evolves, two relatively more contrast-enhancing rims are observed in relationship to the solid enhancing tumor surrounding the central necrosis in the later stages. The outer more prominently enhancing rim at the tumor border probably represents the infiltrating tumor edge, and the inner enhancing rim at the peri-necrotic region may represent locally infiltrating immune cells. The associated inflammation at the peri-necrotic region was further confirmed by immunohistochemical study of the temporal development of tumor necrosis. Neutrophils appear to be the predominant immune cell population in this region as necrosis evolves. This study shows central, brightly enhancing areas associated with inflammation in the tumor microenvironment may represent an early indication of necrosis development in GBM.

Automated Brain Metastases Segmentation With a Deep Dive Into False-positive Detection

PurposeThe clinical management of brain metastases after stereotactic radiosurgery (SRS) is difficult, because a physician must review follow-up magnetic resonance imaging (MRI) scans to determine treatment outcome, which is often labor intensive. The purpose of this study was to develop an automated framework to contour brain metastases in MRI to help treatment planning for SRS and understand its limitations. Methods and MaterialsTwo self-adaptive nnU-Net models trained on postcontrast 3-dimensional T1-weighted MRI scans from patients who underwent SRS were analyzed. Performance was evaluated by computing positive predictive value (PPV), sensitivity, and Dice similarity coefficient (DSC). The training and testing sets included 3482 metastases on 845 patient MRI scans and 930 metastases on 206 patient MRI scans, respectively. ResultsIn the per-patient analysis, PPV was 90.1% ± 17.7%, sensitivity 88.4% ± 18.0%, DSC 82.2% ± 9.5%, and false positive (FP) 0.4 ± 1.0. For large metastases (≥6 mm), the per-patient PPV was 95.6% ± 17.5%, sensitivity 94.5% ± 18.1%, DSC 86.8% ± 7.5%, and FP 0.1 ± 0.4. The quality of autosegmented true-positive (TP) contours was also assessed by 2 physicians using a 5-point scale for clinical acceptability. Seventy-five percent of contours were assigned scores of 4 or 5, which shows that contours could be used as-is in clinical application, and the remaining 25% were assigned a score of 3, which means they needed minor editing only. Notably, a deep dive into FPs indicated that 9% were TP metastases not identified on the original radiology review, but identified on subsequent follow-up imaging (early detection). Fifty-four percent were real metastases (TP) that were identified but purposefully not contoured for target treatment, mainly because the patient underwent whole-brain radiation therapy before/after SRS treatment. ConclusionsThese findings show that our tool can help radiologists and radiation oncologists detect and contour tumors from MRI, make precise decisions about suspicious lesions, and potentially find lesions at early stages.

Does diploic venous flow drain extracranially in the pterional area? A magnetic resonance imaging study.

To the best of our knowledge, no study using neuroimaging modalities has documented calvarial diploic veins (DVs) connected to the extracranial sites. This study aimed to characterize them using magnetic resonance imaging (MRI). A total of 88 patients underwent thin-sliced contrast MRI. In addition, the DVs coursing through the pterional area were observed on three injected cadaver heads. On postcontrast MRI, the DVs of the pterional area directly drained into the temporalis muscle or supplied branches coursing into the muscle in 43% on the right side and 40% on the left. The DVs and their branches were highly variable in diameter and number. In 9% of cases, the DV of the pterional area was found to drain into an extracranial site and connected to large venous channels distributed in the temporalis muscle. Furthermore, in 17% of cases, the DVs drained into the extracranial sites in the frontal skull region, followed by 9% in the parietal, 2% in the occipital, and 1% in the temporal regions. The DVs coursed superficially in the pterional area on both sides of all three cadaver heads. On one side, the DV in the area was open to an extracranial site. Part of the diploic venous flow drains extracranially in the pterional area. This area may provide an important interface between the calvarial DVs and the extracranial venous system.

Contrast Enhancement Patterns in Pediatric Glioblastomas.

Brain tumors are the most common cause of cancer-related deaths among the pediatric population. Among these, pediatric glioblastomas (GBMs) comprise 2.9% of all central nervous system tumors and have a poor prognosis. The purpose of this study is to determine whether the imaging findings can be a prognostic factor for survival in children with GBMs. The imaging studies and clinical data from 64 pediatric patients with pathology-proven GBMs were evaluated. Contrast enhancement patterns were classified into focal, ring-like, and diffuse, based on preoperative postcontrast T1-weighted magnetic resonance images. We used the Kaplan-Meier method and Cox proportional hazard regression to evaluate the prognostic value of imaging findings. Patients with ring-enhanced GBMs who underwent gross total resection or subtotal resection were found to have a significantly shorter progression-free survival ( P = 0.03) comparing with other enhancing and nonenhancing glioblastomas. In this study, we analyzed survival factors in children with pediatric glioblastomas. In the group of patients who underwent gross total resection or subtotal resection, those patients with focal-enhanced GBMs had significantly longer progression-free survival ( P = 0.03) than did those with other types of enhancing GBMs (diffuse and ring-like).