Transient deoxyhemoglobin formation as a contrast for perfusion MRI studies in patients with brain tumors: a feasibility study.

Background: Transient hypoxia-induced deoxyhemoglobin (dOHb) has recently been shown to represent a comparable contrast to gadolinium-based contrast agents for generating resting perfusion measures in healthy subjects. Here, we investigate the feasibility of translating this non-invasive approach to patients with brain tumors. Methods: A computer-controlled gas blender was used to induce transient precise isocapnic lung hypoxia and thereby transient arterial dOHb during echo-planar-imaging acquisition in a cohort of patients with different types of brain tumors (n = 9). We calculated relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit time (MTT) using a standard model-based analysis. The transient hypoxia induced-dOHb MRI perfusion maps were compared to available clinical DSC-MRI. Results: Transient hypoxia induced-dOHb based maps of resting perfusion displayed perfusion patterns consistent with underlying tumor histology and showed high spatial coherence to gadolinium-based DSC MR perfusion maps. Conclusion: Non-invasive transient hypoxia induced-dOHb was well-tolerated in patients with different types of brain tumors, and the generated rCBV, rCBF and MTT maps appear in good agreement with perfusion maps generated with gadolinium-based DSC MR perfusion.

Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac.

Background and Purpose: The feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and Methods: Apparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. Results: Phantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. Conclusion: Accurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.

MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and "proof of principle".

Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clinical decision-making. In this study, we aimed to investigate the potential of radiomic features as imaging biomarker to distinguish between high grade and low-grade salivary gland malignancies. We have also investigated the effect of image and feature level harmonization on the performance of radiomic models. For this study, our dual center cohort consisted of 126 patients, with histologically proven SGC, who underwent curative-intent treatment in two tertiary oncology centers. We extracted and analyzed the radiomics features of 120 pre-therapeutic MRI images with gadolinium (T1 sequences), and correlated those with the definitive post-operative histology. In our study the best radiomic model achieved average AUC of 0.66 and balanced accuracy of 0.63. According to the results, there is significant difference between the performance of models based on MRI intensity normalized images + harmonized features and other models (p value < 0.05) which indicates that in case of dealing with heterogeneous dataset, applying the harmonization methods is beneficial. Among radiomic features minimum intensity from first order, and gray level-variance from texture category were frequently selected during multivariate analysis which indicate the potential of these features as being used as imaging biomarker. The present bicentric study presents for the first time the feasibility of implementing MR-based, handcrafted radiomics, based on T1 contrast-enhanced sequences and the ComBat harmonization method in an effort to predict the formal grading of salivary gland carcinoma with satisfactory performance.

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation.

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.

A phase Ib/II randomized, open-label drug repurposing trial of glutamate signaling inhibitors in combination with chemoradiotherapy in patients with newly diagnosed glioblastoma: the GLUGLIO trial protocol.

BACKGROUND: Glioblastoma is the most common and most aggressive malignant primary brain tumor in adults. Glioblastoma cells synthesize and secrete large quantities of the excitatory neurotransmitter glutamate, driving epilepsy, neuronal death, tumor growth and invasion. Moreover, neuronal networks interconnect with glioblastoma cell networks through glutamatergic neuroglial synapses, activation of which induces oncogenic calcium oscillations that are propagated via gap junctions between tumor cells. The primary objective of this study is to explore the efficacy of brain-penetrating anti-glutamatergic drugs to standard chemoradiotherapy in patients with glioblastoma. METHODS/DESIGN: GLUGLIO is a 1:1 randomized phase Ib/II, parallel-group, open-label, multicenter trial of gabapentin, sulfasalazine, memantine and chemoradiotherapy (Arm A) versus chemoradiotherapy alone (Arm B) in patients with newly diagnosed glioblastoma. Planned accrual is 120 patients. The primary endpoint is progression-free survival at 6 months. Secondary endpoints include overall and seizure-free survival, quality of life of patients and caregivers, symptom burden and cognitive functioning. Glutamate levels will be assessed longitudinally by magnetic resonance spectroscopy. Other outcomes of interest include imaging response rate, neuronal hyperexcitability determined by longitudinal electroencephalography, Karnofsky performance status as a global measure of overall performance, anticonvulsant drug use and steroid use. Tumor tissue and blood will be collected for translational research. Subgroup survival analyses by baseline parameters include segregation by age, extent of resection, Karnofsky performance status, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, steroid intake, presence or absence of seizures, tumor volume and glutamate levels determined by MR spectroscopy. The trial is currently recruiting in seven centers in Switzerland. TRIAL REGISTRATION: NCT05664464. Registered 23 December 2022.

Neurologic involvement in cystinosis: Focus on brain lesions and new evidence of four-repeat (4R-) Tau immunoreactivity.

Nephropathic cystinosis is a rare autosomal recessive storage disorder caused by CTNS gene mutations, leading to autophagy-lysosomal pathway impairment and cystine crystals accumulation. Neurologic involvement is highly variable and includes both neurodevelopmental and neurodegenerative disturbances, as well as focal neurologic deficits. By presenting longitudinal data of a 28-year-old patient with a large infratentorial lesion, we summarized the pathology, clinical and imaging features of neurological involvement in cystinosis patients. Brain damage in form of cystinosis-related cerebral lesions occurs in advanced disease phases and is characterized by the accumulation of cystine crystals, subsequent inflammation with vasculitis-like features, necrosis, and calcification. Epilepsy is a frequent comorbidity in affected individuals. Steroids might play a role in the symptomatic treatment of "stroke-like" episodes due to edematous-inflammatory lesions, but probably do not change the overall prognosis. Lifelong compliance to depleting therapy with cysteamine still represents the main therapeutic option. However, consequences of CTNS gene defects are not restricted to cystine accumulation. New evidence of four-repeat (4R-) Tau immunoreactivity suggests concurrent progressive neurodegeneration in cystinosis patients, highlighting the need of innovative therapeutic strategies, and shedding light on the crosstalk between proteinopathies and autophagy-lysosomal system defects. Eventually, emerging easily accessible biomarkers such as serum neurofilament light chains (NfL) might detect subclinical neurologic involvement in cystinosis patients.

Resolving the Smoking Paradox: No Evidence for Smoking-Induced Preconditioning in Large Vessel Occlusion Stroke.

INTRODUCTION: Smoking is an established risk factor for stroke. However, several studies have reported a better outcome after stroke for patients who smoke. According to this "smoking paradox" hypothesis, smoking might promote less severe strokes, higher collateral scores, and smaller infarct cores. METHODS: In this retrospective study, we screened data of 2,980 acute ischemic stroke patients with MCA-M1 occlusion treated with mechanical thrombectomy. Patients were categorized according to smoking status (current, former, or never). We assessed univariate associations between clinical characteristics and smoking status. Subsequently, we used adjusted regression analysis to evaluate associations of smoking with stroke severity on admission (National Institutes of Health Stroke Scale [NIHSS]; primary endpoint), infarct core volume, and collateral status (secondary endpoints). RESULTS: Out of 320 patients, 19.7% (n = 63) were current smokers and 18.8% (n = 60) were former smokers. Admission NIHSS, reperfusion success, and modified Rankin Scale (mRS) after 3-6 months were similar in all groups. Current smokers were younger, more often male and less likely to have atrial fibrillation compared to former and never smokers. In regression analyses, smoking status was neither associated with admission NIHSS (estimate 0.54, 95% confidence interval [CI]: -1.27-2.35, p = 0.557) nor with collateral status (estimate 0.79, 95% CI: 0.44-1.44, p = 0.447) or infarct core volume (estimate -0.69, 95% CI: -15.15-13.77, p = 0.925 for current vs. never smokers). CONCLUSION: We could not confirm the smoking paradox. Our results support the fact that smoking causes stroke at a younger age, highlighting the role of smoking as a modifiable vascular risk factor.

Assessing Perfusion in Steno-Occlusive Cerebrovascular Disease Using Transient Hypoxia-Induced Deoxyhemoglobin as a Dynamic Susceptibility Contrast Agent.

BACKGROUND AND PURPOSE: Resting brain tissue perfusion in cerebral steno-occlusive vascular disease can be assessed by MR imaging using gadolinium-based susceptibility contrast agents. Recently, transient hypoxia-induced deoxyhemoglobin has been investigated as a noninvasive MR imaging contrast agent. Here we present a comparison of resting perfusion metrics using transient hypoxia-induced deoxyhemoglobin and gadolinium-based contrast agents in patients with known cerebrovascular steno-occlusive disease. MATERIALS AND METHODS: Twelve patients with steno-occlusive disease underwent DSC MR imaging using a standard bolus of gadolinium-based contrast agent compared with transient hypoxia-induced deoxyhemoglobin generated in the lungs using an automated gas blender. A conventional multi-slice 2D gradient echo sequence was used to acquire the perfusion data and analyzed using a standard tracer kinetic model. MTT, relative CBF, and relative CBV maps were generated and compared between contrast agents. RESULTS: The spatial distributions of the perfusion metrics generated with both contrast agents were consistent. Perfusion metrics in GM and WM were not statistically different except for WM MTT. CONCLUSIONS: Cerebral perfusion metrics generated with noninvasive transient hypoxia-induced changes in deoxyhemoglobin are very similar to those generated using a gadolinium-based contrast agent in patients with cerebrovascular steno-occlusive disease.

Corrigendum to "Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study" [Phys. Imaging Radiat. Oncol. 22 (2022) 131-136].

[This corrects the article DOI: 10.1016/j.phro.2022.05.006.].

Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study.

Background and purpose: Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods: Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results: Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions: MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions.

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading.

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.

Factors predicting help seeking for mental illness among college students.

BACKGROUND: Evidence supports the contribution of various stigma-related constructs to help-seeking. These constructs have yet to be tested in a single model among college students, a group highly affected by mental illness. AIMS: Using data from 153 college students, this study examines factors contributing to help seeking for mental illness. METHOD: Using path analysis, the current study evaluated a model of the relationship between level of familiarity, personal stigma, desired social distance, label avoidance, attitudes towards treatment seeking and intentions to seek treatment. RESULTS: Findings support a model of help-seeking describing the relationship between familiarity with mental illness, personal stigma, social distance, label avoidance, attitudes and intentions to seek treatment. CONCLUSIONS: Findings suggest label avoidance, attitudes towards treatment seeking and intentions to seek treatment might be augmented through interventions aimed at increasing college students' levels of familiarity, or intimate contact, with individuals with mental illness. Additional implications for practice and further research are addressed.

Machine-learning-based outcome prediction in stroke patients with middle cerebral artery-M1 occlusions and early thrombectomy.

BACKGROUND AND PURPOSE: Clinical outcomes vary substantially among individuals with large vessel occlusion (LVO) stroke. A small infarct core and large imaging mismatch were found to be associated with good recovery. The aim of this study was to investigate whether those imaging variables would improve individual prediction of functional outcome after early (<6 h) endovascular treatment (EVT) in LVO stroke. METHODS: We included 222 patients with acute ischemic stroke due to middle cerebral artery (MCA)-M1 occlusion who received EVT. As predictors, we used clinical variables and region of interest (ROI)-based magnetic resonance imaging features. We developed different machine-learning models and quantified their prediction performance according to the area under the receiver-operating characteristic curves and the Brier score. RESULTS: The rate of successful recanalization was 78%, with 54% patients having a favorable outcome (modified Rankin scale score 0-2). Small infarct core was associated with favorable functional outcome. Outcome prediction improved only slightly when imaging was added to patient variables. Age was the driving factor, with a sharp decrease in likelihood of favorable functional outcome above the age of 78 years. CONCLUSIONS: In patients with MCA-M1 occlusion strokes referred to EVT within 6 h of symptom onset, infarct core volume was associated with outcome. However, ROI-based imaging variables led to no significant improvement in outcome prediction at an individual patient level when added to a set of clinical predictors. Our study is in concordance with current practice, where imaging mismatch or collateral readouts are not recommended as factors for excluding patients with MCA-M1 occlusion for early EVT.

Immunocytokines are a promising immunotherapeutic approach against glioblastoma.

Glioblastoma is a poorly immunogenic cancer, and the successes with recent immunotherapies in extracranial malignancies have, so far, not been translated to this devastating disease. Therefore, there is an urgent need for new strategies to convert the immunologically cold glioma microenvironment into a hot one to enable effective antitumor immunity. Using the L19 antibody, which is specific to a tumor-associated epitope of extracellular fibronectin, we developed antibody-cytokine fusions-immunocytokines-with interleukin-2 (IL2), IL12, or tumor necrosis factor (TNF). We showed that L19 accumulated in the tumor microenvironment of two orthotopic immunocompetent mouse glioma models. Furthermore, intravenous administration of L19-mIL12 or L19-mTNF cured a proportion of tumor-bearing mice, whereas L19-IL2 did not. This therapeutic activity was abolished in RAG-/- mice or upon depletion of CD4 or CD8 T cells, suggesting adaptive immunity. Mechanistically, both immunocytokines promoted tumor-infiltrating lymphocytes and increased the amounts of proinflammatory cytokines within the tumor microenvironment. In addition, L19-mTNF induced tumor necrosis. Systemic administration of the fully human L19-TNF fusion protein to patients with glioblastoma (NCT03779230) was safe, decreased regional blood perfusion within the tumor, and was associated with increasing tumor necrosis and an increase in tumor-infiltrating CD4 and CD8 T cells. The extensive preclinical characterization and subsequent clinical translation provide a robust basis for future studies with immunocytokines to treat malignant brain tumors.

Evaluation of the clinical utility of maximum intensity projections of 3D contrast-enhanced, T1-weighted imaging for the detection of brain metastases.

BACKGROUND: To visualize and assess brain metastases on magnetic resonance imaging, radiologists face an ever-increasing pressure to perform faster and more efficiently. The usage of maximum intensity projections (MIPs) of contrast-enhanced T1-weighed (T1ce) magnetization-prepared rapid acquisition with gradient echo (MP-RAGE) images proposes to increase reading efficiency by increasing lesion conspicuity while reducing in the number of images to be reviewed. AIM: To assess if MIPs save reading time and achieve the same level of diagnostic accuracy as standard 1 mm T1ce images for the detection of brain metastases. METHODS: Forty-four patients were included in this retrospective study. Axial reformations of T1ce MP-RAGE (TR/TE = 2300/2.25 ms, resolution = 1 mm3 ) images were analyzed and post-processed into 5 and 10 mm MIPs. Two readers evaluated the randomly assorted images and recorded reading time. Reading time differences were analyzed using the Wilcoxon test, and inter-reader statistics were performed using Bland-Altman plots. RESULTS: About 22.5 61.2 s/study and 43.8 ± 159.9 s/study were saved using 5 and 10 mm MIPs, respectively. Combined average sensitivity was 92.0% for 5 mm MIPs and 86.3% for 10 mm MIPs compared to standard 1 mm axial slices, with an average rate of 0.98 and 0.57 false positives per study, respectively CONCLUSION: While 5 mm and 10 mm T1ce MP-RAGE MIPs showed a clinical benefit in reducing reading times for evaluation of brain metastases, they should be used in conjunction with standard 1 mm images for best sensitivity and specificity, a practice which possibly annuls their benefit.

Deep learning based detection of intracranial aneurysms on digital subtraction angiography: A feasibility study.

