Background: The use of ultrasonography to diagnose and manage peripheral nerve injury is not routinely performed, but is an advantageous alternative to magnetic resonance imaging (MRI) in the pediatric population. Case Description: The authors report a case of a toddler-aged female who sustained a supracondylar fracture and subsequent median and ulnar nerve injuries. All preoperative and postoperative imaging was performed through high-resolution ultrasound as opposed to MRI. Starting at 6 months post-nerve repair and with 18 months of follow-up, the patient exhibited substantial improvement in motor strength and sensory function. This case demonstrated a successful outcome while providing an imaging alternative that is portable, relatively low-cost, lacks ionizing radiation, provides additional information on vascular integrity, and obviates the need for general anesthetic such as MRI. Conclusion: The authors conclude that the use of ultrasonography to diagnose and manage traumatic peripheral nerve injury is advantageous, particularly in the pediatric population.

BACKGROUND Uterine leiomyosarcoma is a rare, extremely aggressive tumor with a high rate of metastasis. Five-year survival for individuals with metastatic disease is only 10%–15%. Metastases to the brain are exceptionally rare and are associated with poor survival. OBSERVATIONS The authors report a case of uterine leiomyosarcoma that metastasized to the brain in a 51-year-old woman. A single lesion on magnetic resonance imaging was discovered in the right posterior temporo-occipital region 44 months after resection of the primary uterine tumor. The patient underwent a right occipital craniotomy with gross-total resection of the tumor and is receiving adjuvant stereotactic radiosurgery and chemotherapy with gemcitabine and docetaxel. At 8 months postresection, the patient remains alive and asymptomatic with no sign of recurrence. A literature review of prior reported cases was conducted to analyze patterns of approach to patient treatment and survival. LESSONS The authors found an apparent survival benefit in patients receiving adjuvant radiation therapy.

The automated interpretation of spinal imaging using machine learning has emerged as a promising method for standardizing the assessment and diagnosis of numerous spinal column pathologies. While magnetic resonance images (MRIs) of the lumbar spine have been extensively studied in this context, the cervical spine remains vastly understudied. Our objective was to develop a method for automatically delineating cervical spinal cord and neural foramina on axial MRIs using machine learning. In this study, we train a state-of-the-art algorithm, namely a multiresolution ensemble of deep U-Nets, to delineate cervical spinal cord and neural foramina on 50 axial T2-weighted MRI-series segmented by a team of expert clinicians. We then evaluate algorithm performance against two independent human raters using 50 separate MRI-series. Dice coefficients, Hausdorff coefficients, and average surface distances (ASDs) were computed for this final set between the algorithm and each rater, and between raters, in order to evaluate algorithm performance for each segmentation task. The resulting cervical cord Dice coefficients were 0.76 (auto vs human, average) and 0.87 (human vs human), and the cervical foramina Dice coefficients were 0.57 (auto vs human, average) and 0.59 (human vs human). Hausdorff coefficients and ASDs reflected similar results. We conclude that the algorithm achieved a higher degree of consistency with human raters for cervical cord than for cervical foramina, and that cervical foramina are challenging to segment accurately for both humans and machine. Further technical development in machine learning is necessary to accurately segment the highly anatomically variable neural foramina of the human spine.

ObjectiveTo assess volumetric changes in the spinal cord at the cervicomedullary junction, diameter of the cervicomedullary cord, and width of the brainstem following posterior fossa decompression (PFD).MethodsA retrospective analysis of adult patients with Chiari malformation who underwent PFD was performed. Segmentations were done on clinical quality T2-weighted cervical magnetic resonance images obtained before and after decompression using ITK-SNAP. Volumes of neural tissue within the cervicomedullary junction were evaluated from 10 mm cranial to the medullary beak to the cervical spinal cord at the level of the caudal endplate of the second cervical vertebra. The diameter of the cervicomedullary cord was calculated perpendicular to the spinal cord. The width of the brainstem was measured perpendicular to the clivus at the level of the basion.ResultsTwenty adult patients, a mean age of 49.55 years, were included. The cervical cord increased in volume by 13 mm3 to 338 mm3, with an average increase of 155 mm3 (P-value of 0.00002). The diameter of the cervicomedullary cord increased 10.30% 7 mm superior to the beak (P-value of 0.00074), 11.49% at the apex of the beak (P-value of 0.00082), 8.29% 7 mm inferior to the beak (P-value of 0.00075), and the brainstem increased 14.46% perpendicular to the clivus (P-value of 0.00109). The spinal cord at the inferior aspect of the C3 vertebra changed insignificantly (P-value of 0.10580).ConclusionThe volume of the cervical cord at the cervical-medullary junction, width of the cervicomedullary cord, and diameter of the brainstem increase following PFD.

