Distinguishing Brain Metastasis Progression From Radiation Effects After Stereotactic Radiosurgery Using Longitudinal GRASP Dynamic Contrast-Enhanced MRI
BACKGROUND:Differentiating brain metastasis progression from radiation effects or radiation necrosis (RN) remains challenging. Golden-angle radial sparse parallel (GRASP) dynamic contrast-enhanced MRI provides high spatial and temporal resolution to analyze tissue enhancement, which may differ between tumor progression (TP) and RN. OBJECTIVE:To investigate the utility of longitudinal GRASP MRI in distinguishing TP from RN after gamma knife stereotactic radiosurgery (SRS). METHODS:We retrospectively evaluated 48 patients with brain metastasis managed with SRS at our institution from 2013 to 2020 who had GRASP MRI before and at least once after SRS. TP (n = 16) was pathologically confirmed. RN (n = 16) was diagnosed on either resected tissue without evidence of tumor or on lesion resolution on follow-up. As a reference, we included a separate group of patients with non-small-cell lung cancer that showed favorable response with tumor control and without RN on subsequent imaging (n = 16). Mean contrast washin and washout slopes normalized to the superior sagittal sinus were compared between groups. Receiver operating characteristic analysis was performed to determine diagnostic performance. RESULTS:After SRS, progression showed a significantly steeper washin slope than RN on all 3 follow-up scans (scan 1: 0.29 ± 0.16 vs 0.18 ± 0.08, P = .021; scan 2: 0.35 ± 0.19 vs 0.18 ± 0.09, P = .004; scan 3: 0.32 ± 0.12 vs 0.17 ± 0.07, P = .002). No significant differences were found in the post-SRS washout slope. Post-SRS washin slope differentiated progression and RN with an area under the curve (AUC) of 0.74, a sensitivity of 75%, and a specificity of 69% on scan 1; an AUC of 0.85, a sensitivity of 92%, and a specificity of 69% on scan 2; and an AUC of 0.87, a sensitivity of 63%, and a specificity of 100% on scan 3. CONCLUSION:Longitudinal GRASP MRI may help to differentiate metastasis progression from RN.
Development and validation of a simple and practical method for differentiating MS from other neuroinflammatory disorders based on lesion distribution on brain MRI
There is an unmet need to develop practical methods for differentiating multiple sclerosis (MS) from other neuroinflammatory disorders using standard brain MRI. To develop a practical approach for differentiating MS from neuromyelitis optica spectrum disorder (NMOSD) and MOG antibody-associated disorder (MOGAD) with brain MRI, we first identified lesion locations in the brain that are suggestive of MS-associated demyelination ("MS Lesion Checklist") and compared frequencies of brain lesions in the "MS Lesion Checklist" locations in a development sample of patients (nÂ =Â 82) with clinically definite MS, NMOSD, and MOGAD. Patients with MS were more likely than patients with non-MS to have lesions in 3 locations only: anterior temporal horn (pÂ <Â 0.0001), periventricular ("Dawson's finger") (pÂ <Â 0.0001), and cerebellar hemisphere (pÂ =Â 0.02). These three lesion locations were used as predictor variables in a multivariable regression model for discriminating MS from non-MS. The model had area under the curve (AUC) of 0.853 (95% confidence interval: 0.76-0.945), sensitivity of 87.1%, and specificity of 72.5%. We then used an independent validation sample with equal representation of MS and NMOSD/MOGAD cases (nÂ =Â 97) to validate our prediction model. In the validation sample, the model was 76.3% accurate in discriminating MS from non-MS. Our simple method for predicting MS versus NMOSD/MOGAD only requires a neuroradiologist or clinician to ascertain the presence of lesions in three locations on conventional MRI sequences. It can therefore be readily applied in the real-world setting for training and clinical practice.
