Gadolinium-enhanced T1-weighted Magnetic Resonance Imaging Research Articles

Simulation of the occipital transtentorial approach incorporating visualization of the cerebellar tentorium using three-dimensional computed tomography angiography and gadolinium-enhanced T1-weighted magnetic resonance imaging: technical note

The occipital transtentorial approach (OTA) is one of the useful approaches to the lesions of the pineal region, dorsal brainstem, and supracerebellar region. However, a wide operative field is sometimes difficult to obtain due to the tentorial sinus and bridging veins. This study evaluated the usefulness of preoperative simulation of OTA, specifically including the cerebellar tentorium in 9 patients. All patients underwent computed tomography angiography and venography and gadolinium-enhanced three-dimensional T1-weighted magnetic resonance images (Gd-3D-T1WI). The images were fused, and the cerebellar tentorium, vessels, and tumor were manually extracted from Gd-3D-T1WI to obtain the simulation images. Visualization of the cerebellar tentorium could discriminate between bridging veins from the occipital lobe and cerebellum, and recognize the site of bridging to the tentorial sinus and variants which may interfere with the tentorial incision. Simulation of the tentorial incision was also possible based on the relationships between the tumor, tentorial sinus, bridging vein, and cerebellar tentorium. The simulation suggested that safe tentorial incision was difficult in two sides because of the crossed tentorial sinus draining the left basal vein and draining veins from the glioblastoma. The OTA was performed in eight cases, and no difficulty was experienced in the tentorial incision in all cases. The simulation findings of the bridging vein and tentorial sinus were consistent with the intraoperative findings. Preoperative simulation including the cerebellar tentorium is useful for determining the optimum and safe side and required extent of the tentorial incision necessary for tumor resection with the OTA.

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy

Objective. Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No commercial models exist for OAR contouring based on brain magnetic resonance imaging (MRI). We trained and evaluated computed tomography (CT) and MRI OAR autosegmentation models in RayStation. To ascertain clinical usability, we investigated the geometric impact of contour editing before training on model quality. Approach. Retrospective glioma cases were randomly selected for training (n = 32, 47) and validation (n = 9, 10) for MRI and CT, respectively. Clinical contours were edited using international consensus (gold standard) based on MRI and CT. MRI models were trained (i) using the original clinical contours based on planning CT and rigidly registered T1-weighted gadolinium-enhanced MRI (MRIu), (ii) as (i), further edited based on CT anatomy, to meet international consensus guidelines (MRIeCT), and (iii) as (i), further edited based on MRI anatomy (MRIeMRI). CT models were trained using: (iv) original clinical contours (CTu) and (v) clinical contours edited based on CT anatomy (CTeCT). Auto-contours were geometrically compared to gold standard validation contours (CTeCT or MRIeMRI) using Dice Similarity Coefficient, sensitivity, and mean distance to agreement. Models’ performances were compared using paired Student’s t-testing. Main results. The edited autosegmentation models successfully generated more segmentations than the unedited models. Paired t-testing showed editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AC performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. No significant differences were found between the CTeCT and CTu except for optic chiasm. Significance. T1w-MRI DL-AC could segment all brain OARs except the lacrimal glands, which cannot be easily visualized on T1w-MRI. Editing contours on MRI before model training improved geometric performance. MRI DL-AC in RT may improve consistency, quality and efficiency but requires careful editing of training contours.

Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors.

It can be challenging in some situations to distinguish primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) based on magnetic resonance imaging (MRI) scans, especially those involving the corpus callosum. The objective of this study was to assess the diagnostic performance of deep learning (DL) models between PCNSLs and GBMs in corpus callosal tumors. The axial T1-weighted gadolinium-enhanced MRI scans of 274 individuals with pathologically confirmed PCNSL (n = 94) and GBM (n = 180) were examined. After image pooling, pre-operative MRI scans were randomly split with an 80/20 procedure into a training dataset (n = 709) and a testing dataset (n = 177) for DL model development. Therefore, the DL model was deployed as a web application and validated with the unseen images (n = 114) and area under the receiver operating characteristic curve (AUC); other outcomes were calculated to assess the discrimination performance. The first baseline DL model had an AUC of 0.77 for PCNSL when evaluated with unseen images. The 2nd model with ridge regression regularization and the 3rd model with drop-out regularization increased an AUC of 0.83 and 0.84. In addition, the last model with data augmentation yielded an AUC of 0.57. DL with regularization may provide useful diagnostic information to help doctors distinguish PCNSL from GBM.

