Citations

Abstract Aims Left ventricular hypertrophy (LVH) is a strong predictor of cardiovascular disease. We previously compared supervised machine learning techniques to classify cardiac magnetic resonance (CMR)-derived LVH using electrocardiogram (ECG) and clinical variables in 37 534 UK Biobank participants, obtaining an area under the receiving operating curve (AUROC) of 0.85, but with limited specificity and requiring external validation. In this study, we develop a deep learning (DL) model to improve classification with external evaluation in the Study of Health in Pomerania (SHIP). Methods and results We analysed 12-lead ECGs of 48 835 participants from the UK Biobank imaging study. The dataset was split into a training set (70%), validation set (15%), and test set (15%) for performance evaluation. The model architecture was a fully convolutional network, for which the input was the participants’ median ECG and clinical variables and the predicted indexed left ventricular mass (iLVM) as the output. A subsequent logistic regression model was used to recalibrate iLVM predictions. In UK Biobank, 717 (1.5%) participants had CMR-derived LVH and the AUROC for the DL model was 0.97. The ECG components most predictive of LVH were the QRS complex and ventricular rate. The DL model outperformed our supervised algorithms, previous DL modelling efforts and clinical ECG benchmarks. There was modest generalizability of the DL model to 1423 participants in SHIP (AUROC 0.78), with differences in clinical profile, ECG acquisition, and CMR labelling as important factors. Conclusion Our findings support the feasibility of scalable DL-based screening tools for the prediction of LVH from the ECG, whilst highlighting the need for model development using larger datasets with greater diversity to ensure generalizability.

OBJECTIVE Brain metastases significantly impact neurocognitive function and overall survival. Stereotactic radiosurgery (SRS) is a cornerstone of treatment for patients with limited metastases and expected survival beyond 3 months. Despite current guidelines, up to 20% of patients with brain metastases undergoing SRS have been reported to die within 90 days. This study retrospectively evaluated prognostic factors associated with 90-day survival after SRS, aiming to improve patient selection. METHODS The authors retrospectively analyzed a cohort of 1546 patients who underwent Gamma Knife SRS for brain metastases at their institution between 2015 and 2023. One hundred seventy patients who survived less than 90 days after SRS were identified and case matched to 170 patients who survived over 90 days. Measured variables included patient demographic characteristics, tumor characteristics, treatment history, functional status, and control of the primary cancer. The authors modeled post-SRS 90-day survival using binomial and multivariate logistic regression. RESULTS Multivariate analysis highlighted Karnofsky Performance Score (KPS) < 70 (OR 17.4, p < 0.001), prior whole-brain radiation (OR 6, p = 0.004), and focal neurological deficits (OR 3.02, p = 0.003) as significant predictors of poor survival, whereas CNS progression before SRS (OR 0.22, p < 0.001) and control of systemic cancer (OR 0.556, p = 0.002) were associated with survival < 90 days. The predictive model demonstrated acceptable performance with an area under the curve (AUC) of 0.85, accuracy of 80%, sensitivity of 87%, and specificity of 71%. CONCLUSIONS Key predictors of 90-day survival after SRS for brain metastases include functional status (KPS), control of systemic cancer, CNS progression status, and focal neurological deficits. These findings are complementary factors that can assist in making decisions and SRS patient selection.

BACKGROUND Recently, the distribution of subparaneurial ganglion cysts involving the common peroneal and tibial nerves was described based on MRI analysis. Crossover in extreme cases was observed at the sciatic bifurcation through a mechanism comparable to a geyser, enabled by fenestrations in the epi- and paraneurium at the level of the bifurcation. The goal of this study was to investigate whether the geyser theory for crossover holds for other locations. OBSERVATIONS The authors’ prospectively maintained database of > 1100 cases of intraneural ganglion cysts in the world’s literature was queried for the existence of subparaneurial ganglion cysts at locations other than the knee. A previously unrecognized case involving the ulnar nerve originating from the elbow joint was found. Reinterpretation of the patient’s MRI studies showed circumferential subparaneurial distribution of a cyst over a long distance. Based on their previously reported geyser theory, the authors wondered if crossover had occurred at the level of the medial cord. A fenestration in the epi- and paraneurium at this level was confirmed on histological analysis of a normal cadaveric specimen. LESSONS This illustrative case shows that the geyser theory also holds in other locations. Surgical treatment of these subparaneurial cysts still follows the principles of the unified articular theory. https://thejns.org/doi/10.3171/CASE25671