Clinical Course and Unique Features of Silent Corticotroph Adenomas
OBJECTIVE:Silent corticotroph adenomas (SCAs) behave more aggressively than other non-functioning adenomas (NFAs). This study aims to expand the body of knowledge of the behavior of SCAs. METHODS:Retrospective analysis of 196 non-corticotroph NFAs and 20 SCAs from 2012-2017 was completed. Demographics, clinical presentation, imaging and biochemical data were gathered. The primary endpoint was to identify features of SCAs vs. other NFAs that suggest aggressive disease, including pre-surgical comorbidities, postoperative complications, extent of tumor and recurrence. GRASP MRI images were obtained from a subset of SCAs and NFAs. Permeability data was obtained to compare signal-to-time curve variation between the two groups. RESULTS:With multivariate regression analysis, SCAs showed higher rates of hemorrhage on preoperative imaging than NFAs (p=0.017). SCAs presented more frequently with headache, vision changes and fatigue (p=0.012, p=0.041, p=0.028). SCAs exhibited greater extent of tumor burden with increased occurrence of stalk deviation, suprasellar invasion, optic chiasm compression and cavernous sinus invasion (p=0.008, p=0.021, p=0.022, p=0.015). On GRASP imaging, SCAs had significantly lower permeability of contrast than NFAs (p=0.001). 30% of SCAs were noted to recur with a 14% recurrence rate in other NFAs, though this difference was not of statistical significance (p=0.220). CONCLUSIONS:SCAs exhibit features of more aggressive disease. Interestingly, a significant increase in recurrence was not seen despite these features. The results of this study support the growing body of evidence that SCAs behave more aggressively than other NFPAs and was able to provide some insight into factors that may contribute to recurrence.
Radial spoiled gradient T1 weighted imaging of the internal auditory canal: Is Scarpa's ganglion now an expected finding and source of fundal enhancement?
StarVIBE is a 3D gradient-echo sequence with a radial, stack-of-stars acquisition having spatial resolution and tissue contrast. With newer sequences, it is important to be familiar with sequence tissue contrasts and appearance of anatomical variants. We evaluated 450 patients utilizing this sequence; 35 patients demonstrated fluffy "cotton wool" enhancement at the internal auditory canal fundus without clear pathology. We favor this represents anatomic neurovascular enhancement that StarVIBE is sensitive to and is a touch-me-not finding.
Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative Glioma Segmentation
BACKGROUND AND PURPOSE/OBJECTIVE:Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentation with a pipeline for clinical implementation. Developed and engineered in concert, the work seeks to accelerate clinical realization of such tools. MATERIALS AND METHODS/METHODS:A deep learning model, autoencoder regularization-cascaded anisotropic, was developed, trained, and tested fusing key elements of autoencoder regularization with a cascaded anisotropic convolutional neural network. We constructed a dataset consisting of 437 cases with 40 cases reserved as a held-out test and the remainder split 80:20 for training and validation. We performed data augmentation and hyperparameter optimization and used a mean Dice score to evaluate against baseline models. To facilitate clinical adoption, we developed the model with an end-to-end pipeline including routing, preprocessing, and end-user interaction. RESULTS:The autoencoder regularization-cascaded anisotropic model achieved median and mean Dice scores of 0.88/0.83 (SD, 0.09), 0.89/0.84 (SD, 0.08), and 0.81/0.72 (SD, 0.1) for whole-tumor, tumor core/resection cavity, and enhancing tumor subregions, respectively, including both preoperative and postoperative follow-up cases. The overall total processing time per case was âˆ¼10â€‰minutes, including data routing (âˆ¼1 minute), preprocessing (âˆ¼6 minute), segmentation (âˆ¼1-2 minute), and postprocessing (âˆ¼1 minute). Implementation challenges were discussed. CONCLUSIONS:We show the feasibility and advantages of building a coordinated model with a clinical pipeline for the rapid and accurate deep learning segmentation of both preoperative and postoperative gliomas. The ability of the model to accommodate cases of postoperative glioma is clinically important for follow-up. An end-to-end approach, such as used here, may lead us toward successful clinical translation of tools for quantitative volume measures for glioma.
Correction to: Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas
Correction: Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas
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.
Differentiation of Jugular Foramen Paragangliomas versus Schwannomas Using Golden-Angle Radial Sparse Parallel Dynamic Contrast-Enhanced MRI
BACKGROUND AND PURPOSE:Accurate differentiation of paragangliomas and schwannomas in the jugular foramen has important clinical implications because treatment strategies may vary but differentiation is not always straightforward with conventional imaging. Our aim was to evaluate the accuracy of both qualitative and quantitative metrics derived from dynamic contrast-enhanced MR imaging using golden-angle radial sparse parallel MR imaging to differentiate paragangliomas and schwannomas in the jugular foramen. MATERIALS AND METHODS:test. A univariate logistic model was created with a binary output, paraganglioma or schwannoma, using a wash-in rate as a variable. Additionally, lesions were clustered on the basis of the wash-in rate and washout rate using a 3-nearest neighbors method. RESULTS:< .001). All 30 lesions were classified correctly by using a 3-nearest neighbors method. CONCLUSIONS:Paragangliomas at the jugular foramen can be reliably differentiated from schwannomas using golden-angle radial sparse parallel MR imaging-dynamic contrast-enhanced imaging when imaging characteristics cannot suffice.
"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.