Menière's Syndrome Associated With Fibrous Dysplasia of the Temporal Bone.

Menière's Syndrome Associated With Fibrous Dysplasia of the Temporal Bone.

Changes in vessel wall magnetic resonance contrast-enhancement in a patient with multiple cerebral infarction due to meningovascular neurosyphilis

A 46-year-old man with a history of generalized skin rash following physical contact with possible syphilis infection developed right upper and lower extremity ataxia and right lower extremity paresis. Brain magnetic resonance (MR) imaging revealed multiple areas of acute cerebral infarction mainly within the territories of the right superior cerebellar artery (SCA) and left anterior cerebral artery. The patient was diagnosed with meningovascular neurosyphilis based on positive results on syphilis testing of the serum and cerebrospinal fluid. MR angiography revealed decreased signal intensity in the proximal segment of the right SCA, and gadolinium-enhanced three-dimensional T1-weighted MR images showed an enhancement of the vessel wall in this segment of the artery. Signal intensity in the right SCA showed partial improvement following the completion of intravenous penicillin treatment, and contrast enhancement of the vessel wall disappeared simultaneously with clinical improvement. Alterations in cerebral vessel walls on contrast-enhanced MR imaging in cases of meningovascular neurosyphilis may reflect vascular inflammatory activity.

Can I Diagnose a Vestibular Schwannoma Using Non-Contrast Imaging?


 
 
 
 “I have an adult patient with new-onset unilateral sensorineural hearing loss. I would like to rule out a vestibular schwannoma by performing an MRI exam. However, the patient has a kidney problem that prevents him from receiving contrast material. Can I still diagnose a vestibular schwannoma using a non-contrast imaging study?”
 
 
 
 
 The answer to this question is ... YES! At the current time, gadolinium-enhanced T1-weighted magnetic resonance imaging (MRI) is considered the gold standard for the diagnosis of vestibular schwannomas.1 However, the use of gadolinium-based contrast material may be contraindicated in patients with impaired renal function, in those who have had an allergic reaction to such contrast material, and in patients who are or may be pregnant.
 
 
 
 
 High-resolution T2-weighted MR sequences as a means of visualizing the fluid-filled inner ear and the 7th and 8th cranial nerves in the internal auditory canal and cerebellopontine angle cistern was described in the early 1990’s.2 These steady-state imaging sequences currently include various manufacturer-specific sequences such as CISS (Constructive Interference into Steady State, Siemens), FIESTA-C (Fast Imaging Employing Steady-state Acquisition Cycled Phases, GE), DRIVE (Driven Equilibrium Radiofrequency Reset Pulse, Philips), bFFE (Balanced Fast Field Echo, Philips) and SPACE (Sampling Perfection with Application optimized Contrasts using different flip angle Evolutions, Siemens).3 The submillimeter-resolution images from these sequences can be manipulated on computer-based DICOM imaging software to provide reformatted images in non-orthogonal planes that allow visualization of the entire 8th cranial nerve, from the brainstem to the fundus of the internal auditory canal. In these sequences, cerebrospinal fluid (CSF) displays a high signal intensity that provides an excellent contrasting background to the inherent low signal intensity of the 7th and 8th cranial nerves. (Figure 1)
 
 
 
 
 Similar to the nerves, a vestibular schwannoma will appear as a low-intensity (dark) filling defect that may be nodular, oblong, or ice cream cone-shaped, and located in the internal auditory canal and/or the cerebellopontine angle cistern immediately adjacent to it. (Figure 2)
 
 
 
 
 In evaluating a patient for the presence of a vestibular schwannoma, the current medical literature indicates that high-resolution T2- weighted imaging sequences have a high sensitivity and specificity compared to gadolinium-enhanced T1-weighted imaging. However, T2-weighted imaging alone may not detect inflammatory, infectious or malignant conditions that may also present with sensorineural hearing loss.1 As such, a gadolinium-enhanced T1-weighted sequence will be needed when such conditions are suspected.
 
 
 
 
 Finally, local experience with institutions that perform imaging of the internal auditory canal and cerebellopontine angle shows that not all centers routinely perform the high-resolution T2-weighted imaging sequences. Therefore, it would be prudent to specifically indicate the need for “a T2-weighted imaging sequence with submillimeter- resolution, such as the CISS or FIESTA-C sequence” in the imaging request, in order to ensure the performance of the appropriate imaging strategy.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Segmentation of Vestibular Schwannomas on Postoperative Gadolinium-Enhanced T1-Weighted and Noncontrast T2-Weighted Magnetic Resonance Imaging Using Deep Learning.

