Citations

AbstractAbstract Background Central nervous system (CNS) tumors are a significant cause of morbidity and mortality in Sub-Saharan Africa (SSA). This project aimed to assess and map out the current neuro-oncology capacity in SSA, brain tumor registries, and biobanks. Methods This cross-sectional study utilized an online survey to gather data from healthcare professionals involved in CNS tumor care across SSA through the Society for Neuro-Oncology SSA (SNOSSA). The survey captured information on the availability of neuro-oncology practitioners, neuropathology, molecular diagnostics, cancer registries, and biobanking. Results A total of 145 respondents representing 70 healthcare institutions across 22 countries participated. Of the 70 institutions, specialties included neurosurgery (89%, 62/70), anatomic pathology (74%, 52/70), and medical oncology (71%, 50/70). For radiologic evaluation, 64% (45/70) of responding institutions reported having both CT and MRI. Access to molecular testing through immunohistochemistry or next-generation sequencing was available in 46% (32/70) of responding institutions. Multidisciplinary tumor boards were available for CNS cases in 86% (60/70) of responding institutions. Brain tumor-specific registries were available in 29% (20/70) of responding institutions, and 10% (7/70) maintained brain tumor biorepositories. Conclusion This study demonstrates that SSA has substantial potential in neuro-oncology care practice capacity and facilities. The most significant gaps were in neuropathology and molecular testing, which makes widespread implementation of the WHO 2021 diagnosis challenging. Understanding the prevalence of CNS tumor subtypes in SSA is difficult due to the low number of brain tumor registries and biobanks.

Study Design: Retrospective cohort analysis. Objective: To determine the effect of long-segment posterior cervical decompression and fusion (PCDF) on perioperative changes in vertebral Hounsfield Units (HUs) and the impact of these changes on radiographic outcomes and fusion status. Summary of Background Data: Low preoperative CT HUs have been associated with adverse outcomes after spinal fusion, including hardware complications and reoperation. No existing studies have investigated the preoperative to postoperative change in HUs after cervical spinal fusion. Stress shielding may decrease bone quality within the fusion construct. Whereas, altered junctional loading and increased segmental mobility may precipitate bony hypertrophy in the terminal and adjacent levels. Methods: All adult patients who underwent C2-T2 PCDF for myelopathy/ myeloradiculopathy at an academic center between 2013 and 2020 were retrospectively identified. Preoperative and postoperative HUs were assessed on axial CT images in the cranial, middle, and caudal C2, C6, T2, and T3 vertebral bodies, outside of the region of instrumentation and artifact by 2 independent reviewers. Paired t test compared preoperative to postoperative changes in HUs. Preoperative and long-term postoperative cervical alignment, fusion, and revision rates were assessed. Univariate and regression analysis compared patient factors and outcomes between groups with perioperative increase versus decrease in vertebral HUs. Results: One hundred patients were included. The average preoperative to postoperative change in HUs in the C2, C6, T2, and T3 vertebral bodies were −19.80 (P=0.008), −52.63 (P<0.001), 15.15 (P=0.014), and 9.41 (P=0.346), respectively. Perioperative increase in C2 HUs was predictive of increased postoperative distal junctional angle (β=3.41, P=0.048) and increased T1–T4 kyphosis (β=6.50, P=0.003). Perioperative increase in C6 HU predicted pseudoarthrosis (fusion odds ratio: 0.19, P=0.007). Conclusions: Significant perioperative decreases in C6 HUs within a long-fused construct may demonstrate stress shielding. The significant association between greater distal kyphosis and an increase in C2 HUs may be attributed to a stress riser effect. Level of Evidence: Level III.

Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

Abstract INTRODUCTION Few African American (AA) donors have been included in post mortem Alzheimer's disease (AD) studies compared to European‐ancestry (EA) individuals. METHODS We generated transcriptome‐wide bulk pre‐frontal cortex (PFC) gene expression data from 125 AA donors with neuropathologically determined AD and 82 AA controls. RESULTS Transcriptome‐wide significant differential expression was observed with 482 genes. The most significant, ADAMTS2 , showed 1.52 times higher expression in AD cases ( p = 2.96x10 −8 ). Comparison of findings with those from a recent gene expression study of EA brain donors revealed substantial concordance, including ADAMTS2 . Other associations not observed in EA results may be especially relevant to AD risk in the AA population. Examination of AA AD GWAS‐implicated variants identified several expression quantitative trait loci. CONCLUSION This first large‐scale AA brain AD gene expression study identified many differentially expressed genes, including ADAMTS2 , and supports gene expression as a molecular pathway underlying the impact of several AA AD risk variants. Highlights We performed the largest African American brain tissue Alzheimer's disease (AD) gene expression study. Expression differences for 482 genes, notably ADAMTS2 , were study‐wide significant. Many significant differentially expressed genes are involved in energy metabolism. Several previously known AD‐associated variants in African Americans are eQTLs. These results advance knowledge of the genetic basis of AD in the AA population.