Machine learning algorithms tend to perform better within the setting wherein they are trained, a phenomenon known as the domain effect. Deep learning-based medical image segmentation algorithms are often trained using data acquired from specific scanners; however, these algorithms are expected to accurately segment anatomy in images acquired from scanners different from the ones used to obtain training images for such algorithms. In this work, we present evidence of a scanner and magnet strength specific domain effect for a deep-U-Net trained to segment spinal canals on axial MR images. The trained network performs better on new data from the same scanner and worse on data from other scanners, demonstrating a scanner-specific domain effect. We then construct ensembles of the U-Nets, in which each U-Net in the ensemble differs from others only in initialization. Finally, we demonstrate that these UNet ensembles reduce the differential between in-domain and out-of-domain performance, thereby mitigating the domain effect associated with single U-Nets. Our study evidences the importance of developing software robust to scanner-specific domain effects to handle scanner bias in Deep Learning.

BACKGROUND:Performing emergent spinal surgery within 6 months of percutaneous placement of drug-eluting coronary stent (DES) is complex. The risks of spinal bleeding in a "closed space" must be compared with the risks of stent thrombosis or major cardiac event from dual antiplatelet therapy (DAPT) interruption.METHODS:Eighty relevant English language papers published in PubMed were reviewed in detail.RESULTS:Variables considered regarding surgery in patients on DAPT for DES included: (1) surgical indications, (2) percutaneous cardiac intervention (PCI) type (balloon angioplasty vs. stenting), (3) stent type (drug-eluting vs. balloon mechanical stent), and (4) PCI to noncardiac surgery interval. The highest complication rate was observed within 6 weeks of stent placement, this corresponds to the endothelialization phase. Few studies document how to manage patients with critical spinal disease warranting operative intervention within 6 months of their PCI for DES placement.CONCLUSION:The treatment of patients requiring urgent or emergent spinal surgery within 6 months of undergoing a PCI for DES placement is challenging. As early interruption of DAPT may have catastrophic consequences, we hereby proposed a novel protocol involving stopping clopidogrel 5 days before and aspirin 3 days before spinal surgery, and bridging the interval with a reversible P2Y12 inhibitor until surgery. Moreover, postoperatively, aspirin could be started on postoperative day 1 and clopidogrel on day 2. Nevertheless, this treatment strategy may not be appropriate for all patients, and multidisciplinary approval of perioperative antiplatelet therapy management protocols is essential.

Manual delineation of anatomy on existing images is the basis of developing deep learning algorithms for medical image segmentation. However, manual segmentation is tedious. It is also expensive because clinician effort is necessary to ensure correctness of delineation. Consequently most algorithm development is based on a tiny fraction of the vast amount of imaging data collected at a medical center. Thus, selection of a subset of images from hospital databases for manual delineation - so that algorithms trained on such data are accurate and tolerant to variation, becomes an important challenge. We address this challenge using a novel algorithm. The proposed algorithm named 'Eigenrank by Committee' (EBC) first computes the degree of disagreement between segmentations generated by each DL model in a committee. Then, it iteratively adds to the committee, a DL model trained on cases where the disagreement is maximal. The disagreement between segmentations is quantified by the maximum eigenvalue of a Dice coefficient disagreement matrix a measure closely related to the Von Neumann entropy. We use EBC for selecting data subsets for manual labeling from a larger database of spinal canal segmentations as well as intervertebral disk segmentations. U-Nets trained on these subsets are used to generate segmentations on the remaining data. Similar sized data subsets are also randomly sampled from the respective databases, and U-Nets are trained on these random subsets as well. We found that U-Nets trained using data subsets selected by EBC, generate segmentations with higher average Dice coefficients on the rest of the database than U-Nets trained using random sampling (p < 0.05 using t-tests comparing averages). Furthermore, U-Nets trained using data subsets selected by EBC generate segmentations with a distribution of Dice coefficients that demonstrate significantly (p < 0.05 using Bartlett's test) lower variance in comparison to U-Nets trained using random sampling for all datasets. We believe that this lower variance indicates that U-Nets trained with EBC are more robust than U-Nets trained with random sampling. (C) 2020 Elsevier B.V. All rights reserved.

