Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images. MATERIALS AND METHODS: Multiparametric preoperative MR imaging scans of 339 pediatric patients (n = 293 internal and n = 46 external cohorts) with a variety of tumor subtypes were preprocessed and manually segmented into 4 tumor subregions, ie, enhancing tumor, nonenhancing tumor, cystic components, and peritumoral edema. After training, performances of the 2 models on internal and external test sets were evaluated with reference to ground truth manual segmentations. Additionally, concordance was assessed by comparing the volume of the subregions as a percentage of the whole tumor between model predictions and ground truth segmentations using the Pearson or Spearman correlation coefficients and the Bland-Altman method. RESULTS: The mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (P ≤ .01). Correlation coefficients for tumor subregion percentage volumes were higher (0.98 versus 0.91 for enhancing tumor, 0.97 versus 0.75 for nonenhancing tumor, 0.98 versus 0.80 for cystic components, 0.95 versus 0.33 for peritumoral edema in the internal test set). Bland-Altman plots were better for nnU-Net compared with DeepMedic. External validation of the trained nnU-Net model on the multi-institutional Brain Tumor Segmentation Challenge in Pediatrics (BraTS-PEDs) 2023 data set revealed high generalization capability in the segmentation of whole tumor, tumor core (a combination of enhancing tumor, nonenhancing tumor, and cystic components), and enhancing tumor with mean Dice scores of 0.87 (SD, 0.13) (median, 0.91), 0.83 (SD, 0.18) (median, 0.89), and 0.48 (SD, 0.38) (median, 0.58), respectively. CONCLUSIONS: The pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

Towards Consistency in Pediatric Brain Tumor Measurements: Challenges, Solutions, and the Role of AI-Based Segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumor from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.

Introducing a novel hybrid educational boot camp to augment medical student training in neurosurgery.

OBJECTIVE: Neurosurgery subinternships are a critical portion of the medical student application to neurosurgery residency programs, allowing programs to assess the student's clinical knowledge, interpersonal skills, work ethic, and character. Despite how critical these auditions are, many students have a poor understanding of expectations prior to beginning these subinternships. Thomas Jefferson University hosted a combined in-person and virtual boot camp session open to all medical students interested in neurosurgery. The authors sought to determine the effectiveness of this inaugural course. METHODS: A total of 304 registered participants were sent a survey assessing their attitudes toward neurosurgery subinternships, beliefs about their abilities, and their comfort with various neurosurgical skills. All participants were sent a postsession survey composed of the same questions. The mean scores for responses to pre- and postsession survey questions were recorded based on graduating year and by medical school type (US allopathic [US MD], US osteopathic [US DO], or foreign degree/international medical graduate [IMG]). Differences in means between pre- and postsession survey responses were analyzed using the Student t-test, and statistical significance was set at p < 0.05. RESULTS: A total of 112 presession surveys and 64 postsession surveys were completed, yielding a presession survey response rate of 36.8% and a postsession survey response rate of 21.1%. Seventy-five percent of the postsession survey respondents attended virtually, and 25% were in-person. US MD, US DO, and IMG attendees demonstrated a significantly increased understanding of the expectations of a neurosurgery subintern (p < 0.001). All students had significantly increased confidence in their ability to succeed as subinterns (US MD students and IMGs p < 0.001, US DO students p < 0.05). Regarding procedural confidence, US MD students had increased confidence in craniotomies and cranial plating (p < 0.001). When comparing responses by graduation year, students in the classes of 2024 and 2025 (rising 4th-year and rising 3rd-year medical students, respectively) demonstrated significantly increased understanding of expectations and confidence in their ability to succeed (< 0.001). Seventy-five percent of our postsession survey respondents attended virtually, and 25% were in-person. The in-person cohort had greater improvements in comfort with procedures such as craniotomies, cranial plating, and extraventricular drain placement (in-person vs Zoom mean differences: craniotomies and cranial plating, -2.29, extraventricular drain placement, -2.31) (p < 0.05). CONCLUSIONS: The boot camp successfully delineated the expectations of neurosurgery subinterns and enhanced the attendees' confidence in their abilities. The authors concluded that a hybrid virtual and in-person format is beneficial and feasible in increasing accessibility to information about neurosurgery subinternships.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

Statistical plots in oncologic imaging, a primer for neuroradiologists.

The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data.

Radiomics for characterization of the glioma immune microenvironment.

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n=215 internal and n=29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n=151), validation (n=43), and withheld internal test (n=21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median±SD) was 0.91±0.10/0.88±0.16 for the whole tumor, 0.73±0.27/0.84±0.29 for ET, 0.79±19/0.74±0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98±0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements. Key Points: We proposed automated tumor segmentation and brain extraction on pediatric MRI.The volumetric measurements using our models agree with ground truth segmentations. Importance of the Study: The current response assessment in pediatric brain tumors (PBTs) is currently based on bidirectional or 2D measurements, which underestimate the size of non-spherical and complex PBTs in children compared to volumetric or 3D methods. There is a need for development of automated methods to reduce manual burden and intra- and inter-rater variability to segment tumor subregions and assess volumetric changes. Most currently available automated segmentation tools are developed on adult brain tumors, and therefore, do not generalize well to PBTs that have different radiological appearances. To address this, we propose a deep learning (DL) auto-segmentation method that shows promising results in PBTs, collected from a publicly available large-scale imaging dataset (Children's Brain Tumor Network; CBTN) that comprises multi-parametric MRI scans of multiple PBT types acquired across multiple institutions on different scanners and protocols. As a complementary to tumor segmentation, we propose an automated DL model for brain tissue extraction.

Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers.

INTRODUCTION: Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes. METHODS: Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes. RESULTS: K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p<0.05). It was also found that age (p < 0.05), tumor location (p < 0.01), and tumor histology (p < 0.0001) differed significantly between the imaging subtypes. CONCLUSION: In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs.

Radiomics and radiogenomics in pediatric neuro-oncology: A review.

The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (ie, radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is twofold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.

The influence of race on outcomes following pituitary tumor resection.

OBJECTIVE: To assess the influence of race on short-term patient outcomes in a pituitary tumor surgery population. PATIENTS AND METHODS: Coarsened exact matching was used to retrospectively analyze consecutive patients (n = 567) undergoing pituitary tumor resection over a six-year period (June 07, 2013 to April 29, 2019) at a single, multi-hospital academic medical center. Black/African American and white patients were exact matched based on twenty-nine (29) patient, procedure, and hospital characteristics. Matching characteristics included surgical costs, American Society of Anesthesiologists grade, duration of surgery, and Charlson Comorbidity Index, amongst others. Outcomes studied included unplanned 90-day readmission, emergency room (ER) evaluation, and unplanned reoperation. RESULTS: Ninety-two (n = 92) patients were exact matched and analyzed. There was no significant difference in 90-day readmission (p = 0.267, OR (black/AA vs white) = 0.500, 95% CI = 0.131-1.653) or ER evaluation within 90 days (p = 0.092, OR = 3.000, 95% CI = 0.848-13.737) between the two cohorts. Furthermore, there was no significant difference in the rate of unplanned reoperation throughout the duration of the follow up period between matched black/African American and white patients (p = 0.607, OR = 0.750, 95% CI = 0.243-2.211). CONCLUSION: This study suggests that the effect of race on post-operative outcomes is largely mitigated when equal access is attained, and when race is effectively isolated from socioeconomic factors and comorbidities in a population undergoing pituitary tumor resection.

Development of an Intraoperative Pipeline for Holographic Mixed Reality Visualization During Spinal Fusion Surgery.

Objective. Holographic mixed reality (HMR) allows for the superimposition of computer-generated virtual objects onto the operator's view of the world. Innovative solutions can be developed to enable the use of this technology during surgery. The authors developed and iteratively optimized a pipeline to construct, visualize, and register intraoperative holographic models of patient landmarks during spinal fusion surgery. Methods. The study was carried out in two phases. In phase 1, the custom intraoperative pipeline to generate patient-specific holographic models was developed over 7 patients. In phase 2, registration accuracy was optimized iteratively for 6 patients in a real-time operative setting. Results. In phase 1, an intraoperative pipeline was successfully employed to generate and deploy patient-specific holographic models. In phase 2, the registration error with the native hand-gesture registration was 20.2 ± 10.8 mm (n = 7 test points). Custom controller-based registration significantly reduced the mean registration error to 4.18 ± 2.83 mm (n = 24 test points, P < .01). Accuracy improved over time (B = -.69, P < .0001) with the final patient achieving a registration error of 2.30 ± .58 mm. Across both phases, the average model generation time was 18.0 ± 6.1 minutes (n = 6) for isolated spinal hardware and 33.8 ± 8.6 minutes (n = 6) for spinal anatomy. Conclusions. A custom pipeline is described for the generation of intraoperative 3D holographic models during spine surgery. Registration accuracy dramatically improved with iterative optimization of the pipeline and technique. While significant improvements and advancements need to be made to enable clinical utility, HMR demonstrates significant potential as the next frontier of intraoperative visualization.

Role of Race in Short-Term Outcomes for 1700 Consecutive Patients Undergoing Brain Tumor Resection.