Abstract Objective This study reports development and validation of a deep learning seizure detection algorithm for two‐channel subscalp electroencephalographic (EEG) recordings. Ultra‐long‐term monitoring of people with epilepsy may produce new insights into the timing and pattern of their seizures and may pave the way for novel therapeutic options. Although EEG is the accepted standard for epilepsy monitoring, ultra‐long‐term EEG recordings generate a massive quantity of data and are not reasonably reviewable in full by human readers. Methods The convolutional neural network‐bidirectional long short‐term memory (CNN‐BiLSTM) hybrid algorithm uses nine layers, operating on channel spectrograms 5 min in length with 50% overlap. Retrospective subscalp EEG data from 16 patients recorded at three centers were available for algorithm development and testing. EEG was recorded for a median of 63 days (range = 9–508), and a median of 17 seizures (range = 0–96) were recorded. Training data were augmented with scalp EEG seizures, and performance was benchmarked against a conventional spectral power classifier algorithm. We also evaluated an intrapatient training–testing approach where the algorithm was trained on the first 45% of data. Results The CNN‐BiLSTM trained on scalp EEG and subscalp EEG achieved an area under the receiver operating characteristic curve (AUROC) of .98 and an area under the precision–recall curve (AUPRC) of .50, which correspond to 94% sensitivity with 1.11 false detections per day. The same model trained on iEEG achieved only AUROC = .94 and AUPRC = .36. The conventional spectral band power detector achieved AUROC = .93 and AUPRC = .38. The CNN‐BiLSTM detector trained on the earliest half of the subscalp EEG data achieved AUROC = .93 and AUPRC = .37, corresponding to 87% sensitivity and 5.9 false detections per day. Significance High sensitivity and specificity are possible in automated seizure detection in two‐channel subscalp EEG data using a CNN‐BiLSTM framework. Performance of the detector is superior using subcutaneous EEG data for training rather than intracranial EEG, but addition of scalp EEG seizures for training was helpful.

Withdrawal Statement bioRxiv has withdrawn this manuscript after being contacted by the current corresponding author because they are unable to access the original data for revisions needed to finalize the preliminary results. If you have any questions, please contact the current corresponding author.

55 Background: Prior authorization (PA) processes are known to create administrative burdens and delays in care, but little is known about how these challenges impact rural oncology patients and practice settings. Methods: To understand the broad impact of prior authorization on rural oncology patients and practices, we conducted this study across three community oncology practices serving majority rural patient populations with distinct geography, healthcare infrastructure, and models of care. Sites included one critical access hospital system located in a highly-rural community (Lakewood Health System; Staples, Minnesota), a community-based hub-and-spoke hospital system-based practice located in a mid-sized community (Mayo Clinic Health System – Northfield; Northfield, Minnesota), and a standalone referral level III trauma center serving a highly rural patient base (Self Regional Healthcare; Greenwood, South Carolina). This retrospective cohort study examined all intravenous and subcutaneous cancer-directed therapy plans (no oral therapy) that required PA submission at these locations over a three-month period from October 1, 2024 to December 31, 2024. Results: 129 PA submissions were required during the study period. 82 were due to cancer-directed therapy agents (of which 14 were immunotherapy agents), and 47 were due to adjunctive therapies (of which 22, 16, and 9 were due to granulocyte stimulation, bone preservation, and anti-emesis agents, respectively). All 129 PA submissions ultimately resulted in approval of the desired medication or formulary-approved biosimilar. Delays from PA submission to approval ranged from 0 to 18 days. A total of 209 patient-days were delayed due to PA during the study period, which did not ultimately result in change of care or denial of care in any instance. Conclusions: At all sites included in this study, all PA submissions were eventually authorized. The PA process was found to introduce inconsistent and potentially avoidable delays for hundreds of patients and their care teams. These findings highlight the inefficiency of current PA processes among rural cancer populations and the practices that care for them, underscoring the need for ongoing efforts directed at state and federal PA reform.