Correction to: Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas
"The Pituitary within GRASP" - Golden-Angle Radial Sparse Parallel Dynamic MRI Technique and Applications to the Pituitary Gland
MRI is the preferred radiologic modality for evaluating the pituitary gland. An important component of pituitary MRI examinations is dynamic contrast-enhanced MRI. Compared to conventional dynamic techniques, golden-angle radial sparse parallel (GRASP) imaging offers multiple advantages, including the ability to achieve higher spatial and temporal resolution. In this narrative review, we discuss dynamic imaging of the pituitary gland, the technical fundamentals of GRASP, and applications of GRASP to the pituitary gland.
Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas
PURPOSE/OBJECTIVE:Both prolactinomas and nonfunctioning adenomas (NFAs) can present with hyperprolactinemia. Distinguishing them is critical because prolactinomas are effectively managed with dopamine agonists, whereas compressive NFAs are treated surgically. Current guidelines rely only on serum prolactin (PRL) levels, which are neither sensitive nor specific enough. Recent studies suggest that accounting for tumor volume may improve diagnosis. The objective of this study is to investigate the diagnostic utility of PRL, tumor volume, and imaging features in differentiating prolactinoma and NFA. METHODS:Adult patients with pathologically confirmed prolactinoma (nâ€‰=â€‰21) or NFA with hyperprolactinemia (nâ€‰=â€‰58) between 2013 and 2020 were retrospectively identified. Diagnostic performance of clinical and imaging variables was analyzed using receiver-operating characteristic curves to calculate area under the curve (AUC). RESULTS:with sensitivity of 100% and specificity of 82.76%. Binary logistic regression found that PRL was a significant positive predictor of prolactinoma diagnosis, whereas tumor volume, presence of CSI not previously defined, and T2 hyperintensity were significant negative predictors. The regression model had an AUC of 0.9915 (pâ€‰<â€‰0.0001). CONCLUSIONS:Consideration of tumor volume improves differentiation between prolactinomas and NFAs, which in turn leads to effective management.
Correction: Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas (Endocrine, (2021), 10.1007/s12020-021-02744-8)
In this erratum the * sign has been included for the following authors Kyla Wright and Matthew Lee to indicate the first co-authors. In the abstract the CSI has been defined to "cavernous sinus invasion".The Original article has been updated.
Medical Student Engagement and Educational Value of a Remote Clinical Radiology Learning Environment: Creation of Virtual Read-Out Sessions in Response to the COVID-19 Pandemic
RATIONALE AND OBJECTIVES/OBJECTIVE:The need for social distancing has resulted in rapid restructuring of medical student education in radiology. While students traditionally spend time learning in the reading room, remote clinical learning requires material shared without direct teaching at the radiology workstation. Can remote clinical learning meet or exceed the educational value of the traditional in-person learning experience? Can student engagement be matched or exceeded in a remote learning environment? MATERIALS AND METHODS/METHODS:To replace the in-person reading room experience, a small-group learning session for medical students named Virtual Read-Out (VRO) was developed using teleconferencing software. After Institutional Review Board approval, two student groups were anonymously surveyed to assess differences in student engagement and perceived value between learning environments: "Conventional" students participating in the reading room (before the pandemic) and "Remote" students participating in VRO sessions. Students reported perceived frequency of a series of five-point Likert statements. Based on number of respondents, an independent t-test was performed to determine the significance of results between two groups. RESULTS:Twenty-seven conventional and 41 remote students responded. Remote students reported modest but significantly higher frequency of active participation in reviewing radiology exams (p < 0.05). There was significantly lower frequency of reported boredom among Remote students (p < 0.05). There was no significant difference in perceived educational value between the two groups. CONCLUSION/CONCLUSIONS:Students report a high degree of teaching quality, clinical relevance, and educational value regardless of remote or in-person learning format. Remote clinical radiology education can be achieved with equal or greater student interaction and perceived value in fewer contact hours than conventional learning in the reading room.