BACKGROUND: Access to medical care seems to be impacted by race. However, the effect of race on outcomes, once care has been established, is poorly understood. PURPOSE: This study seeks to assess the influence of race on patient outcomes in a brain tumor surgery population. IMPORTANCE AND RELEVANCE TO HEALTHCARE QUALITY: This study offers insights to if or how quality is impacted based on patient race, after care has been established. Knowledge of disparities may serve as a valuable first step toward risk factor mitigation. METHODS: Patients differing in race, but matched on other outcomes affecting characteristics, were assessed for differences in outcomes subsequent to brain tumor resection. Coarsened exact matching was used to match 1700 supratentorial brain tumor procedures performed over a 6-year period at a single, multihospital academic medical center. Patient outcomes assessed included unplanned readmission, mortality, emergency department (ED) visits, and unanticipated return to surgery. RESULTS: There was no significant difference in readmissions, mortality, ED visits, return to surgery after index admission, or return to surgery within 30 days between the two races. CONCLUSION: This study suggests that race does not independently influence postsurgical outcomes but may instead serve as a proxy for other closely related demographics.

PET/MR Imaging in Musculoskeletal Precision Imaging - Third wave after X-Ray and MR.

18F-Fluorodeoxyglucose PET has been used to evaluate a wide array of inflammatory and neoplastic pathologies. MR imaging has great soft tissue resolution and high accuracy for detection of edema. Combining PET with MR imaging offers substantial advantages in musculoskeletal imaging. Specifically, evidence demonstrates the potential of imaging of bone marrow, soft tissue, and synovia by PET/MR imaging. Because of inherent limitations of 1H-MR to image cortical bone, there are some challenges; however, the use of 18F-sodium fluoride for PET/MR imaging could change the landscape. This article reviews the literature regarding PET/MR imaging in identification and management of many musculoskeletal diseases.

Predicting short-term outcomes following supratentorial tumor surgery.

OBJECTIVES: The LACE+ index risk prediction tool has not been successfully used to predict short-term outcomes after neurosurgery. This study assessed the ability of LACE+ to predict 30-day (30D) adverse outcomes after supratentorial brain tumor surgery. PATIENTS AND METHODS: LACE+ scores were retrospectively calculated for consecutive patients (n = 624) who received surgery for supratentorial tumors at one multi-center health system (2017-2019). Coarsened exact matching was employed to control for confounding variables. Outcomes including unplanned hospital readmission, emergency department visits, and death were compared for patients with different LACE+ score quartiles (Q1, Q2, Q3, Q4). RESULTS: 134 patients were matched between Q1 and Q4; 152 patients between Q2 and Q4; 192 patients between Q3 and Q4. LACE+ score was not found to predict readmission within 30D of discharge for Q1 vs Q4 (p = 0.239), Q2 vs Q4 (p = 0.336), or Q3 vs Q4 (p = 0.739). LACE + score also did not predict 30D risk of emergency department visits for Q1 vs Q4 (p = 0.210), Q2 vs Q4 (p = 0.839), or Q3 vs Q4 (p = 0.167). LACE + did predict death within 30D of surgery for Q3 vs Q4 (1.04 % vs 7.29 %, p = 0.039), but not for Q1 vs Q4 (p = 0.625) or Q2 vs Q4 (p = 0.125). CONCLUSION: LACE + may not be suitable for characterizing short-term risk of certain perioperative events in a patient population undergoing supratentorial brain tumor surgery.

The LACE+ Index as a Predictor of 90-Day Supratentorial Tumor Surgery Outcomes.

BACKGROUND: The LACE+ (Length of stay, Acuity of admission, Charlson Comorbidity Index [CCI] score, and Emergency department [ED] visits in the past 6 mo) index risk-prediction tool has never been successfully tested in a neurosurgery population. OBJECTIVE: To assess the ability of LACE+ to predict adverse outcomes after supratentorial brain tumor surgery. METHODS: LACE+ scores were retrospectively calculated for all patients (n = 624) who underwent surgery for supratentorial tumors at the University of Pennsylvania Health System (2017-2019). Confounding variables were controlled with coarsened exact matching. The frequency of unplanned hospital readmission, ED visits, and death was compared for patients with different LACE+ score quartiles (Q1, Q2, Q3, and Q4). RESULTS: A total of 134 patients were matched between Q1 and Q4; 152 patients were matched between Q2 and Q4; and 192 patients were matched between Q3 and Q4. Patients with higher LACE+ scores were significantly more likely to be readmitted within 90 d (90D) of discharge for Q1 vs Q4 (21.88% vs 46.88%, P = .005) and Q2 vs Q4 (27.03% vs 55.41%, P = .001). Patients with larger LACE+ scores also had significantly increased risk of 90D ED visits for Q1 vs Q4 (13.33% vs 30.00%, P = .027) and Q2 vs Q4 (22.54% vs 39.44%, P = .039). LACE+ score also correlated with death within 90D of surgery for Q2 vs Q4 (2.63% vs 15.79%, P = .003) and with death at any point after surgery/during follow-up for Q1 vs Q4 (7.46% vs 28.36%, P = .002), Q2 vs Q4 (15.79% vs 31.58%, P = .011), and Q3 vs Q4 (18.75% vs 31.25%, P = .047). CONCLUSION: LACE+ may be suitable for characterizing risk of certain perioperative events in a patient population undergoing supratentorial brain tumor resection.