Surveillance of postoperative vestibular schwannomas currently relies on manual segmentation and measurement of the tumor by content experts, which is both labor intensive and time consuming. We aimed to develop and validate deep learning models for automatic segmentation of postoperative vestibular schwannomas on gadolinium-enhanced T1-weighted magnetic resonance imaging (GdT1WI) and noncontrast high-resolution T2-weighted magnetic resonance imaging (HRT2WI). A supervised machine learning approach using a U-Net model was applied to segment magnetic resonance imaging images into pixels representing vestibular schwannoma and background pixels. Tertiary care hospital. Our retrospective data set consisted of 122 GdT1WI and 122 HRT2WI studies in 82 postoperative adult patients with a vestibular schwannoma treated with subtotal surgical resection between September 1, 2007, and April 17, 2018. Forty-nine percent of our cohort was female, the mean age at the time of surgery was 49.8 years, and the median time from surgery to follow-up scan was 2.26 years. N/A. Tumor areas were manually segmented in axial images and used as ground truth for training and evaluation of the model. We measured the Dice score of the predicted segmentation results in comparison to manual segmentations from experts to assess the model's accuracy. The GdT1WI model achieved a Dice score of 0.89, and the HRT2WI model achieved a Dice score of 0.85. We demonstrated that postoperative vestibular schwannomas can be accurately segmented on GdT1WI and HRT2WI without human intervention using deep learning. This artificial intelligence technology has the potential to improve the postoperative surveillance and management of patients with vestibular schwannomas.

Magnetic resonance image-based brain tumour segmentation methods: A systematic review.

BackgroundImage segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development.PurposeTo determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation.MethodsWe conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score).Statistical testsWe compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour.ResultsWe found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation.ConclusionU-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.

Evaluation of the malignant potential of gliomas using diffusion-weighted and gadolinium-enhanced magnetic resonance imaging

Objective: This study aimed to determine whether the apparent diffusion coefficients (ADCs) determined by diffusion-weighted imaging (DWI) of magnetic resonance imaging (MRI) could facilitate the malignancy grading of various gliomas. Methods: Sixty patients with a primary cerebral glioma underwent diffusion-weighted and gadolinium-enhanced (Gd) T1-weighted MRI using a 1.5-T MRI scanner. Scoring was performed based on signal intensities on DWI and Gd images. The mean and minimum ADC values were calculated, and Ki-67 staining was performed for each histological sample to evaluate their tumor proliferative potential. Then, the DWI, Gd, and combined scores were analyzed and compared with the Ki-67 staining index and malignant grade. The relationships among the mean and minimum ADC values, Ki-67 staining index, and malignant grade were also evaluated. Results: The minimum ADC was inversely correlated with the Ki-67 staining index, with a low minimum ADC suggestive of tumor malignancy. The qualitative evaluation of the D score of water molecule diffusion on DWI accurately reflected the pathological grades of gliomas, with an effectiveness that was at least as good as the quantitative analysis using the minimum ADC. The diagnostic value of Gd images in determining glioma malignancy grades was inferior to that of DWI. Conclusion: Both DWI and gadolinium-enhanced images of MRI should be considered essential for the diagnosis of tumor malignancy.

Regression and pseudoprogression of pediatric optic pathway glioma in patients treated with proton beam therapy.

We examined regression patterns in pediatric optic pathway gliomas (OPGs) after proton beam therapy (PBT) and evaluated local control and visual outcomes. A total of 42 brain magnetic resonance imaging (MRI) scans from seven consecutive sporadic OPGs that were initially treated with chemotherapy and received PBT between June 2007 and September 2016 at the National Cancer Center, Korea were analyzed. Patients underwent brain MRI regularly before and after PBT. Total tumor, cystic lesion, and solid enhancing lesion area delineation and volume calculations were performed on gadolinium-enhanced T1-weighted MRI using Eclipse version 13, Varian. The median follow-up period after PBT was 70months (range 47-88). The median age at the time of PBT was 7years (range 4-16) and the median duration of chemotherapy before referral to PBT center was 25months (range 3-70). The median time to the greatest increase in cystic volume was 32months (range 12-43) after PBT. Solid enhancing lesion volume gradually decreased throughout the follow-up period. On an individual basis, total volume change was varied. However, on average, it regressed, although at a slower rate than solid enhancing lesion volume did. The local control rate was 85.7% (5-year progression-free survival rate, 80%; 5-year overall survival rate, 100%) and the rate of vision preservation was 71.4% (five of seven patients). The regression patterns in pediatric OPGs after PBT involve significant cystic change. Therefore, total volume is not appropriate for evaluating response. Care by a multidisciplinary team is necessary to manage clinical symptoms related to radiologic changes.

Reliability and agreement of proton density-weighted vs. gadolinium-enhanced T1-weighted MRI in hand osteoarthritis. An OMERACT MRI special interest group reliability exercise

ObjectivesTo compare reliabilities of assessing synovitis in hand osteoarthritis (OA) using Magnetic Resonance Imaging (MRI) with/without gadolinium (Gd). MethodsThree readers scored synovitis on non-enhanced two-dimensional (2D) proton density (PD)-weighted MRI and Gd-enhanced (3D) MRI of hand joints in 20 patients. Inter-reader reliabilities were examined. ResultsReliability was good for Gd-enhanced MRI, but poor for non-enhanced PD-weighted MRI (intraclass correlation coefficient 0.83 and 0.21, respectively). Agreement between the two sequences was poor (weighted kappa 0.18). ConclusionGd-enhanced MRI was more reliable than PD-weighted MRI for assessing synovitis. Gd-enhancement, but also resolution and tissue contrast, might have contributed to this.

The use of cochlear-enhancement imaging to predict hearing preservation following vestibular schwannoma removal.

Hearing preservation is challenging for patients after the removal of large vestibular schwannomas (VSs). Here, using preoperative magnetic resonance (MR) imaging, we investigated the significance of cochlear enhancement (CE) for predicting postoperative hearing preservation. Between January 2014 and December 2019, 34 VS-patients with serviceable hearing underwent tumor-removal surgery using a retrosigmoid approach. The presence or absence of CE using both T2-weighted and gadolinium-enhanced T1-weighted MR images was assessed in VS patients using the pixel-analysis method. Segmented volumetric analyses were also performed using GrowCut 3D slicer software. There were 17 patients (50%) without CE and 17 (50%) with CE. Ten of the 17 non-CE patients (58.8%) had postoperative hearing preservation. In contrast, only 3 of the 17 patients with CE (17.6%) had postoperative hearing preservation. There were no significant tumor-characteristic differences between the two groups. The presence of CE on both the T2-weighted and the gadolinium-enhanced T1-weighted MR images correlated significantly with postoperative hearing outcomes (P=0.032). Only pure-tone averages were significantly different between the two groups (P=0.049). Preoperative serviceable hearing is likely to be preserved after surgery in non-CE VS patients. Preoperative CE assessment using MR imaging may be a useful predictor for postoperative hearing outcomes in VS patients.

GCT-31. DIAGNOSTIC CAPABILITY OF CSF-PLAP ON INTRACRANIAL GERM CELL TUMOR

BACKGROUNDSince the majority of intracranial germ cell tumor(GCT) is sensitive for chemoradiation, biopsy specimens are usually tiny and not enough for accurate pathological diagnosis. To supply complementary diagnostic information, a-fetoprotein or human chorionic gonadotropin-b are important biomarkers. Recently CSF-placental alkaline-phosphatase(PLAP) is also reported as an additional biomarker in intracranial GCT. This study’s purpose is to evaluate the significance of CSF-PLAP.METHODSCSF-PLAP was obtained from the patients with the intraventricular and periventricular tumor before any adjuvant therapy. Definitive diagnoses were made by histopathological information and/or their clinical courses; GCT(germinoma or non-germinomatous GCT(NGGCT)) or other tumors. In GCT, the relationship between CSF-PLAP and tumor reduction volume was evaluated. Tumor volumes were calculated on gadolinium-enhanced T1-weighted magnetic resonance imaging before and after initial chemoradiotherapy.RESULTSBetween 2005 and 2019, 42 patients were studied: 24 with GCT and 18 with others. CSF-PLAP value in patients with GCT was significantly higher than those with others: the Specificity was 88% and the sensitivity was 95% at the cutoff value of 8.0 pg/ml. For GCT patients, CSF-PLAP value tended to be higher in germinoma(n=12, mean 4756 pg/ml), compared to the value in NGGCT(n=7, mean 332 pg/ml), although there was no statistical difference. There was a significant positive correlation between initial CSF-PLAP value and tumor reduction volume.CONCLUSIONCSF-PLAP is a useful tumor marker for GCT differentiating from the other tumors located in intraventricular and periventricular region and CSF-PLAP value might correlate with the volume of germinomatous component of the tumor.