Abstract BACKGROUND Up to 15% of previously irradiated metastatic spine tumors will progress. Re-irradiation of these tumors poses a significant risk of exceeding the radiation tolerance to the spinal cord. High-dose rate (HDR) brachytherapy is a treatment alternative. OBJECTIVE To develop a novel HDR spine brachytherapy technique using an intraoperative computed tomography-guided navigation (iCT navigation). METHODS Patients with progressive metastatic spine tumors were included in the study. HDR brachytherapy catheters were placed under iCT navigation. CT-based planning with magnetic resonance imaging fusion was performed to ensure conformal dose delivery to the target while sparing normal tissue, including the spinal cord. Patients received single fraction radiation treatment. RESULTS Five patients with thoracolumbar tumors were treated with HDR brachytherapy. Four patients previously received radiotherapy to the same spinal level. Preimplant plans demonstrated median clinical target volume (CTV) D90 of 116.5% (110.8%-147.7%), V100 of 95.7% (95.5%-99.6%), and Dmax of 8.08 Gy (7.65-9.8 Gy) to the spinal cord/cauda equina. Postimplant plans provided median CTV D90 of 113.8% (93.6%-120.1%), V100 of 95.9% (87%-99%), and Dmax of 9.48 Gy (6.5-10.3 Gy) to cord/cauda equina. Patients who presented with back pain (n = 3) noted symptomatic improvement at a median follow-up of 22 d after treatment. Four patients demonstrated local tumor control of spinal metastatic tumor at a median follow-up of 92 d after treatment. One patient demonstrated radiographic evidence of local tumor progression 2.7 mo after treatment. CONCLUSION HDR spine brachytherapy with iCT navigation is a promising treatment alternative to induce local tumor control and reduce pain symptoms associated with metastatic spine disease.

Translation of fully automated deep learning based medical image segmentation technologies to clinical workflows face two main algorithmic challenges. The first, is the collection and archival of large quantities of manually annotated ground truth data for both training and validation. The second is the relative inability of the majority of deep learning based segmentation techniques to alert physicians to a likely segmentation failure. Here we propose a novel algorithm, named `Eigenrank' which addresses both of these challenges. Eigenrank can select for manual labeling, a subset of medical images from a large database, such that a U-Net trained on this subset is superior to one trained on a randomly selected subset of the same size. Eigenrank can also be used to pick out, cases in a large database, where deep learning segmentation will fail. We present our algorithm, followed by results and a discussion of how Eigenrank exploits the Von Neumann information to perform both data subset selection and failure prediction for medical image segmentation using deep learning.

BACKGROUND AND PURPOSE:Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics. MATERIALS AND METHODS:Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model. RESULTS:We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height (P < .05) but not with age or sex. CONCLUSIONS:Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.

OBJECTIVE:There have been numerous studies demonstrating increased pain and disability when patients' spinopelvic parameters fall outside of certain accepted ranges. However, these values were established based on patients suffering from spinal deformities. It remains unknown how these parameters change over a lifetime in asymptomatic individuals. The goal of this study was to define a range of spinopelvic parameters from asymptomatic individuals. METHODS:Sagittal scoliosis radiographs of 210 asymptomatic patients were evaluated. All measurements were reviewed by 2 trained observers, supervised by a trained clinician. The following parameters and relationships were measured or calculated: cervical lordosis (CL), thoracic kyphosis (TK), lumbar lordosis (LL), pelvic incidence (PI), sagittal vertical axis (SVA), cervical SVA (cSVA), and T1 slope, TK/LL, truncal inclination, pelvic tilt (PT), LL-PI, LL/PI, and T1 slope/PI. Patients were stratified by decade of life, and regression analysis was performed to delineate the relationship between each consecutive age group and the aforementioned parameters. RESULTS:Cervical lordosis (R2 = 0.61), thoracic kyphosis (R2 = 0.84), SVA (R2 = 0.88), cSVA (R2 = 0.51), and T1 slope (R2 = 0.77) all increase with age. Truncal inclination (R2 = 0.36) and T1 slope/CL remain stable over all decades (R2 = 0.01). LL starts greater than PI, but in the 6th decade of life, LL becomes equal to PI and in the 7th decade becomes smaller than PI (R2 = 0.96). The ratio of TK/LL is stable until the 7th decade of life (R2 = 0.81), whereas PT is stable until the 6th decade (R2 = 0.92). CONCLUSIONS:This study further refines the generally accepted LL = PI + 10° by showing that patients under the age of 50 years should have more LL compared to PI, whereas after the 5th decade the relationship is reversed. SVA was not as sensitive across age groups, exhibiting a marked increase only in the 7th decade of life. Given the reliable increase of CL with age, and the stability of T1 slope/CL, this represents another important relationship that should be maintained when performing cervical deformity/fusion surgery. This study has important implications for evaluating adult patients with spinal deformities and for establishing corrective surgical goals.

