iSPAN: Explainable prediction of outcomes post thrombectomy with Machine Learning.

PURPOSE: This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy. MATERIALS AND METHODS: This retrospective study included all patients aged over 18 years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020 with external validation. The primary outcome was day 90 mRS ≥3. Existing clinical scores (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinical score (iSPAN) was derived by adding an optimised weighting of the most important ML features to the SPAN. RESULTS: 812 patients were initially included (397 female, average age 73), 63 for external validation. The best performing clinical score and ML model were SPAN and XGB (sensitivity, specificity and accuracy 0.290, 0.967, 0.628 and 0.693, 0.783, 0.738 respectively). A significant difference was found overall and our XGB model was more accurate than SPAN (p < 0.0018). The most important features were Age, mTICI and total number of passes. The addition of 11 points for mTICI of ≤2B and 3 points for ≥3 passes to the SPAN achieved the best accuracy and was used to create the iSPAN. iSPAN was not significantly less accurate than our XGB model (p > 0.5). In the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67) respectively. CONCLUSION: iSPAN incorporates machine-derived features to achieve better predictions compared to existing clinical scores. It is not inferior to our XGB model and is externally generalisable.

A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.

BACKGROUND AND PURPOSE: MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS. MATERIALS AND METHODS: This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model. RESULTS: The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications. CONCLUSIONS: Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.

Marginal corneal infiltrates as an ocular manifestation of acute generalized exanthematous pustulosis.

Purpose: To report a case of keratoconjunctivitis with marginal corneal infiltrates in a patient with acute generalized exanthematous pustulosis (AGEP) secondary to trimethoprim-sulfamethoxazole. Observations: A 63-year-old female presented with a diffuse pustular skin rash and bilateral keratoconjunctivitis with marginal corneal infiltrates. Skin biopsy led to the diagnosis of AGEP secondary to trimethoprim-sulfamethoxazole use. Treatment of the ocular findings with topical corticosteroids and lubrication led to near-full resolution after two weeks. Conclusions and Importance: To the best of our knowledge, this is the first reported association between AGEP and keratoconjunctivitis with marginal corneal infiltrates. A hypersensitivity reaction to a foreign antigen is implicated in the pathogenesis of both AGEP and sterile marginal infiltrates, and we suggest that the patient's underlying hypersensitivity process associated with AGEP accounted for the ocular findings.

Paving a Path to Gender Parity: Recent Trends in Participation of Women in an Academic Surgery Society (Society for Surgery of the Alimentary Tract).

INTRODUCTION: The proportion of women surgeons is increasing, although women in surgical leadership and research has not kept pace. The Society for Surgery of the Alimentary Tract (SSAT) pledged its commitment to diversity and inclusion in 2016. Our study sought to evaluate the temporal trend of gender representation in leadership, speakership, and research at SSAT. METHODS: Publicly available SSAT meeting programs from 2010 to 2022 were reviewed to assess gender proportions within leadership positions (officers and committee chairs); invited speakerships, multidisciplinary symposia, and committee panel session moderators and speakers; and contributions to scientific sessions (moderator, first author and senior author). Verified individual professional profiles were analyzed to categorize gender as woman, man, or unavailable. Descriptive and trend analyses using linear regression and chi-squared testing were performed. RESULTS: A total of 5506 individuals were reviewed; 1178 (21.4%) were identified as women and 4328 (78.6%) as men or did not have available data. The absolute proportion of total female participation increased by 1.05% per year (R2=0.82). There was a statistically significant difference in the total proportion of women participation before and after 2016 (18.5% vs. 27.1%, p<0.01). Increases in the proportion of women were demonstrated in leadership, invited speakerships, multidisciplinary symposia, committee panel sessions, research session moderators, and abstract first authors. The proportion of women senior authors remained stagnant. CONCLUSION: Though this upward trajectory in SSAT women participation is encouraging, current trends predict that gender parity will not be reached until 2044.

Delayed Iatrogenic Bladder Rupture Diagnosed by POCUS in the Emergency Department.