Surprise Diagnosis of COVID-19 following Neuroimaging Evaluation for Unrelated Reasons during the Pandemic in Hot Spots
During the height of the recent outbreak of coronavirus 19 (COVID-19) in New York City, almost all the hospital emergency departments were inundated with patients with COVID-19, who presented with typical fever, cough, and dyspnea. A small number of patients also presented with either unrelated conditions (such as trauma) or other emergencies, and some of which are now known to be associated with COVID-19 (such as stroke). We report such a scenario in 17 patients who were admitted and investigated with CT spine imaging and CT angiography for nonpulmonary reasons (traumaâ€‰= 13, strokeâ€‰= 4). Their initial work-up did not suggest COVID-19 as a diagnosis but showed unsuspected/incidental lung findings, which led to further investigations and a diagnosis of COVID-19.
Spontaneous, Intrasphenoidal Rupture of Ecchordosis Physaliphora with Pneumocephalus Captured During Serial Imaging and Clinical Follow-up: Pathoanatomic Features and Management [Case Report]
BACKGROUND:Ecchordosis physaliphora (EP) is a congenital, uniformly asymptomatic, hamartomatous lesion of the primitive notochord. Herein we report, to our knowledge, the first credible case report of unprovoked intra-sphenoidal rupture resulting in recurrent pneumocephalus and cerebrospinal fluid (CSF) leak, definitively captured over serial imaging during clinical and radiologic surveillance. CASE DESCRIPTION/METHODS:A 68-year old woman with Marfan syndrome presented to the Emergency Department with the worst headache of life. Imaging demonstrated extensive pneumocephalus and revealed a small, dorsal midline clival lesion consistent with EP and a trans-sphenoidal defect. Remote imaging encounters confirmed typical EP without pneumocephalus or cortical defect, and an uneventful clinical course years preceding presentation. Over the ensuing months during neurosurgical follow-up, the patient reported recurrent headaches, imbalance, and unprovoked clear rhinorrhea. Further imaging demonstrates an apparently enlarging trans-sphenoidal defect which was managed by endoscopic trans-nasal resection and nasoseptal flap. Pathologic evaluation confirmed the diagnosis of EP and chronic dural defect. CONCLUSIONS:This represents, to our knowledge, the first unambiguous example of spontaneous EP rupture and recurrent pneumocephalus captured over serial imaging. The case further underscores rare, but potentially significant complications of EP and highlights management options. BACKGROUND:. Herein we report, to our knowledge, the first documented spontaneous rupture of EP resulting in recurrent pneumocephalus, credibly captured over serial radiologic surveillance. CLINICAL PRESENTATION/METHODS:A 68 year-old woman with history of hypertension, hyperlipidemia, and Marfan syndrome presented to the Emergency Department reporting the "worst headache of her life" after engaging in an interpersonal dispute the evening preceding presentation.
COVID-19 related neuroimaging findings: A signal of thromboembolic complications and a strong prognostic marker of poor patient outcome
OBJECTIVE:To investigate the incidence and spectrum of neuroimaging findings and their prognostic role in hospitalized COVID-19 patients in New York City. METHODS:This is a retrospective cohort study of 3218 COVID-19 confirmed patients admitted to a major healthcare system (three hospitals) in New York City between March 1, 2020 and April 13, 2020. Clinical data were extracted from electronic medical records, and particularly data of all neurological symptoms were extracted from the imaging reports. Four neuroradiologists evaluated all neuroimaging studies for acute neuroimaging findings related to COVID-19. RESULTS:14.1% of admitted COVID-19 patients had neuroimaging and this accounted for only 5.5% of the total imaging studies. Acute stroke was the most common finding on neuro-imaging, seen in 92.5% of patients with positive neuro-imaging studies, and present in 1.1% of hospitalized COVID-19 patients. Patients with acute large ischemic and hemorrhagic stroke had much higher mortality risk adjusted for age, BMI and hypertension compared to those COVID-19 patients without neuroimaging. (Odds Ratio 6.02 by LR; Hazard Ratio 2.28 by CRR). CONCLUSIONS:Our study demonstrates acute stroke is the most common neuroimaging finding among hospitalized COVID-19 patients. Detection of an acute stroke is a strong prognostic marker of poor outcome. Our study also highlights the fact there is limited use of neuroimaging in these patients due to multiple logistical constraints.
Awake Laser Ablation for Patients With Tumors in Eloquent Brain Areas: Operative Technique and Case Series
Darts: Denseunet-based automatic rapid tool for brain segmentation
Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+ aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~ 1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation)