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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.

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.

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.

Understanding the immune microenvironment in pediatric low-grade glioma (pLGG) patients may help in identification of the patients who benefit from anti-tumor immunotherapies. However, surgical resection is not feasible for many pLGG tumors in certain anatomical locations. Therefore, developing non-invasive tools that characterize the tumor microenvironment prior to therapeutic interventions could contribute to stratification and enrollment of the patients into relevant clinical trials. In this work, we derived radiomic signatures of immune profiles (radioimmunomics) based on machine learning (ML) analysis of readily available conventional MRI scans. Transcriptomic data for a cohort of 197 subjects was retrospectively collected from Open Pediatric Brain Tumor Atlas (OpenPBTA). The patients were categorized into three groups (Group1-3) based on their immunological profiles using consensus clustering algorithm. This analysis revealed greater immune cell infiltration in non-BRAF mutated pLGGs. Group1 showed more enrichment in M1 macrophages, and microenvironment and immune scores compared to Group2 and Group3. Elevated tumor inflammation score (TIS), as a predictor of clinical response to anti-PD-1 blockade, was observed in Group1 compared to Group2 (p= 1.4e-7) and Group3 (p= 0.0054). Radiomic features, including volumetric, morphologic, histogram, and texture descriptors, were extracted from the segmented tumor regions on multiparametric MRI (mpMRI) scans of 71 (of 197) patients. Multivariate ML models were trained to predict the three immunological groups based on radiomic features using cross-validated random forest classifier along with recursive feature elimination, which yielded AUC of 0.72 for this multi-class classification problem. Our findings indicate the presence of distinct immunological groups in pLGG tumors, with possibly more favorable response to immunotherapies in Group1 tumors. Furthermore, we developed radioimmunomic signatures based on pre-operative conventional mpMRI that can potentially stratify the patients based on their immune tumor microenvironment. Based on these initial promising results, we are exploring additional features to increase the accuracy of radioimmunomics model.

Volumetric measurements of whole tumor and its components on MRI scans, facilitated by automatic segmentation tools, are essential to reduce inter-observer variability in monitoring tumor progression and response assessment for pediatric brain tumors. Here, we present a fully automatic segmentation model based on deep learning that reliably delineates the tumor components recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group for evaluation of treatment response. Multi-parametric MRI (mpMRI) scans (T1-pre, T1-post, T2, and T2-FLAIR), acquired on multiple MRI scanners with different field strengths and vendors, for a cohort of 218 pediatric patients with a variety of histologically confirmed brain tumor subtypes were collected. The mpMRI scans were co-registered and manually segmented by experienced neuroradiologists in consensus to identify the tumor subregions including the enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED) regions. A convolutional neural network model based on DeepMedic architecture was trained using mpMRI scans as the inputs for segmentation of the whole tumor and subregions. The trained model showed excellent performance in segmentation of the whole tumor, as suggested by median dice of 0.90/0.85 for validation (n = 44)/independent test (n = 22) sets. ET and non-enhancing components (union of NET, CC, and ED) were segmented with median dice scores of 0.78/0.84 and 0.76/0.74 for validation/test sets, respectively. The automated and manual segmentations demonstrated strong agreement in estimating VASARI (Visually AcceSAble Rembrandt Images) MRI features with Pearson’s correlation coefficient R > 0.75 (p < 0.0001) for ET, NET, CC, and ED components. Our proposed automated segmentation method developed based on MRI scans acquired with different protocols, equipment, and from a variety of brain tumor subtypes, shows potential application for reliable and generalizable volumetric measurements which can be used for treatment response assessment in clinical trials.

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. 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. 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. These results suggest that LACE + may be a suitable prediction model for important patient outcomes after urologic surgery.

Pediatric low-grade glioma (pLGG) encompasses a variety of tumor subtypes with heterogeneous treatment response and relatively long progression-free survival (PFS). Radiomics may serve as a non-invasive and in-vivo tool for early prediction of PFS as a surrogate marker for treatment response and to objectively gauge the efficacy of novel treatment strategies. Here, we present a multivariate model based on radiomic features and clinical variables for risk stratification of pLGGs in terms of PFS and seek associations of the predicted risk groups and mutations in key molecular markers using data from PedCBioportal. Pre-operative multi-parametric MRI scans (T1-pre, T1-post, T2, T2-FLAIR) of 129 patients with newly diagnosed pLGG (median age, 7.76, range, 0.35-19.58 years; median PFS, 28.5, range, 1.1-124.8 months) were collected and quantitative radiomic features (n = 881) were extracted. A multivariate Cox proportional hazard’s (Cox-PH) regression model was fitted based on clinical (age, sex, and extent of tumor resection) and radiomic variables using 4-fold cross-validation. A subset of radiomic features (n = 27) that were most predictive of PFS was selected by applying Elastic Net regularization penalty during Cox-PH model fitting. High-, medium- and low-risk groups were determined based on model predictions. Cox-PH modeling showed excellent performance for prediction of PFS as suggested by the concordance index of 0.78. Radiogenomic assessment (data available in 94/129 patients) showed more enrichment of mutations in NF1 and RB1 genes in the high-risk group, as compared to the low- and medium-risk groups. We showed the potential value of radiomics in providing upfront prediction of PFS, which may further be used as an added treatment arm for early assessment of treatment response of the pLGG patients enrolled in the clinical trials. In the next step of this work, we will expand the cohort and cross-validate these results in an external cohort.