Assessing the Role of Patient Race in Disparity of 90-Day Brain Tumor Resection Outcomes.

BACKGROUND: This study assesses the influence of race on patient outcomes in a brain tumor surgery population. METHODS: Coarsened exact matching was used to retrospectively analyze 1700 supratentorial brain tumor procedures over a 6-year period (June 7, 2013 to April 29, 2019) at a single, multihospital academic medical center. Outcome measures included readmission, mortality, emergency room visits, and reoperation. RESULTS: McNemar test (mid-P) showed no significant difference in 90-day mortality between the 2 races (P = 0.3018). However, there was a significant difference in 90-day readmissions between the 2 races (P = 0.0237). There was no significant difference in 90-day emergency room visits (P = 0.0579), 90-day return to surgery after index admission (P = 0.6015), or return to surgery within 90 days (P = 0.6776) between the 2 races. There was also no significant difference in return to surgery for the duration of the follow-up period (P = 0.8728). CONCLUSIONS: This study suggests that race alone does not result in disparate outcomes; however, there was an associated difference in 90-day postsurgical readmissions. Despite coarsened exact matching, persistent differences in median household income may play a role in the disparate outcome noted.

The Effect of Race on Short-Term Pituitary Tumor Outcomes.

BACKGROUND: The relationship between race and neurosurgical outcomes is poorly characterized despite its importance. The influence of race on short-term patient outcomes in a pituitary tumor surgery population was assessed. METHODS: Coarsened exact matching was used to retrospectively analyze 567 consecutive pituitary tumor cases from a 6-year period (June 7, 2013, to April 29, 2019) at a single, multihospital academic medical center. Outcomes studied included 30-day readmission, mortality, and reoperation. RESULTS: There were 92 exact-matched cases suitable for analysis. There was a significant difference in 30-day emergency department visits between the 2 races (black/African American vs. white odds ratio = 4.5, 95% confidence interval = 1.072-30.559, P = 0.0386). There was no observed mortality over the 30-day postoperative period. There was no significant difference in 30-day readmission between the 2 race cohorts (P = 0.3877), in return to surgery after index admission within 30 days (P = 1.000), or in return to surgery within 30 days (P = 0.3750). CONCLUSIONS: This study suggests that the effect of race on outcomes is partly mitigated for individuals who can attain access, and when socioeconomic factors and comorbidities are controlled for. The noted significant difference in emergency department visits could be indicative of confounding variables that were not well controlled for and requires further exploration.

The LACE + index as a predictor of 90-day urologic surgery outcomes.

PURPOSE: This study assessed the ability of the LACE + [Length of stay, Acuity of admission, Charlson Comorbidity Index (CCI) score, and Emergency department visits in the past 6 months] index to predict adverse outcomes after urologic surgery. METHODS: LACE + scores were retrospectively calculated for all consecutive patients (n = 9824) who received urologic surgery at one multi-center health system over 2 years (2016-2018). Coarsened exact matching was employed to sort patient data before analysis; matching criteria included duration of surgery, BMI, and race among others. Outcomes including unplanned hospital readmission, emergency room visits, and reoperation were compared for patients with different LACE + quartiles. RESULTS: 722 patients were matched between Q1 and Q4; 1120 patients were matched between Q2 and Q4; 2550 patients were matched between Q3 and Q4. Higher LACE + score significantly predicted readmission within 90 days (90D) of discharge for Q1 vs Q4 and Q2 vs Q4. Increased LACE + score also significantly predicted 90D emergency room visits for Q1 vs Q4, Q2 vs Q4, and Q3 vs Q4. LACE + score was also significantly predictive of 90D reoperation for Q1 vs Q4. LACE + score did not predict 90D reoperation for Q2 vs Q4 or Q3 vs Q4 or 90D readmission for Q3 vs. Q4. CONCLUSION: These results suggest that LACE + may be a suitable prediction model for important patient outcomes after urologic surgery.