Exploring intentions of physician-scientist trainees: factors influencing MD and MD/PhD interest in research careers.

BACKGROUND: Prior studies have described the career paths of physician-scientist candidates after graduation, but the factors that influence career choices at the candidate stage remain unclear. Additionally, previous work has focused on MD/PhDs, despite many physician-scientists being MDs. This study sought to identify career sector intentions, important factors in career selection, and experienced and predicted obstacles to career success that influence the career choices of MD candidates, MD candidates with research-intense career intentions (MD-RI), and MD/PhD candidates. METHODS: A 70-question survey was administered to students at 5 academic medical centers with Medical Scientist Training Programs (MSTPs) and Clinical and Translational Science Awards (CTSA) from the NIH. Data were analyzed using bivariate or multivariate analyses. RESULTS: More MD/PhD and MD-RI candidates anticipated or had experienced obstacles related to balancing academic and family responsibilities and to balancing clinical, research, and education responsibilities, whereas more MD candidates indicated experienced and predicted obstacles related to loan repayment. MD/PhD candidates expressed higher interest in basic and translational research compared to MD-RI candidates, who indicated more interest in clinical research. Overall, MD-RI candidates displayed a profile distinct from both MD/PhD and MD candidates. CONCLUSIONS: MD/PhD and MD-RI candidates experience obstacles that influence their intentions to pursue academic medical careers from the earliest training stage, obstacles which differ from those of their MD peers. The differences between the aspirations of and challenges facing MD, MD-RI and MD/PhD candidates present opportunities for training programs to target curricula and support services to ensure the career development of successful physician-scientists.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases.

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy.

PURPOSE: To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. METHODS: Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation. RESULTS: DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. CONCLUSIONS: The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.

BACKGROUND: Cerebral vasospasm (CV) is a feared complication which occurs after 20-40% of subarachnoid haemorrhage (SAH). It is standard practice to admit patients with SAH to intensive care for an extended period of resource-intensive monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date. METHODS: Patients with SAH admitted to UCLA from 2013 to 2022 and a validation cohort from VUMC from 2018 to 2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or no verapamil. At each institution, a light gradient boosting machine (LightGBM) was trained using five-fold cross validation to predict the primary endpoint at various hospitalization timepoints. FINDINGS: A total of 1750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 > 1 week in advance and ruled out 8% of non-verapamil patients with zero false negatives. Our models predicted "no CVRV" vs "CVRV within three days" vs "CVRV after three days" with AUCs = 0.88, 0.83, and 0.88, respectively. From VUMC, 1654 patients were included, 75 receiving verapamil. VUMC predictions averaged within 0.01 AUC points of UCLA predictions. INTERPRETATION: We present an accurate and early predictor of CVRV using machine learning with multi-center validation. This represents a significant step towards optimized clinical management and resource allocation in patients with SAH. FUNDING: Robert E. Freundlich is supported by National Center for Advancing Translational Sciences federal grant UL1TR002243 and National Heart, Lung, and Blood Institute federal grant K23HL148640; these funders did not play any role in this study. The National Institutes of Health supports Vanderbilt University Medical Center which indirectly supported these research efforts. Neither this study nor any other authors personally received financial support for the research presented in this manuscript. No support from pharmaceutical companies was received.

Machine Learning Predicts Cerebral Vasospasm in Subarachnoid Hemorrhage Patients.

Background: Cerebral vasospasm (CV) is a feared complication occurring in 20-40% of patients following subarachnoid hemorrhage (SAH) and is known to contribute to delayed cerebral ischemia. It is standard practice to admit SAH patients to intensive care for an extended period of vigilant, resource-intensive, clinical monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date. Methods: SAH patients admitted to UCLA from 2013-2022 and a validation cohort from VUMC from 2018-2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or ICU downgrade. At each institution, a light gradient boosting machine (LightGBM) was trained using five- fold cross validation to predict the primary endpoint at various timepoints during hospital admission. Receiver-operator curves (ROC) and precision-recall (PR) curves were generated. Results: A total of 1,750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 an average of over one week in advance, and successfully ruled out 8% of non-verapamil patients with zero false negatives. Minimum leukocyte count, maximum platelet count, and maximum intracranial pressure were the variables with highest predictive accuracy. Our models predicted "no CVRV" vs "CVRV within three days" vs "CVRV after three days" with AUCs=0.88, 0.83, and 0.88, respectively. For external validation at VUMC, 1,654 patients were included, 75 receiving verapamil. Predictive models at VUMC performed very similarly to those at UCLA, averaging 0.01 AUC points lower. Conclusions: We present an accurate (AUC=0.88) and early (>1 week prior) predictor of CVRV using machine learning over two large cohorts of subarachnoid hemorrhage patients at separate institutions. This represents a significant step towards optimized clinical management and improved resource allocation in the intensive care setting of subarachnoid hemorrhage patients.

Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification.

Multivariate pattern analysis (MVPA) methods such as support vector machines (SVMs) have been increasingly applied to fMRI and sMRI analyses, enabling the detection of distinctive imaging patterns. However, identifying brain regions that significantly contribute to the classification/group separation requires computationally expensive permutation testing. In this paper we show that the results of SVM-permutation testing can be analytically approximated. This approximation leads to more than a thousandfold speedup of the permutation testing procedure, thereby rendering it feasible to perform such tests on standard computers. The speedup achieved makes SVM based group difference analysis competitive with standard univariate group difference analysis methods.

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles.

BACKGROUND: Narrowing of the lumbar spinal canal, or lumbar stenosis (LS), may cause debilitating radicular pain or muscle weakness. It is the most frequent indication for spinal surgery in the elderly population. Modern diagnosis relies on magnetic resonance imaging and its inherently subjective interpretation. Diagnostic rigor, accuracy, and speed may be improved by automation. In this work, we aimed to determine whether a deep-U-Net ensemble trained to segment spinal canals on a heterogeneous mix of clinical data is comparable to radiologists' segmentation of these canals in patients with LS. METHODS: The deep U-nets were trained on spinal canals segmented by physicians on 100 axial T2 lumbar magnetic resonance imaging selected randomly from our institutional database. Test data included a total of 279 elderly patients with LS that were separate from the training set. RESULTS: Machine-generated segmentations (MA) were qualitatively similar to expert-generated segmentations (ME1, ME2). Machine- and expert-generated segmentations were quantitatively similar, as evidenced by Dice scores (MA vs. ME1: 0.88 ± 0.04, MA vs. ME2: 0.89 ± 0.04), the Hausdorff distance (MA vs. ME1: 11.7 mm ± 13.8, MA vs. ME2: 13.1 mm ± 16.3), and average surface distance (MAvs. ME1: 0.18 mm ± 0.13, MA vs. ME2 0.18 mm ± 0.16) metrics. These metrics are comparable to inter-rater variation (ME1 vs. ME2 Dice scores: 0.94 ± 0.02, the Hausdorff distances: 9.3 mm ± 15.6, average surface distances: 0.08 mm ± 0.09). CONCLUSION: We conclude that machine learning algorithms can segment lumbar spinal canals in LS patients, and automatic delineations are both qualitatively and quantitatively comparable to expert-generated segmentations.

Imaging Biomarker Development for Lower Back Pain Using Machine Learning: How Image Analysis Can Help Back Pain.

State-of-the-art diagnosis of radiculopathy relies on "highly subjective" radiologist interpretation of magnetic resonance imaging of the lower back. Currently, the treatment of lumbar radiculopathy and associated lower back pain lacks coherence due to an absence of reliable, objective diagnostic biomarkers. Using emerging machine learning techniques, the subjectivity of interpretation may be replaced by the objectivity of automated analysis. However, training computer vision methods requires a curated database of imaging data containing anatomical delineations vetted by a team of human experts. In this chapter, we outline our efforts to develop such a database of curated imaging data alongside the required delineations. We detail the processes involved in data acquisition and subsequent annotation. Then we explain how the resulting database can be utilized to develop a machine learning-based objective imaging biomarker. Finally, we present an explanation of how we validate our machine learning-based anatomy delineation algorithms. Ultimately, we hope to allow validated machine learning models to be used to generate objective biomarkers from imaging data-for clinical use to diagnose lumbar radiculopathy and guide associated treatment plans.