Quantitative metric for assessment of pancreatic ductal adenocarcinoma treatment response in T1-weighted gadolinium-enhanced magnetic resonance imaging.

Background:We theoretically derived a new quantitative metric reflecting the product of T1 signal intensity and contrast media concentration (T1C) using first principles for the signal provided by the gradient echo sequence. This metric can be used with conventional gadolinium contrast-enhanced magnetic resonance imaging (CE-MRI) exams. We used this metric to test our hypothesis that gadolinium enhancement changes with pancreatic ductal adenocarcinoma (PDA) treatment response, and that this metric may differentiate responders from non-responders.Methods:Out of 264 initially identified patients, a final total of 35 patients with PDA were included in a retrospective study of responders (n=24) and non-responders (n=11), which used changes in cancer antigen 19–9 (CA 19–9) and tumor size as reference standards. T1C was computed for the pancreatic mass in the arterial, portal venous, and delayed phases in pre-treatment and post-treatment MRIs. Changes in measurements and correlations with treatment response were assessed by repeated measures analysis of variance and paired t-tests.Results:In the treatment responder group, T1C significantly increased in the arterial, portal venous, and delayed phases (P=7.57e–5, P=3.25e–4, P=1.75e–4). In the non-responder group, T1C did not significantly change in any phase (P>0.58). Post-treatment T1C significantly differed between responders and non-responders (P=0.044) by repeated measures analysis of variance.Conclusions:T1C significantly increases in all phases of CE-MRI in responders to treatment, but does not change in non-responders. T1C correlates with treatment response, can be computed from clinical MRI exams, and may be useful as an additional metric to stratify patients undergoing treatment.

Automated Detection of Vestibular Schwannoma Growth Using a Two-Dimensional U-Net Convolutional Neural Network.

To determine if an automated vestibular schwannoma (VS) segmentation model has comparable performance to using the greatest linear dimension to detect growth. Case-control Study. Patients were selected from an internal database who had an initial gadolinium-enhanced T1-weighted magnetic resonance imaging scan and a follow-up scan captured at least 5 months later. Two observers manually segmented the VS to compute volumes, and one observer's segmentations were used to train a convolutional neural network model to automatically segment the VS and determine the volume. The results of automatic segmentation were compared to the observer whose measurements were not used in model development to measure agreement. We then examined the sensitivity, specificity, and area under the receiver-operating characteristic curve (AUC) to compare automated volumetric growth detection versus using the greatest linear dimension. Growth detection determined by the external observer's measurements served as the gold standard. A total of 65 patients and 130 scans were studied. The automated method of segmentation demonstrated excellent agreement with the observer whose measurements were not used for model development for the initial scan (interclass correlational coefficient [ICC] = 0.995; 95% confidence interval [CI]: 0.991-0.997) and follow-up scan (ICC = 0.960; 95% CI: 0.935-0.975). The automated method of segmentation demonstrated increased sensitivity (72.2% vs. 63.9%), specificity (79.3% vs. 69.0%), and AUC (0.822 vs. 0.701) compared to using the greatest linear dimension for growth detection. In detecting VS growth, a convolutional neural network model outperformed using the greatest linear dimension, demonstrating a potential application of artificial intelligence methods to VS surveillance. 4 Laryngoscope, 131:E619-E624, 2021.

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI.

Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural similarities to normal structures (e.g., vasculature).We propose a BM-detection framework using a single-sequence gadolinium-enhanced T1-weighted 3D MRI dataset. The framework focuses on the detection of smaller (<15mm) BM lesions and consists of: (1) candidate-selection stage, using Laplacian of Gaussian approach for highlighting parts of an MRI volume holding higher BM occurrence probabilities, and (2) detection stage that iteratively processes cropped region-of-interest volumes centered by candidates using a custom-built 3D convolutional neural network ("CropNet"). Data is augmented extensively during training via a pipeline consisting of random gamma correction and elastic deformation stages; the framework thereby maintains its invariance for a plausible range of BM shape and intensity representations.This approach is tested using five-fold cross-validation on 217 datasets from 158 patients, with training and testing groups randomized per patient to eliminate learning bias. The BM database included lesions with a mean diameter of ∼5.4mm and a mean volume of ∼160mm3. For 90% BM-detection sensitivity, the framework produced on average 9.12 false-positive BM detections per patient (standard deviation of 3.49); for 85% sensitivity, the average number of false-positives declined to 5.85. Comparative analysis showed that the framework produces comparable BM-detection accuracy with the state-of-art approaches validated for significantly larger lesions.