Bladder rupture is an uncommon injury that leads to significant morbidity and mortality. Though occurring mostly due to trauma, this life-threatening pathology may also occur spontaneously or after a procedure such as transurethral resection of bladder tumor (TURBT). Computed tomography (CT) cystography is the standard imaging modality for diagnosis. However, this test is unlikely to be ordered in a patient with undifferentiated abdominal pain unless there is specific suspicion for this diagnosis. In our emergency department, a 48 year-old male with history of bladder cancer and TURBT two weeks prior to arrival presented with severe abdominal pain and difficulty urinating for 3 days. Point of care ultrasound (POCUS) revealed an irregularly shaped bladder, likely site of bladder rupture, and large amount of abdominal free fluid with sediment. These findings prompted an expedited diagnostic CT scan with cystography. Emergent exploratory laparotomy ultimately confirmed a small bladder defect with 2.5 L of urinary ascites. The diagnosis of non-traumatic bladder rupture can be overlooked in patients presenting with a peritonitic abdominen. The typically ordered test for such patients is standard CT, which carries a high false-negative rate for bladder rupture. This case highlights the utility of POCUS in facilitating a rapid diagnosis.

DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy.

OBJECTIVES: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion. METHODS: All patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy. RESULTS: In total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n = 194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI < 3 with an AUC of 0.71. CONCLUSIONS: Our model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention). KEY POINTS: • DEEP MOVEMENT represents a novel application of a model applied to acute stroke imaging to handle two types of temporal complexity, dynamic video and pre and post intervention. • The model takes as an input digital subtraction angiograms of the anterior cerebral circulation and classifies according to (1) the presence or absence of large vessel occlusion, (2) the location of the occlusion, and (3) the efficacy of thrombectomy. • Potential clinical utility lies in providing decision support via rapid interpretation (pre thrombectomy) and automated objective gradation of thrombectomy outcomes (post thrombectomy).

Corneal Pigment Opacity From Face-Down Positioning After Pars Plana Vitrectomy, Treated With Descemet Stripping Only Technique.

PURPOSE: The purpose of this study was to report a case of corneal opacity resulting from pigment deposition after face-down positioning, which was treated with Descemet stripping only (DSO) to enable concurrent pars plana vitrectomy (PPV) for retinal detachment repair. METHODS: A 79-year-old man with a history of Fuchs endothelial dystrophy and retinal detachment presented for the repair of recurrent retinal detachment and evaluation of a central corneal opacity. RESULTS: The patient was found to have significant corneal endothelial pigment deposition obscuring the view to the fundus. A repeat macula-involving retinal detachment was visualized on limited fundoscopic examination and confirmed using ultrasonography. The patient subsequently underwent combination scleral buckle, DSO, and PPV. DSO achieved corneal clarity for the entire duration of the PPV and allowed for the necessary postoperative face-down positioning. Immunohistochemistry of the corneal specimen revealed deposition of retinal pigment epithelium as the origin of the pigment opacity. The corneal edema cleared at postoperative month 4, and the retina remained attached, resulting in an improvement of visual acuity from counting fingers preoperatively to 20/70. DISCUSSION: This is, to the best of our knowledge, the first case describing the formation of a corneal endothelial opacity because of retinal pigment epithelium deposition associated with face-down positioning after PPV for retinal detachment. DSO is a minimally invasive, viable alternative to endothelial keratoplasty or temporary keratoprosthesis placement for the clearance of focal corneal endothelial opacities for PPV.

Attention-guided deep learning for gestational age prediction using fetal brain MRI.

Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R2 score of 0.945, mean absolute error of 6.7 days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39 weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R2 scores of 0.81-0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.

Immunization-Associated Corneal Transplantation Rejection: A Review.