Structural Changes in the Cervicomedullary Junction in Adult Chiari Patients.

OBJECTIVE: To 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). METHODS: A 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. RESULTS: Twenty 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). CONCLUSION: The volume of the cervical cord at the cervical-medullary junction, width of the cervicomedullary cord, and diameter of the brainstem increase following PFD.

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms.

BACKGROUND: Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months. METHODS: Spontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition. RESULTS: Autism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD. CONCLUSIONS: Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.

Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.

BACKGROUND: The referral process for consultation with a spine surgeon remains inefficient, given a substantial proportion of referrals to spine surgeons are nonoperative. OBJECTIVE: To develop a machine-learning-based algorithm which accurately identifies patients as candidates for consultation with a spine surgeon, using only magnetic resonance imaging (MRI). METHODS: We trained a deep U-Net machine learning model to delineate spinal canals on axial slices of 100 normal lumbar MRI scans which were previously delineated by expert radiologists and neurosurgeons. We then tested the model against lumbar MRI scans for 140 patients who had undergone lumbar spine MRI at our institution (60 of whom ultimately underwent surgery, and 80 of whom did not). The model generated automated segmentations of the lumbar spinal canals and calculated a maximum degree of spinal stenosis for each patient, which served as our biomarker for surgical pathology warranting expert consultation. RESULTS: The machine learning model correctly predicted surgical candidacy (ie, whether patients ultimately underwent lumbar spinal decompression) with high accuracy (area under the curve = 0.88), using only imaging data from lumbar MRI scans. CONCLUSION: Automated interpretation of lumbar MRI scans was sufficient to correctly determine surgical candidacy in nearly 90% of cases. Given that a significant proportion of referrals placed for spine surgery evaluation fail to meet criteria for surgical intervention, our model could serve as a valuable tool for patient triage and thereby address some of the inefficiencies within the outpatient surgical referral process.

Perioperative dual antiplatelet therapy for patients undergoing spine surgery soon after drug eluting stent placement.

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.

Relationship Between Superior Semicircular Canal Dehiscence Volume with Clinical Symptoms: Case Series.

BACKGROUND: Superior semicircular canal dehiscence (SSCD) is an osseous defect of the arcuate eminence of the petrosal temporal bone. Patients typically present with auditory and vestibular symptoms, such as hearing loss and disequilibrium. Using advanced imaging segmentation techniques, we evaluated whether the volume of SSCD correlated with preoperative symptoms and postoperative outcomes. METHODS: Our laboratory previously described a novel method of quantifying the size of an SSCD via manual segmentation. High-resolution computed tomography images of the temporal bones were imported into a specialized segmentation software. The volume of the dehiscence was outlined on consecutive slices of the coronal and axial planes via a single-pixel-thick paintbrush tool and was then calculated according to the number of nonzero image voxels. RESULTS: This study included 111 patients (70 women and 41 men; mean age, 55.1 years; age range, 24-87 years) with a total of 164 SSCDs. Mean postoperative follow-up time was 5.2 months (range, 0.03-59.5 months). The most common preoperative and postoperative symptoms were tinnitus (n = 85) and dizziness (n = 45), respectively. Surgery resulted in improvement of symptoms in most patients. The average volume of 164 SSCDs was 1.3 mm3. SSCD volume was not significantly associated with either preoperative symptoms or postoperative outcomes. CONCLUSIONS: Advances in imaging techniques have allowed increased visualization of SSCD. Further research will be necessary to evaluate the potential correlation of volume of the dehiscence with clinical variables.

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation.

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.

Comparison of Clinical Outcomes Stratified by Target Delineation for Patients Undergoing Stereotactic Body Radiotherapy for Spinal Metastases.