Early allograft failure (EAF) after living donor liver transplantation (LDLT) remains a clinical challenge. Existing prediction models developed for deceased donor transplantation poorly apply to LDLT due to distinct surgical and physiological factors. This study identifies clinical determinants of EAF and develops an LDLT-specific prediction model. We conducted a multicenter retrospective cohort study from 17 high-volume LDLT centers (January 2016–December 2020) with external validation at a tertiary center in Saudi Arabia (January 2015–December 2022). The primary outcome was EAF (graft loss or patient death ≤90 d). Multivariable mixed-effects logistic regression identified preoperative/intraoperative risk factors. The EAGLE-LDLT model was constructed using postoperative laboratory values. Performance was compared against established models (EAD, MEAF, A2ALL) using ROC analysis and decision curve analysis. The development cohort included 2944 adult LDLT recipients (67.7% male; median age 55 y; median MELD 14) with a 5.5% EAF rate. External validation included 1020 recipients (median MELD 21, 6.7% EAF). Independent risk factors for EAF were MELD (OR 1.06, 95% CI 1.04–1.08), donor BMI (OR 1.05, 95% CI 1.00–1.10), portal vein thrombosis (OR 1.73, 95% CI 1.13–2.63), and hepaticojejunostomy (OR 1.58, 95% CI 1.06–2.36). The EAGLE-LDLT model incorporating peak ALT (>468 U/L), peak INR (>1.9), and bilirubin (>3.5 mg/dL) and INR (>1.3) at POD7, demonstrated superior discrimination (AUC=0.81) compared with MEAF (AUC=0.77, p =0.004), EAD (AUC=0.67, p <0.001), and A2ALL (AUC=0.65, p <0.001). EAGLE-LDLT achieved balanced sensitivity (75.0%) and specificity (73.7%), effectively stratifying patients into high-risk (15% of patients; 40.4% EAF incidence) and low-risk groups. Preoperative and intraoperative clinical factors predict EAF in LDLT. The EAGLE-LDLT model accurately identifies LDLT recipients at the highest risk for EAF postoperatively.

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide, accounting for approximately 10% of all cancer cases. Despite the proven effectiveness of conventional screening modalities such as colonoscopy and fecal immunochemical testing (FIT), their invasive nature, high cost, and limited patient compliance hinder widespread adoption. Recent advancements in artificial intelligence (AI) and bowel sound-based signal processing have enabled non-invasive approaches for gastrointestinal diagnostics. Among these, bowel sound analysis—historically considered subjective—has reemerged as a promising biomarker using digital auscultation and machine learning. Objective: This review explores the potential of AI-powered bowel sound analytics for early detection, screening, and characterization of colorectal cancer. It aims to assess current methodologies, summarize reported performance metrics, and highlight translational opportunities and challenges in clinical implementation. Methods: A narrative review was conducted across PubMed, Scopus, Embase, and Cochrane databases using the terms colorectal cancer, bowel sounds, phonoenterography, artificial intelligence, and non-invasive diagnosis. Eligible studies involving human bowel sound-based recordings, AI-based sound analysis, or machine learning applications in gastrointestinal pathology were reviewed for study design, signal acquisition methods, AI model architecture, and diagnostic accuracy. Results: Across studies using convolutional neural networks (CNNs), gradient boosting, and transformer-based models, reported diagnostic accuracies ranged from 88% to 96%. Area under the curve (AUC) values were ≥0.83, with F1 scores between 0.71 and 0.85 for bowel sound classification. In CRC-specific frameworks such as BowelRCNN, AI models successfully differentiate abnormal bowel sound intervals and spectral patterns associated with tumor-related motility disturbances and partial obstruction. Distinct bowel sound-based signatures—such as prolonged sound-to-sound intervals and high-pitched “tinkling” proximal to lesions—demonstrate the physiological basis for CRC detection through bowel sound-based biomarkers. Conclusions: AI-driven bowel sound analysis represents an emerging, exploratory research direction rather than a validated colorectal cancer screening modality. While early studies demonstrate physiological plausibility and technical feasibility, no large-scale, CRC-specific validation studies currently establish sensitivity, specificity, PPV, or NPV for cancer detection. Accordingly, bowel sound analytics should be viewed as hypothesis-generating and potentially complementary to established screening tools, rather than a near-term alternative to validated modalities such as FIT, multitarget stool DNA testing, or colonoscopy.

