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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.
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Realidade Aumentada , Fusão Vertebral , Cirurgia Assistida por Computador , Humanos , Imageamento Tridimensional , Procedimentos NeurocirúrgicosRESUMO
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.
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Tempo de Internação/estatística & dados numéricos , Readmissão do Paciente/estatística & dados numéricos , Doenças Urológicas/cirurgia , Procedimentos Cirúrgicos Urológicos , Serviço Hospitalar de Emergência , Previsões , Hospitalização , Humanos , Complicações Pós-Operatórias/epidemiologia , Reoperação , Estudos Retrospectivos , Fatores de Tempo , Resultado do Tratamento , Doenças Urológicas/complicaçõesRESUMO
Mild traumatic brain injury (mTBI) is a low-level injury, which often remains undiagnosed, and in most cases it leads to death and disability as it advances as secondary injury. Therefore, it is important to study the underlying signaling mechanisms of mTBI-associated neurological ailments. While transforming growth factor-beta1 (TGF-ß1) has a significant role in inflammation and apoptosis in myriads of other pathophysiological conditions, the precise function of increased TGF-ß1 after mTBI is unknown. In this study, our objective is to study the physiological relevance and associated mechanisms of TGF-ß1-mediated inflammation and apoptosis in mTBI. Using an in vitro stretch-injury model in rat neuronal cultures and the in vivo fluid percussion injury (FPI) model in rats, we explored the significance of TGF-ß1 activation in mTBI. Our study demonstrated that the activation of TGF-ß1 in mTBI correlated with the induction of free radical generating enzyme NADPH oxidase 1 (NOX1). Further, using TGF-ß type I receptor (TGF-ßRI) inhibitor SB431542 and transfection of TGF-ß1 siRNA and TGF-ß antagonist Smad7, we established the neuroinflammatory and apoptotic role of TGF-ß1 in mTBI. Inhibition of TGF-ßRI or TGF-ß1 diminished TGF-ß1-induced inflammation and apoptosis. Further, the enhanced TGF-ß1 activation increased the phosphorylation of R-Smads including Smad2 and Smad3 proteins. By immunofluorescence, western blotting, ELISA and TUNEL experiments, we demonstrated the up-regulation of pro-inflammatory cytokines IL-1ß and TNF-α and apoptotic cell death in neurons. In conclusion, this study could establish the significance of TGF-ß1 in transforming the pathophysiology of mTBI into secondary injury.
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Apoptose , Concussão Encefálica/metabolismo , Encefalite/metabolismo , Fator de Crescimento Transformador beta1/metabolismo , Animais , Concussão Encefálica/complicações , Células Cultivadas , Encefalite/complicações , Mediadores da Inflamação/metabolismo , Masculino , NADPH Oxidase 1/metabolismo , Estresse Oxidativo , Ratos Sprague-Dawley , Transdução de SinaisRESUMO
Traumatic brain injury (TBI), even at mild levels, can activate matrix metalloproteinases (MMPs) and the induction of neuroinflammation that can result in blood brain barrier breakdown and neurodegeneration. MMP2 has a significant role in neuroinflammation and neurodegeneration by modulating the chemokine CXCL12α (stromal cell derived factor SDF-1α) signaling pathway and the induction of apoptosis. SDF-1α is responsible for cell proliferation and differentiation throughout the nervous system and is also implicated in various neurodegenerative illnesses. We hypothesized that TBI leads to MMP2 activation and cleavage of the N-terminal 4 amino acid residues of CXCL12α with generation of the highly neurotoxic fragment SDF-1(5-67). Using an in vitro stretch-injury model of rat neuronal cultures and the in vivo fluid percussion injury (FPI) model in rats, we found that oxidative stress has a significant role in the activation of MMP2. This is initiated by the induction of free radical generating enzyme NADPH oxidase 1 (NOX1). Induction of NOX1 correlated well with the signatures of oxidative stress marker, 4HNE in the injured neuronal cultures and cerebral cortex of rats. Further, using MMP2 siRNA and pharmacological MMP2 inhibitor, ARP100, we established the neurodegenerative role of MMP2 in cleaving SDF-1α to a neurotoxic fragment SDF-1(5-67). By immunofluorescence, western blotting and TUNEL experiments, we show the cleaved form of SDF leads to apoptotic cell death in neurons. This work identifies a new potential therapeutic target to reduce the complications of brain damage in TBI.