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.

Natural history of the aging spine: a cross-sectional analysis of spinopelvic parameters in the asymptomatic population.

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.

Clinical and surgical management of holocervical spinal cord ependymomas.

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.

Quantitative Analysis of Neural Foramina in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.

PURPOSE: To use machine learning tools and leverage big data informatics to statistically model the variation in the area of lumbar neural foramina in a large asymptomatic population. MATERIALS AND METHODS: By using an electronic health record and imaging archive, lumbar MRI studies in 645 male (mean age, 50.07 years) and 511 female (mean age, 48.23 years) patients between 20 and 80 years old were identified. Machine learning algorithms were used to delineate lumbar neural foramina autonomously and measure their areas. The relationship between neural foraminal area and patient age, sex, and height was studied by using multivariable linear regression. RESULTS: Neural foraminal areas correlated directly with patient height and inversely with patient age. The associations involved were statistically significant (P < .01). CONCLUSION: By using machine learning and big data techniques, a linear model encoding variation in lumbar neural foraminal areas in asymptomatic individuals has been established. This model can be used to make quantitative assessments of neural foraminal areas in patients by comparing them to the age-, sex-, and height-adjusted population averages.© RSNA, 2019Supplemental material is available for this article.

Timing of adjuvant radiation therapy and survival outcomes after surgical resection of intracranial non-small cell lung cancer metastases.

OBJECTIVE: To investigate if delay of adjuvant radiotherapy (ART) beyond 6 post-operative weeks affects survival outcomes in patients undergoing craniotomy or craniectomy for resection of non-small cell lung cancer (NSCLC) intracranial metastases. PATIENTS AND METHODS: We performed a retrospective analysis of 28 patients undergoing resection of intracranial metastases and ART at our institution from 2001 to 2016. We assessed survival outcomes for patients who received delayed versus non-delayed ART, as well as associated risk factors. RESULTS: Among 28 patients, 8 (29%) had delayed ART beyond 6 post-operative weeks. Fifteen received stereotactic radiotherapy (SRT), 8 (29%) received whole brain radiotherapy (WBRT), and 5 (18%) received combination WBRT + SRT. There were no significant differences in ART modality or dosing, age, sex, number of intracranial metastases, primary metastasis volume, rates of chemotherapy, extracranial metastases, or post-operative functional scores between groups. Expected post-operative survival was shorter with delayed ART (7 months versus 28 months, P = 0.01). The most common reason for delayed ART was complicated post-operative course (n = 3.38%). Significant risk factors for delayed ART included non-routine discharge (P = 0.01) and additional invasive procedures between surgery and ART start date (P = 0.02). CONCLUSIONS: Our results suggest delayed ART in patients undergoing surgical resection of intracranial NSCLC metastases is associated with shorter overall survival. However, risk factors for delayed ART, including non-routine discharge and the need for additional invasive procedures, may have in themselves reflected poorer clinical courses that may have also contributed to the observed survival differences.

A Comparative Study of Automatic Approaches for Preclinical MRI-based Brain Segmentation in the Developing Rat.

Accurate pre-clinical study reporting requires validated processing tools to increase data reproducibility within and between laboratories. Segmentation of rodent brain from non-brain tissue is an important first step in preclinical imaging pipelines for which well validated tools are still under development. The current study aims to clarify the best approach to automatic brain extraction for studies in the immature rat. Skull stripping modules from AFNI, PCNN-3D, and RATS software packages were assessed for their ability to accurately segment brain from non-brain by comparison to manual segmentation. Comparison was performed using Dice coefficient of similarity. Results showed that the RATS package outperformed the others by including a lower percentage of false positive, non-brain voxels in the brain mask. However, AFNI resulted in a lower percentage of false negative voxels. Although the automatic approaches for brain segmentation significantly facilitate the data stream process, the current study findings suggest that the task of rodent brain segmentation from T2 weighted MRI needs to be accompanied by a supervised quality control step when developmental brain imaging studies were targeted.