OBJECTIVE:Stereotactic body radiotherapy (SBRT) is an effective treatment of spinal metastases in the vertebral body. However, variation has existed between practitioners regarding the appropriate target delineation. As such, we compared the tumor control, rates of compression fractures, and pain control for patients who had undergone SBRT for spinal metastases to either the lesion only (LO) or the full vertebral body (FVB). METHODS:A total of 126 spinal metastases in 84 patients had received single-fraction SBRT from January 2009 to February 2015. Of the 126 lesions, 36 (29%) were in the FVB group and 90 were in the LO group. The SBRT plans were reviewed to determine the treatment volume. Odds ratios were used to compare the rates of compression fracture and local failure. Regression analysis was performed to identify the predictors of outcome. RESULTS:A total of 5 failures had occurred in the FVB group and 14 in the LO group; however, the difference was not statistically significant (P = 0.5). No difference was found in pain reduction between the 2 groups (P = 0.9). Seven post-treatment compression fractures occurred in the LO group and four in the FVB group; however, the difference was not statistically significant (P = 0.6). The minimum dose to the planning target volume, patient age, and planning target volume size were the only significant factors predicting for local failure, vertebral body fracture, and pain control, respectively. CONCLUSIONS:Given that we found no difference in tumor control, pain reduction, or fracture rate between patients treated to the FVB versus the. LO, it might be reasonable to consider SBRT to the LO for select patients.

BACKGROUND:Spinal ependymomas are rare tumors of the central nervous system, and those spanning the entire cervical spine are atypical. Here, we present two unusual cases of holocervical (C1-C7) spinal ependymomas.CASE DESCRIPTION:Two patients, a 32-year-old female and a 24-year-old male presented with neck pain, motor, and sensory deficits. Sagittal MRI confirmed hypointense lesions on T1 and hyperintense regions on T2 spanning the entire cervical spine. These were accompanied by cystic cavities extending caudally into the thoracic spine and rostrally to the cervicomedullary junction. Both patients underwent gross total resection of these lesions and sustained excellent recoveries.CONCLUSION:Two holocervical cord intramedullary ependymomas were safely and effectively surgically resected without incurring significant perioperative morbidity.

OBJECTIVES:Delayed symptomatic hyponatremia is a known phenomenon occurring > 3 days after transsphenoidal surgery. This is a significant cause of post-operative emergency room visits and re-admissions. We describe and characterize post-operative hyponatremia in patients undergoing endoscopic transsphenoidal surgery, identify predictive factors, and create a clinical tool for predicting high risk patients.PATIENTS & METHODS:We retrospectively reviewed a series of over 300 consecutive patients undergoing endoscopic transsphenoidal surgery and identified patients with delayed hyponatremia as well as patient, tumor, and surgical characteristics. In addition, we recorded inpatient post-operative sodium and specific gravity values as well as treatment upond discharge. Univariate and multivariate analyses were carried out to identify predictors of delayed hyponatremia and stratify patients into risk groups.RESULTS:We found that 15% of patients developed delayed hyponatremia and that this occurred most commonly on post-operative day 7. This accounted for more than half of re-admissions after this type of surgery. Female patients and patients needing fluid restriction or fludrocortisone upon discharge were more likely to develop delayed hyponatremia. Patients with post-operative diabetes insipidus were less likely to develop delayed hyponatremia. Using ROC analysis we developed a score which reliably could stratify patients at risk for delayed hyponatremia.CONCLUSIONS:We confirm the risk of delayed hyponatremia after transphenoidal surgery and identify factors that are revealed before discharge to identify patients at higher risk of delayed hyponatremia. These data may help identify patients who require treatment upon discharge and short interval follow up to avoid significant costs of re-admission.