Utilization of Novel High-Resolution, MRI-Based Vascular Imaging Modality for Preoperative Stereoelectroencephalography Planning in Children: A Technical Note

Introduction: Stereoelectroencephalography (SEEG) is a powerful intracranial diagnostic tool that requires accurate imaging for proper electrode trajectory planning to ensure efficacy and maximize patient safety. Computed tomography (CT) angiography and digital subtraction angiography are commonly used, but recent developments in magnetic resonance angiography allow for high-resolution vascular visualization without added risks of radiation. We report on the accuracy of electrode placement under robotic assistance planning utilizing a novel high-resolution magnetic resonance imaging (MRI)-based imaging modality. Methods: Sixteen pediatric patients between February 2014 and October 2017 underwent SEEG exploration for epileptogenic zone localization. A gadolinium-enhanced 3D T1-weighted spoiled gradient recalled echo sequence with minimum echo time and repetition time was applied for background parenchymal suppression and vascular enhancement. Electrode placement accuracy was determined by analyzing postoperative CT scans laid over preoperative virtual electrode trajectory paths. Entry point, target point, and closest vessel intersection were measured. Results: For any intersection along the trajectory path, 57 intersected vessels were measured. The mean diameter of an intersected vessel was 1.0343 ± 0.1721 mm, and 21.05% of intersections involved superficial vessels. There were 157 overall intersection + near-miss events. The mean diameter for an involved vessel was 1.0236 ± 0.0928 mm, and superficial vessels were involved in 20.13%. Looking only at final electrode target, 3 intersection events were observed. The mean diameter of an intersected vessel was 1.0125 ± 0.2227 mm. For intersection + near-miss events, 24 were measured. An involved vessel’s mean diameter was 1.1028 ± 0.2634 mm. For non-entry point intersections, 45 intersected vessels were measured. The mean diameter for intersected vessels was 0.9526 ± 0.0689 mm. For non-entry point intersections + near misses, 126 events were observed. The mean diameter for involved vessels was 0.9826 ± 0.1008 mm. Conclusion: We believe this novel sequence allows better identification of superficial and deeper subcortical vessels compared to conventional T1-weighted gadolinium-enhanced MRI.

Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies.

BackgroundAccurate assessments of patient response to therapy are a critical component of personalized medicine. In glioblastoma (GBM), the most aggressive form of brain cancer, tumor growth dynamics are heterogenous across patients, complicating assessment of treatment response. This study aimed to analyze days gained (DG), a burgeoning model-based dynamic metric, for response assessment in patients with recurrent GBM who received bevacizumab-based therapies.MethodsDG response scores were calculated using volumetric tumor segmentations for patients receiving bevacizumab with and without concurrent cytotoxic therapy (N = 62). Kaplan–Meier and Cox proportional hazards analyses were implemented to examine DG prognostic relationship to overall (OS) and progression-free survival (PFS) from the onset of treatment for recurrent GBM.ResultsIn patients receiving concurrent bevacizumab and cytotoxic therapy, Kaplan–Meier analysis showed significant differences in OS and PFS at DG cutoffs consistent with previously identified values from newly diagnosed GBM using T1-weighted gadolinium-enhanced magnetic resonance imaging (T1Gd). DG scores for bevacizumab monotherapy patients only approached significance for PFS. Cox regression showed that increases of 25 DG on T1Gd imaging were significantly associated with a 12.5% reduction in OS hazard for concurrent therapy patients and a 4.4% reduction in PFS hazard for bevacizumab monotherapy patients.ConclusionDG has significant meaning in recurrent therapy as a metric of treatment response, even in the context of anti-angiogenic therapies. This provides further evidence supporting the use of DG as an adjunct response metric that quantitatively connects treatment response and clinical outcomes.