Recent studies have shown preliminary evidence for differentiation of the tumor microenvironment (TME) and immune landscape between molecularly-defined medulloblastoma (MB) subtypes. Identifying radiological correlates of these TME patterns could establish a non-invasive method of immune profile characterization for guiding patient-centered therapies. Here, we examine immune profiles between MB subtypes using data from Open Pediatric Brain Tumor Atlas (OpenPBTA), and their relationship to tumor measurements from pre-operative MRIs. We identified a retrospective cohort of 94 pediatric MB patients with available molecular subtyping and immune profiles (36 cell types) from bulk gene expression data. A random forest analysis was used to classify the four MB subtypes based on immune profiles. Four cell types had high impact on classification performance: plasmacytoid dendritic cells (PDC; 25.8% accuracy decrease when randomized), hematopoietic stem cells (HSC; 21.9%), plasma B cells (20.3%), and cancer associated fibroblasts (18.8%). Pairwise comparisons revealed SHH and WNT tumors had significantly higher numbers of fibroblasts and HSCs compared to Group3/Group4. We also found novel evidence for significantly lower amounts of plasma B cells in the SHH group, and high PDC levels in Group4, followed by Group3, and low PDC in SHH/WNT. Multi-parametric MRI scans for 39 patients were used to segment tumor volumes. Overall tumor volume was significantly correlated with composite stroma scores (R = 0.34, p = 0.036). Additionally, patients with higher volumes of gadolinium contrast-enhancing compared to non-enhancing components had higher immune (R = 0.42, p = 0.009) and microenvironment (summed immune and stromal cell types; R = 0.44, p = 0.006) scores, regardless of their molecular subtype. Together, our results demonstrate: (1) the use of rich immune profiles for differentiating molecular subtypes of MB and their unique TME characterization; and (2) initial evidence for radiological correlates of these profiles based on pre-operative imaging collected through standard practices.

PURPOSE: Treatment response is heterogeneous among patients with pediatric low-grade glioma (pLGG), the most frequent childhood brain tumor. Upfront prediction of progression-free survival (PFS) may facilitate more personalized treatment planning and improve outcomes for the pLGG patients. In this work, we explored the additive value of radiomics to clinical measures for prediction of PFS in pLGGs. We further sought associations between the derived risk groups and underlying alterations in key genomic and transcriptomic variables. METHODS: Quantitative radiomic features were extracted from pre-operative multi-parametric MRI scans (T1, T1-post, T2, T2-FLAIR) of 96 patients with newly diagnosed pLGG (median age, 8.59, range, 0.35-18.87 years; median PFS, 25.23, range, 3.03-124.83 months). Multivariate Cox proportional hazard’s (Cox-PH) regression models were fitted using 5-fold cross-validation on a training cohort of 68 subjects and tested on 28 patients. Three models were generated using (1) only clinical variables (age, sex, and extent of tumor resection), (2) radiomic features, and (3) clinical and radiomic variables. The dimensionality of radiomic features in Cox-PH models was reduced by applying Elastic Net regularization penalty to identify a subset of variables that are most predictive of PFS. The patients were then stratified into three groups of high, medium, and low-risk based on model predictions. RESULTS: Cox-PH modeling resulted in a concordance index (c-index) of 0.55 for clinical data, 0.65 for radiomics, and 0.73 for a combination of clinical and radiomic variables, highlighting the additive value of radiomics to the readily available clinical information in prediction of PFS. Radiogenomic assessments revealed significant differences in expression of BRAF, NF1, TSC1, ALK (p

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.

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.

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.

Background The present study assesses the influence of race on patient outcomes in a brain tumor surgery population. Methods Coarsened exact matching (CEM) was used to retrospectively analyze 1700 supratentorial brain tumor procedures over a six-year period (June 07, 2013 to April 29, 2019) at a single, multi-hospital academic medical center. Outcome measures included readmission, mortality, emergency room visits and reoperation. Results McNemar’s test (mid-P) showed no significant difference in 90-day mortality between the two races (p = .3018). However, there was a significant difference in 90-day readmissions between the two races (p = 0.0237). There was no significant difference in 90-day ER Visits (p = 0.0579), 90 day return to surgery after index admission (p = 0.6015), or return to surgery within 90 days (p = .6776) between the two races. There was also no significant difference in return to surgery for the duration of the follow-up period (p = .8728). Conclusion This study suggests that race alone does not result in disparate outcomes, although there was an associated difference in 90-day post-surgical readmissions. Despite coarsened exact matching persistent differences in median household income may play a role in the disparate outcome noted.

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.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.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.These results suggest that LACE + may be a suitable prediction model for important patient outcomes after urologic surgery.

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.

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-β1) has a significant role in inflammation and apoptosis in myriads of other pathophysiological conditions, the precise function of increased TGF-β1 after mTBI is unknown. In this study, our objective is to study the physiological relevance and associated mechanisms of TGF-β1-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-β1 activation in mTBI. Our study demonstrated that the activation of TGF-β1 in mTBI correlated with the induction of free radical generating enzyme NADPH oxidase 1 (NOX1). Further, using TGF-β type I receptor (TGF-βRI) inhibitor SB431542 and transfection of TGF-β1 siRNA and TGF-β antagonist Smad7, we established the neuroinflammatory and apoptotic role of TGF-β1 in mTBI. Inhibition of TGF-βRI or TGF-β1 diminished TGF-β1-induced inflammation and apoptosis. Further, the enhanced TGF-β1 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-1β and TNF-α and apoptotic cell death in neurons. In conclusion, this study could establish the significance of TGF-β1 in transforming the pathophysiology of mTBI into secondary injury.

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.