Simulated outcomes for durotomy repair in minimally invasive spine surgery.

Minimally invasive spine surgery (MISS) is increasingly performed using endoscopic and microscopic visualization, and the captured video can be used for surgical education and development of predictive artificial intelligence (AI) models. Video datasets depicting adverse event management are also valuable, as predictive models not exposed to adverse events may exhibit poor performance when these occur. Given that no dedicated spine surgery video datasets for AI model development are publicly available, we introduce Simulated Outcomes for Durotomy Repair in Minimally Invasive Spine Surgery (SOSpine).

Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation.

Background And Objectives: Assessment and feedback are critical to surgical education, but direct observational feedback by experts is rarely provided because of time constraints and is typically only qualitative. Automated, video-based, quantitative feedback on surgical performance could address this gap, improving surgical training. The authors aim to demonstrate the ability of Shannon entropy (ShEn), an information theory metric that quantifies series diversity, to predict surgical performance using instrument detections generated through deep learning.

BAF Complex Maintains Glioma Stem Cells in Pediatric H3K27M Glioma.

Unlabelled: Diffuse midline gliomas are uniformly fatal pediatric central nervous system cancers that are refractory to standard-of-care therapeutic modalities. The primary genetic drivers are a set of recurrent amino acid substitutions in genes encoding histone H3 (H3K27M), which are currently undruggable. These H3K27M oncohistones perturb normal chromatin architecture, resulting in an aberrant epigenetic landscape.

Validation of Machine Learning-Based Automated Surgical Instrument Annotation Using Publicly Available Intraoperative Video.

Background: Intraoperative tool movement data have been demonstrated to be clinically useful in quantifying surgical performance. However, collecting this information from intraoperative video requires laborious hand annotation. The ability to automatically annotate tools in surgical video would advance surgical data science by eliminating a time-intensive step in research.

Transcriptional Plasticity Drives Leukemia Immune Escape.

Unlabelled: Relapse of acute myeloid leukemia (AML) after allogeneic bone marrow transplantation has been linked to immune evasion due to reduced expression of major histocompatibility complex class II (MHCII) genes through unknown mechanisms. In this work, we developed CORENODE, a computational algorithm for genome-wide transcription network decomposition that identified a transcription factor (TF) tetrad consisting of IRF8, MYB, MEF2C, and MEIS1, regulating MHCII expression in AML cells. We show that reduced MHCII expression at relapse is transcriptionally driven by combinatorial changes in the expression of these TFs, where MYB and IRF8 play major opposing roles, acting independently of the IFNγ/CIITA pathway.

Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.

Major vascular injury resulting in uncontrolled bleeding is a catastrophic and often fatal complication of minimally invasive surgery. At the outset of these events, surgeons do not know how much blood will be lost or whether they will successfully control the hemorrhage (achieve hemostasis). We evaluate the ability of a deep learning neural network (DNN) to predict hemostasis control ability using the first minute of surgical video and compare model performance with human experts viewing the same video.

Code-free machine learning for object detection in surgical video: a benchmarking, feasibility, and cost study.

Objective: While the utilization of machine learning (ML) for data analysis typically requires significant technical expertise, novel platforms can deploy ML methods without requiring the user to have any coding experience (termed AutoML). The potential for these methods to be applied to neurosurgical video and surgical data science is unknown.

Methods: AutoML, a code-free ML (CFML) system, was used to identify surgical instruments contained within each frame of endoscopic, endonasal intraoperative video obtained from a previously validated internal carotid injury training exercise performed on a high-fidelity cadaver model.

Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success From Video.

Background: Deep neural networks (DNNs) have not been proven to detect blood loss (BL) or predict surgeon performance from video.

Objective: To train a DNN using video from cadaveric training exercises of surgeons controlling simulated internal carotid hemorrhage to predict clinically relevant outcomes.

Methods: Video was input as a series of images; deep learning networks were developed, which predicted BL and task success from images alone (automated model) and images plus human-labeled instrument annotations (semiautomated model).

Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications.

Importance: Surgical data scientists lack video data sets that depict adverse events, which may affect model generalizability and introduce bias. Hemorrhage may be particularly challenging for computer vision-based models because blood obscures the scene.

Objective: To assess the utility of the Simulated Outcomes Following Carotid Artery Laceration (SOCAL)-a publicly available surgical video data set of hemorrhage complication management with instrument annotations and task outcomes-to provide benchmarks for surgical data science techniques, including computer vision instrument detection, instrument use metrics and outcome associations, and validation of a SOCAL-trained neural network using real operative video.

A distinct core regulatory module enforces oncogene expression in KMT2A-rearranged leukemia.