BACKGROUND:Epithelioid osteoblastoma of the cranium is extremely rare and can mimic other etiologies on radiographic imaging, pathology, and symptomatology.CASE DESCRIPTION:An 18-year-old male patient had a 3-week history of a palpable left temporal mass. Magnetic resonance imaging revealed a large, extra-axial, hypervascular mass in the left temporal bone, with bony erosion and intracranial extension. The patient underwent surgical near gross-total resection of the mass. Initial frozen microscopic examination of the tumor was inconclusive. The postoperative course was uneventful, and the patient was discharged a few days later. Final pathology confirmed the diagnosis of epithelioid osteoblastoma.CONCLUSIONS:Epithelioid osteoblastoma of the skull base is exceedingly rare but should be included in the differential diagnoses of all extra axial tumors. Preoperative radiographic clues are limited, and final diagnosis relies solely on accurate pathologic examination. A diagnosis of epithelioid osteoblastoma should be considered for all cranial bone-based tumors, as an incorrect diagnosis of another radiographic and histologic mimic could lead to the patient receiving unnecessary and harmful neoadjuvant/adjuvant chemotherapy or radiotherapy.

OBJECTIVE Moyamoya disease (MMD) is a chronic cerebrovascular disease that can lead to devastating neurological outcomes. Surgical intervention is the definitive treatment, with direct, indirect, and combined revascularization procedures currently employed by surgeons. The optimal surgical approach, however, remains unclear. In this decision analysis, the authors compared the effectiveness of revascularization procedures in both adult and pediatric patients with MMD. METHODS A comprehensive literature search was performed for studies of MMD. Using complication and success rates from the literature, the authors constructed a decision analysis model for treatment using a direct and indirect revascularization technique. Utility values for the various outcomes and complications were extracted from the literature examining preferences in similar clinical conditions. Sensitivity analysis was performed. RESULTS A structured literature search yielded 33 studies involving 4197 cases. Cases were divided into adult and pediatric populations. These were further subdivided into 3 different treatment groups: indirect, direct, and combined revascularization procedures. In the pediatric population at 5- and 10-year follow-up, there was no significant difference between indirect and combination procedures, but both were superior to direct revascularization. In adults at 4-year follow-up, indirect was superior to direct revascularization. CONCLUSIONS In the absence of factors that dictate a specific approach, the present decision analysis suggests that direct revascularization procedures are inferior in terms of quality-adjusted life years in both adults at 4 years and children at 5 and 10 years postoperatively, respectively. These findings were statistically significant (p < 0.001 in all cases), suggesting that indirect and combination procedures may offer optimal results at long-term follow-up.

Lower back pain and pathologies related to it are one of the most common results for a referral to a neurosurgical clinic in the developed and the developing world. Quantitative evaluation of these pathologies is a challenge. Image based measurements of angles/vertebral heights and disks could provide a potential quantitative biomarker for tracking and measuring these pathologies. Detection of vertebral bodies is a key element and is the focus of the current work.From the variety of medical imaging techniques, MRI and CT scans have been typically used for developing image segmentation methods. However, CT scans are known to give a large dose of x-rays, increasing cancer risk [8]. MRI can be substituted for CTs when the risk is high [8] but are difficult to obtain in smaller facilities due to cost and lack of expertise in the field [2]. X-rays provide another option with its ability to control the x-ray dosage, especially for young people, and its accessibility for smaller facilities. Hence, the ability to create quantitative biomarkers from x-ray data is especially valuable.Here, we develop a multiscale template matching, inspired by [9], to detect centers of vertebral bodies from x-ray data. The immediate application of such detection lies in developing quantitative biomarkers and in querying similar images in a database. Previously, shape similarity classification methods have been used to address this problem, but these are challenging to use in the presence of variation due to gross pathology and even subtle effects [1].