Objective: To establish international benchmark values for relevant outcome parameters in robotic Whipple. Summary Background Data: For safe adoption of surgical innovation, robust quality control is essential. Benchmarking is a validated tool for assessing surgical performance. Recent international consensus identified establishing benchmark values for robotic Whipple as top priority. Methods: We analyzed consecutive patients undergoing robotic Whipple between 2020-2023 with a minimum one-year follow-up. Reference centers were required to perform ≥15 cases/year, be scientifically active in the field, and maintain a prospective database. Benchmark criteria included benign or resectable malignant disease without neoadjuvant therapy, arterial resection, major co-morbidities, or significant previous abdominal surgery. Benchmarks were established for 13 outcome parameters. Result: The benchmark cohort comprised 418 patients from 12 centers across four continents. Benchmark values were: conversion rate ≤4.3%, transfusion rate ≤2.1%, 6-month mortality ≤2.2%, major complications ≤23.2%, and CCI® ≤20.9. Clinically relevant pancreatic fistula (grade B/C) and hemorrhage (grade B/C) rates were ≤23.6% and ≤12.7%, respectively. For pancreatic ductal adenocarcinoma (n=123), the benchmark for lymph node yield was ≥20. Higher surgical difficulty was associated with increased overall postoperative morbidity (R 2 =0.38, P =0.019), higher center caseload with reduced pancreas-specific complications (R 2 =0.28, P =0.044). Independent POPF predictors included duct diameter ≤4 mm (OR 1.37, 95% CI: 1.03, 1.82), anticoagulation (OR 2.45, 95% CI: 1.47, 3.99), and indication other than PDAC (OR 2.33, 95% CI: 1.68, 3.27). Conclusions: This study establishes the first international benchmarks for robotic Whipple, demonstrating oncologic outcomes and morbidity comparable to open surgery with the benefits of minimally invasive surgery.

This guideline has been updated. This ASCO Living Guideline is reviewed on an eight-week cycle. Please see the online dynamic version for the latest updates. Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating clinician and do not account for individual variation among patients. See appendix for disclaimers and other important information ( Appendix 1 and Appendix 2 ). Updates are published regularly and can be found at www.asco.org/genitourinary-cancer-guidelines .

OBJECTIVE: Volatile anesthetics (VAs) are gaining renewed interest as a sedation strategy in the intensive care, offering an alternative to traditional IV agents. VAs provide several pharmacologic advantages, including rapid onset and offset, minimal systemic metabolism, and favorable recovery profiles. Advances in delivery systems enabled the safe and practical administration of volatile agents in the ICU. Thus, we aimed to describe the pharmacology and safety aspects of inhaled agents as well as the systems designed to deliver VAs in the ICU. DATA SOURCES: Relevant literature was identified through PubMed and MEDLINE databases. STUDY SELECTION: Original research, review articles, commentaries, and guidelines addressing safety, efficacy, and use of VAs in adult ICU patients were included. DATA EXTRACTION: Studies were reviewed by the authors, with key findings summarized and organized by pharmacologic properties, delivery systems, and safety domains. DATA SYNTHESIS: VAs are halogenated hydrocarbons whose mechanism of action is not fully understood. Although the CNS is the primary site of action, the end-tidal concentration of exhaled anesthetic is used to monitor clinical effects such as immobility to a noxious stimulus. Inhaled agents have unique pharmacokinetics, minimal metabolisms, and distinct recovery. The side effect profile is also unique, with malignant hyperthermia being the most feared, yet rare complication. Two systems for inhalational sedation delivery are available internationally, with one currently under evaluation in the United States. The systems are composed of a miniature vaporizer, delivery controller, and a monitor. The systems have distinct safety considerations, such as tidal volume limits. CONCLUSIONS: VAs can be used as sedative agents in the ICU. This article comprehensively reviews the pharmacology of VAs along with their safety profile and describes the structure and function of miniature vaporizers currently available on the world market.

Bone scintigraphy is a valuable adjunct to cross-sectional imaging and can be used to evaluate disease extent in multifocal processes such as blastic osseous metastatic disease, provide diagnostic information to aid in problem solving in challenging cases, and serve as the primary diagnostic modality for diseases that manifest with pathognomonic scintigraphic appearances.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH’s Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article’s cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH’s GSS principles.