Aspergillus terreus spondylodiscitis following an abdominal stab wound: a case report

BackgroundAspergillus terreus, a saprophytic fungus, is recognized as an emerging pathogen in various infections in humans. However, bone and joint involvement is uncommon. To the best of our knowledge, only seven cases of spondylodiscitis caused by Aspergillus terreus have been reported previously in humans. We report a case of a patient with Aspergillus terreus spondylodiscitis following an abdominal stab wound.Case presentationA 74-year-old Japanese man with no particular medical history fell from a ladder and sustained a left abdominal stab wound from an L-shaped metal peg. Computed tomography showed the trace of the L-shaped metal peg from the left abdomen to the left rib and left kidney. The scan also showed an anterolateral bone avulsion of the left side of the T12 vertebral body, as well as fractures of the L1 left transverse process and the left 10th–12th ribs. He was hospitalized and treated with conservative therapy for 6 weeks. He was readmitted to the hospital with complaints of sudden back pain, numbness of both legs, and inability to walk 13 weeks after the fall. Magnetic resonance imaging findings were typical of spondylodiscitis. Gadolinium-enhanced T1-weighted magnetic resonance imaging indicated increased signal intensity at T11–T12 vertebral bodies and severe cord compression and epidural abscess at T11–T12 associated with infiltration of soft paravertebral tissues. On the seventh day after admission, he underwent partial laminectomy at T11 and posterior fusion at T9 to L2. The result of his blood culture was negative, but Aspergillus terreus was isolated from the material of T11–T12 intervertebral disc and vertebral bodies. His Aspergillus antigen was positive in a blood examination. Histological examination showed chronic suppurative osteomyelitis. On the 35th day after admission, he underwent anterior fusion at T11 and T12 with a rib bone graft. For 5 months, voriconazole was administered, and he wore a rigid corset. Posterior partial laminectomy at T11 and anterior fusion at T11 and T12 resulted in a good clinical course. The patient’s neurological dysfunction was completely recovered, and his back pain disappeared. Two years after the operation, computed tomography was performed and showed bone fusion at T11 and T12. Magnetic resonance imaging revealed no evidence of increased signal intensity at T11–T12 vertebral bodies and severe cord compression and epidural abscess at T11–T12.ConclusionsTo our knowledge, this is the first report of spondylodiscitis caused by Aspergillus terreus after an abdominal penetrating injury. The histological finding of chronic suppurative osteomyelitis and the radiological findings strongly suggested direct inoculation of Aspergillus terreus.

Utility of Noncontrast Magnetic Resonance Imaging for Detection of Recurrent Vestibular Schwannoma.

Gadolinium-enhanced T1-weighted magnetic resonance imaging (MRI) (T1WI) is the conventional imaging technique of choice to detect vestibular schwannoma (VS) recurrence or regrowth, despite suboptimal specificity secondary to enhancing postoperative changes. Furthermore, recent concerns regarding the accumulation of gadolinium in body tissues have led for a call to reduce the number of contrast-enhanced examinations. The objective of the current study is to evaluate the diagnostic accuracy of high-resolution noncontrast (three-dimensional [3D] T2) MRI relative to gadolinium-enhanced T1WI in the detection of VS recurrence after resection. Following Institutional Review Board approval, 13 consecutive postoperative patients with VS recurrence or regrowth were identified from a prospectively maintained clinical database in which recurrence was determined by progression on serial postoperative MRI examination. Three blinded neuroradiologists retrospectively evaluated a total of 41 postoperative MRI examinations from these patients using only gadolinium-enhanced T1WI and 3D T2 MRI for recurrence or regrowth. Interobserver agreement, differences in detection between the two sequences, and the sensitivity and specificity of 3D T2 MRI were assessed. Fifteen of the 41 postoperative MRIs demonstrated progression, as determined by examiner consensus on the gadolinium-enhanced T1WI. Agreement, measured using Krippendorff's alpha, was 0.82 for the 3D T2 images and 0.83 for the contrast-enhanced T1WI. All the three examiners demonstrated no difference in the detection of progression between the two sequences (McNemar's test p values 0.69 for examiner 1, 0.63 for examiner 2, and 0.99 for examiner 3). The sensitivity of 3D T2 was 0.78 (CI 0.60-0.96), while the specificity was 0.94 (CI 0.86-1.00). Noncontrast high-resolution 3D T2 MRI seems sufficient to assess for recurrence or regrowth after VS resection. The results of this study have implications for reducing cost, time, and adverse events associated with gadolinium administration in this population requiring serial follow-up examinations. These promising, yet preliminary findings warrant confirmation with a larger prospective cohort of patients.