Acute myeloid leukemia with KMT2A (MLL) rearrangements is characterized by specific patterns of gene expression and enhancer architecture, implying unique core transcriptional regulatory circuitry. Here, we identified the transcription factors MEF2D and IRF8 as selective transcriptional dependencies of KMT2A-rearranged AML, where MEF2D displays partially redundant functions with its paralog, MEF2C. Rapid transcription factor degradation followed by measurements of genome-wide transcription rates and superresolution microscopy revealed that MEF2D and IRF8 form a distinct core regulatory module with a narrow direct transcriptional program that includes activation of the key oncogenes MYC, HOXA9, and BCL2.

Use of surgical video-based automated performance metrics to predict blood loss and success of simulated vascular injury control in neurosurgery: a pilot study.

Objective: Experts can assess surgeon skill using surgical video, but a limited number of expert surgeons are available. Automated performance metrics (APMs) are a promising alternative but have not been created from operative videos in neurosurgery to date. The authors aimed to evaluate whether video-based APMs can predict task success and blood loss during endonasal endoscopic surgery in a validated cadaveric simulator of vascular injury of the internal carotid artery.

An CRISPR Screening Platform for Prioritizing Therapeutic Targets in AML.

CRISPR-Cas9-based genetic screens have successfully identified cell type-dependent liabilities in cancer, including acute myeloid leukemia (AML), a devastating hematologic malignancy with poor overall survival. Because most of these screens have been performed using established cell lines, evaluating the physiologic relevance of these targets is critical. We have established a CRISPR screening approach using orthotopic xenograft models to validate and prioritize AML-enriched dependencies , including in CRISPR-competent AML patient-derived xenograft (PDX) models tractable for genome editing.

A systematic review of virtual reality for the assessment of technical skills in neurosurgery.

Objective: Virtual reality (VR) and augmented reality (AR) systems are increasingly available to neurosurgeons. These systems may provide opportunities for technical rehearsal and assessments of surgeon performance. The assessment of neurosurgeon skill in VR and AR environments and the validity of VR and AR feedback has not been systematically reviewed.

Haplotype-resolved germline and somatic alterations in renal medullary carcinomas.

Background: Renal medullary carcinomas (RMCs) are rare kidney cancers that occur in adolescents and young adults of African ancestry. Although RMC is associated with the sickle cell trait and somatic loss of the tumor suppressor, SMARCB1, the ancestral origins of RMC remain unknown. Further, characterization of structural variants (SVs) involving SMARCB1 in RMC remains limited.

Improved surgeon performance following cadaveric simulation of internal carotid artery injury during endoscopic endonasal surgery: training outcomes of a nationwide prospective educational intervention.

Objective: Internal carotid artery injury (ICAI) is a rare, life-threatening complication of endoscopic endonasal approaches that will be encountered by most skull base neurosurgeons and otolaryngologists. Rates of surgical proficiency for managing ICAI are not known, and the role of simulation to improve performance has not been studied on a nationwide scale.

Methods: Attending and resident neurosurgery and otorhinolaryngology surgeons (n = 177) were recruited from multicenter regional and national training courses to assess training outcomes and validity at scale of a prospective educational intervention to improve surgeon technical skills using a previously validated, perfused human cadaveric simulator.

A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision.

Objective: Computer vision (CV) is a subset of artificial intelligence that performs computations on image or video data, permitting the quantitative analysis of visual information. Common CV tasks that may be relevant to surgeons include image classification, object detection and tracking, and extraction of higher order features. Despite the potential applications of CV to intraoperative video, however, few surgeons describe the use of CV.

Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas.

Purpose: Functional pituitary adenomas (FPAs) cause severe neuro-endocrinopathies including Cushing's disease (CD) and acromegaly. While many are effectively cured following FPA resection, some encounter disease recurrence/progression or hormonal non-remission requiring adjuvant treatment. Identification of risk factors for suboptimal postoperative outcomes may guide initiation of adjuvant multimodal therapies.

The SAFE-T upper endoscopy tool: a web-based application for the point-of-care evaluation of gastroenterology fellow performance in upper endoscopy.

Background: Attending assessment is a critical part of endoscopic education for gastroenterology fellows. The aim of this study was to develop and validate a concise, web-based assessment tool to evaluate real-time fellow performance in upper endoscopy.

Methods: We developed the Skill Assessment in Fellow Endoscopy Training (SAFE-T) upper endoscopy tool to capture both summative and formative feedback in a concise, five-part questionnaire.

Synthetic Lethal Interaction between the ESCRT Paralog Enzymes VPS4A and VPS4B in Cancers Harboring Loss of Chromosome 18q or 16q.

Few therapies target the loss of tumor suppressor genes in cancer. We examine CRISPR-SpCas9 and RNA-interference loss-of-function screens to identify new therapeutic targets associated with genomic loss of tumor suppressor genes. The endosomal sorting complexes required for transport (ESCRT) ATPases VPS4A and VPS4B score as strong synthetic lethal dependencies.