BACKGROUND: Digital subtraction angiography is the gold standard for detecting and characterising aneurysms. Here, we assess the feasibility of commercial-grade deep learning software for the detection of intracranial aneurysms on whole-brain anteroposterior and lateral 2D digital subtraction angiography images. MATERIAL AND METHODS: Seven hundred and six digital subtraction angiography images were included from a cohort of 240 patients (157 female, mean age 59 years, range 20-92; 83 male, mean age 55 years, range 19-83). Three hundred and thirty-five (47%) single frame anteroposterior and lateral images of a digital subtraction angiography series of 187 aneurysms (41 ruptured, 146 unruptured; average size 7±5.3 mm, range 1-5 mm; total 372 depicted aneurysms) and 371 (53%) aneurysm-negative study images were retrospectively analysed regarding the presence of intracranial aneurysms. The 2D data was split into testing and training sets in a ratio of 4:1 with 3D rotational digital subtraction angiography as gold standard. Supervised deep learning was performed using commercial-grade machine learning software (Cognex, ViDi Suite 2.0). Monte Carlo cross validation was performed. RESULTS: Intracranial aneurysms were detected with a sensitivity of 79%, a specificity of 79%, a precision of 0.75, a F1 score of 0.77, and a mean area-under-the-curve of 0.76 (range 0.68-0.86) after Monte Carlo cross-validation, run 45 times. CONCLUSION: The commercial-grade deep learning software allows for detection of intracranial aneurysms on whole-brain, 2D anteroposterior and lateral digital subtraction angiography images, with results being comparable to more specifically engineered deep learning techniques.

Golden-angle radial sparse parallel (GRASP) MRI in clinical routine detection of pituitary microadenomas: First experience and feasibility.

Background and purpose To demonstrate the clinical feasibility of a novel MRI pulse sequence, Golden-angle radial sparse parallel MRI (GRASP) through comparison to the current imaging technique, dynamic T1- weighted contrast enhanced (DCE) imaging in terms of image quality and lesion depiction in the detection of microlesions (microadenomas and cysts) of the pituitary gland. MATERIALS AND METHODS: 16 patients (11 microadenomas, 5 cysts) underwent two MRI examinations (Siemens 1.5T and 3T) on separate dates, one using standard DCE (temporal resolution 30 s) and the other using GRASP (temporal resolution of 4.4 s). Two neuroradiologists separately recorded measures of image quality (Scale 1-5, 5 = best), lesion size and contrast arrival times in terms of first and best lesion conspicuity. RESULTS: In qualitiative analysis there were no significant differences in terms of average visual image sharpness (DCE 3.9 ±â€¯0.9, GRASP 3.9 ±â€¯0.9) or visual contrast scores (DCE 4.1 ±â€¯1.2, GRASP 4.4 ±â€¯0.8). Pearson's correlation coefficients for interreader lesion measurements (width and height, mm) ranged from substantial to almost perfect agreement (r = 0.73 to 0.88). Analysis of contrast arrival times revealed an average lesion first-conspicuity time of 60.7 ±â€¯16.7 s for DCE compared to 50.2 ±â€¯10.3 s for GRASP with a difference of 10.5 ±â€¯16.2 s (p = 0.023). CONCLUSION: Depiction of pituitary microlesions is feasible with GRASP, which has the potential to increase sensitivity through higher temporal resolutions combined with isotropic acquisition allowing for multi-planar reconstructions; this remains to be proven in larger cohorts.

Dopamine agonist-responsive Cushing's disease.

A 47-year-old Caucasian man was referred to our clinic with a severe clinical and biochemical phenotype of endogenous hypercortisolism for further evaluation and treatment. In addition to confirming adrenocorticotropic hormone (ACTH)-dependent Cushing's syndrome, we found left temporal hemianopsia, massively increased prolactin, increased growth hormone/insulin-like growth factor 1 values, hypogonadotropic hypogonadism and central hypothyroidism. As the cause of these abnormalities we revealed an invasive macroadenoma of the pituitary secreting ACTH, prolactin and growth hormone, resulting not only in a clinically predominant picture of Cushing's syndrome but also causing hypogonadotropic hypogonadism and central hypothyroidism. The patient responded surprisingly well to dopamine agonist treatment leading not only to normalisation of prolactin levels but also to clinical and biochemical remission of Cushing's syndrome. Tumour size decreased successively in follow-up MRI scans. Despite lacking immunohistochemical analysis of tumour tissue, we assume plurihormonal secretion of ACTH, prolactin and growth hormone from pituitary macroadenoma, which fortunately responded well to dopamine agonist treatment.