PURPOSE: The purpose of this review was to examine and characterize the available literature regarding immunization-associated corneal graft rejection. METHODS: A Literature search was conducted using PubMed keywords relevant to corneal transplantation, graft rejection, and immunization to find relevant publications through July 2021. Nine studies were included in this review. Data including patient demographics, type of transplant, chronology of disease, type of immunization, treatment, and outcomes were evaluated. RESULTS: Twenty-three cases of corneal graft rejection associated temporally with immunizations have been described in the literature. Most of these patients were female, and most commonly had received the influenza vaccine before the rejection episode. Most episodes resulted in graft preservation with intensive corticosteroid therapy. CONCLUSIONS: Immunization-associated corneal graft rejection is a rare but likely underreported phenomenon. Patients and surgeons should be aware of this possible risk, although the evidence is inconclusive. Conclusions are limited because of the small sample size and the retrospective nature of all existing literature on this subject. Surgeons should be encouraged to document and report these episodes.

Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.

BACKGROUND: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE: To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS: We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS: A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P = .001). CONCLUSION: Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID-) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.

Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.

PURPOSE: To investigate the efficacy of radiomics in diagnosing patients with coronavirus disease (COVID-19) and other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19. METHODS: Between 18 January 2020 and 20 May 2020, 110 SARS-CoV-2 positive and 108 SARS-CoV-2 negative patients were retrospectively recruited from three hospitals based on the inclusion criteria. Manual segmentation of pneumonia lesions on CT scans was performed by four radiologists. The latest version of Pyradiomics was used for feature extraction. Four classifiers (linear classifier, k-nearest neighbour, least absolute shrinkage and selection operator [LASSO], and random forest) were used to differentiate SARS-CoV-2 positive and SARS-CoV-2 negative patients. Comparison of the performance of the classifiers and radiologists was evaluated by ROC curve and Kappa score. RESULTS: We manually segmented 16,053 CT slices, comprising 32,625 pneumonia lesions, from the CT scans of all patients. Using Pyradiomics, 120 radiomic features were extracted from each image. The key radiomic features screened by different classifiers varied and lead to significant differences in classification accuracy. The LASSO achieved the best performance (sensitivity: 72.2%, specificity: 75.1%, and AUC: 0.81) on the external validation dataset and attained excellent agreement (Kappa score: 0.89) with radiologists (average sensitivity: 75.6%, specificity: 78.2%, and AUC: 0.81). All classifiers indicated that "Original_Firstorder_RootMeanSquared" and "Original_Firstorder_Uniformity" were significant features for this task. CONCLUSIONS: We identified radiomic features that were significantly associated with the classification of COVID-19 pneumonia using multiple classifiers. The quantifiable interpretation of the differences in features between the two groups extends our understanding of CT imaging characteristics of COVID-19 pneumonia.

Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

PURPOSE: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework. METHODS: A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia. RESULTS: 120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model. CONCLUSIONS: We uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance.

Simultaneous time of flight-MRA and T2* imaging for cerebrovascular MRI.

PURPOSE: 3D multi-echo gradient-recalled echo (ME-GRE) can simultaneously generate time-of-flight magnetic resonance angiography (pTOF) in addition to T2*-based susceptibility-weighted images (SWI). We assessed the clinical performance of pTOF generated from a 3D ME-GRE acquisition compared with conventional TOF-MRA (cTOF). METHODS: Eighty consecutive children were retrospectively identified who obtained 3D ME-GRE alongside cTOF. Two blinded readers independently assessed pTOF derived from 3D ME-GRE and compared them with cTOF. A 5-point Likert scale was used to rank lesion conspicuity and to assess for diagnostic confidence. RESULTS: Across 80 pediatric neurovascular pathologies, a similar number of lesions were reported on pTOF and cTOF (43-40%, respectively, p > 0.05). Rating of lesion conspicuity was higher with cTOF (4.5 ± 1.0) as compared with pTOF (4.0 ± 0.7), but this was not significantly different (p = 0.06). Diagnostic confidence was rated higher with cTOF (4.8 ± 0.5) than that of pTOF (3.7 ± 0.6; p < 0.001). Overall, the inter-rater agreement between two readers for lesion count on pTOF was classified as almost perfect (κ = 0.98, 96% CI 0.8-1.0). CONCLUSIONS: In this study, TOF-MRA simultaneously generated in addition to SWI from 3D MR-GRE can serve as a diagnostic adjunct, particularly for proximal vessel disease and when conventional TOF-MRA images are absent.

Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.

OBJECTIVE: Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals. METHODS: The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software. RESULTS: Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan). CONCLUSIONS: The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.

Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs.

PURPOSE: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs. MATERIALS AND METHODS: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set. RESULTS: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction. CONCLUSIONS: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application.

Reduced field of view echo-planar imaging diffusion tensor MRI for pediatric spinal tumors.

OBJECTIVE: Spine MRI is a diagnostic modality for evaluating pediatric CNS tumors. Applying diffusion-weighted MRI (DWI) or diffusion tensor imaging (DTI) to the spine poses challenges due to intrinsic spinal anatomy that exacerbates various image-related artifacts, such as signal dropouts or pileups, geometrical distortions, and incomplete fat suppression. The zonal oblique multislice (ZOOM)-echo-planar imaging (EPI) technique reduces geometric distortion and image blurring by reducing the field of view (FOV) without signal aliasing into the FOV. The authors hypothesized that the ZOOM-EPI method for spine DTI in concert with conventional spinal MRI is an efficient method for augmenting the evaluation of pediatric spinal tumors. METHODS: Thirty-eight consecutive patients (mean age 8 years) who underwent ZOOM-EPI spine DTI for CNS tumor workup were retrospectively identified. Patients underwent conventional spine MRI and ZOOM-EPI DTI spine MRI. Two blinded radiologists independently reviewed two sets of randomized images: conventional spine MRI without ZOOM-EPI DTI, and conventional spine MRI with ZOOM-EPI DTI. For both image sets, the reviewers scored the findings based on lesion conspicuity and diagnostic confidence using a 5-point Likert scale. The reviewers also recorded presence of tumors. Quantitative apparent diffusion coefficient (ADC) measurements of various spinal tumors were extracted. Tractography was performed in a subset of patients undergoing presurgical evaluation. RESULTS: Sixteen patients demonstrated spinal tumor lesions. The readers were in moderate agreement (kappa = 0.61, 95% CI 0.30-0.91). The mean scores for conventional MRI and combined conventional MRI and DTI were as follows, respectively: 3.0 and 4.0 for lesion conspicuity (p = 0.0039), and 2.8 and 3.9 for diagnostic confidence (p < 0.001). ZOOM-EPI DTI identified new lesions in 3 patients. In 3 patients, tractography used for neurosurgical planning showed characteristic fiber tract projections. The mean weighted ADCs of low- and high-grade tumors were 1201 × 10-6 and 865 × 10-6 mm2/sec (p = 0.002), respectively; the mean minimum weighted ADCs were 823 × 10-6 and 474 × 10-6 mm2/sec (p = 0.0003), respectively. CONCLUSIONS: Diffusion MRI with ZOOM-EPI can improve the detection of spinal lesions while providing quantitative diffusion information that helps distinguish low- from high-grade tumors. By adding a 2-minute DTI scan, quantitative diffusion information and tract profiles can reliably be obtained and serve as a useful adjunct to presurgical planning for pediatric spinal tumors.

Laboratory Animal Bite Anaphylaxis: A National Survey: Part 1: Case Series and Review of the Literature.

OBJECTIVE: This study documents previously unreported cases of laboratory animal bite anaphylaxis in animal laboratory facilities in the United States. METHODS: An online survey was e-mailed to designated institutional officials at laboratory animal facilities identified by the National Institutes of Health Office of Laboratory Animal Welfare. RESULTS: One hundred ninety eight organizations responded and 15 organizations indicated that workers had experienced anaphylaxis following an animal bite. Case report forms were completed by nine of these institutions for 14 cases, 13 for rodent bites, and one involving a needlestick from a horse. In half of the cases involving rodents, there was no prior history of animal allergy. All workers had uncomplicated recoveries. Treatment, testing, and work restrictions varied across cases. CONCLUSIONS: While uncommon, anaphylaxis from laboratory animal bites occurs more frequently than suggested by the literature.