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Lesões Encefálicas Traumáticas/enzimologia , Quimiocina CXCL12/metabolismo , Metaloproteinase 2 da Matriz/metabolismo , Degeneração Neural/enzimologia , Degeneração Neural/genética , Animais , Apoptose/efeitos dos fármacos , Lesões Encefálicas Traumáticas/genética , Caspase 3/biossíntese , Caspase 3/genética , Sobrevivência Celular/genética , Células Cultivadas , Quimiocina CXCL12/genética , Ativação Enzimática , Técnicas de Silenciamento de Genes , Metaloproteinase 2 da Matriz/genética , Inibidores de Metaloproteinases de Matriz/farmacologia , NADPH Oxidase 1/biossíntese , NADPH Oxidase 1/genética , Neurônios/efeitos dos fármacos , Estresse Oxidativo , RNA Interferente Pequeno/farmacologia , Ratos , Ratos Sprague-DawleyRESUMO
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.
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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.
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Neoplasias Encefálicas , Aprendizado Profundo , Humanos , Neoplasias Encefálicas/diagnóstico por imagem , Criança , Masculino , Feminino , Adolescente , Pré-Escolar , Imageamento por Ressonância Magnética Multiparamétrica/métodos , Lactente , Imageamento Tridimensional/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodosRESUMO
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.
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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.
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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.
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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.
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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.
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Neoplasias Encefálicas , Glioma , Humanos , Criança , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patologia , Aprendizado de Máquina não Supervisionado , Proteínas Proto-Oncogênicas B-raf , Estudos Retrospectivos , Glioma/diagnóstico por imagem , Glioma/genética , Glioma/metabolismo , Imageamento por Ressonância Magnética/métodos , BiomarcadoresRESUMO
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.
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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.
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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.
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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.
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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.
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Negro ou Afro-Americano/estatística & dados numéricos , Neoplasias Hipofisárias/etnologia , Neoplasias Hipofisárias/cirurgia , Complicações Pós-Operatórias/epidemiologia , População Branca/estatística & dados numéricos , Serviço Hospitalar de Emergência , Humanos , Duração da Cirurgia , Readmissão do Paciente , Reoperação , Estudos Retrospectivos , Fatores Socioeconômicos , Resultado do TratamentoRESUMO
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.
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Neoplasias Encefálicas , Readmissão do Paciente , Neoplasias Encefálicas/cirurgia , Serviço Hospitalar de Emergência , Hospitalização , Humanos , Estudos RetrospectivosRESUMO
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.
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Neoplasias Encefálicas/epidemiologia , Neoplasias Encefálicas/cirurgia , Disparidades em Assistência à Saúde/estatística & dados numéricos , Procedimentos Neurocirúrgicos/estatística & dados numéricos , Grupos Raciais , Neoplasias Supratentoriais/epidemiologia , Neoplasias Supratentoriais/cirurgia , População Negra , Neoplasias Encefálicas/mortalidade , Serviços Médicos de Emergência/estatística & dados numéricos , Acessibilidade aos Serviços de Saúde/estatística & dados numéricos , Humanos , Renda , Readmissão do Paciente/estatística & dados numéricos , Complicações Pós-Operatórias/epidemiologia , Reoperação/estatística & dados numéricos , Estudos Retrospectivos , Neoplasias Supratentoriais/mortalidade , Resultado do Tratamento , Estados Unidos/epidemiologia , População BrancaRESUMO
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.
Assuntos
Negro ou Afro-Americano , Procedimentos Neurocirúrgicos/métodos , Neoplasias Hipofisárias/cirurgia , População Branca , Idoso , Feminino , Hospitalização , Humanos , Masculino , Pessoa de Meia-Idade , Complicações Pós-Operatórias , Reoperação , Estudos Retrospectivos , Fatores de Risco , Fatores Socioeconômicos , Resultado do TratamentoRESUMO
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.