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Association between body composition and recurrence in stage II-III colon cancer: a retrospective cohort study

Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine management. Patients with advanced disease often become cachectic, losing skeletal muscle mass and density, as well as subcutaneous fat. CT can be used to assess body composition by measuring skeletal muscle area, density and subcutaneous and visceral fat. We hypothesise that evidence of sarcopenia or myosteatosis at diagnosis is associated with an increased risk of cancer recurrence. Patients discussed at the…

Clinical Nutrition ESPEN · Health

Evidence-backed gain

An Interpretable Machine Learning Framework with Clinical Nomogram for Predicting In-Hospital Mortality in Acute Ischemic Stroke Using High-Granularity Bedside Data

This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency department data collected within the first 24 hours to predict in-hospital mortality in acute ischemic stroke (AIS). We analyzed a retrospective cohort of 5,014 adult AIS patients from three tertiary academic centers (2019-2023). Centers A and B (n=3,512) formed the development cohort; Center C (n=1,502) served as the external validation cohort. Sixty-three predictors across seven dom…

Health

Evidence-backed gain

TrialScout links published results to trial registrations using a large language model

Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find them. Summary results of clinical trials are often not published in trial registries, and publications containing trial results are often not explicitly linked to their respective trial registrations. Finding these results is important to researchers, systematic reviewers, research funders, regulators, clinical practitioners, and patients. We developed TrialScout, a computer program that uses a large langua…

Health

Evidence-backed gain

Noninvasive Profiling of the Glioma Vascular Microenvironment via 7T MRI: Decoding Angiogenic Signatures for Isocitrate Dehydrogenase and World Health Organization Grade Differentiation

Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microenvironment promotes tumor progression. A noninvasive screening tool capable of mapping tumor vascularity may assist in preoperative grading and subtyping of gliomas. To evaluate 7T susceptibility-weighted imaging (SWI) for differentiating glioma isocitrate dehydrogenase (IDH) status and World Health Organization (WHO) grade based on vascular microenvironment features. Retrospective. Among 218 patients wit…

Health

Evidence-backed gain

EndoVLM: A Vision-Language Assistant for Gastrointestinal Endoscopy

Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for efficient and accurate computer-aided diagnosis of gastrointestinal diseases. To address this, we propose EndoVLM (Endoscopy Vision-Language Model), a specialized visual question-answering assistant for gastroenterology. EndoVLM introduces ConvNeXt as a hierarchical visual encoder to replace traditional ViTs (Vision Transformers), inherently compressing high-resolution gastrointestinal endoscopy images in…

Health

Evidence-backed gain

Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulated follicular variant of papillary thyroid carcinoma (IEFVPTC) are diagnostically challenging thyroid neoplasms with overlapping clinical and molecular characteristics. Although artificial intelligence has shown promise for diagnostic support in radiology and histopathology, its application to gross pathology remains unexplored. This study analyzed gross pathology photographs from 87 patients (43 with NIF…

Health

Evidence-backed gain

AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions

Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decision-making, but its contribution varies by application and methodological maturity. RWE remains limited by challenges in data quality, population selection, treatment characterization, outcome assessment, and statistical methodology. Machine learning and generative AI (genAI), combined with causal inference frameworks, may address these challenges. We review applications, limitations, including reproducibil…

Policy

Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation

Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates m…

Health

Naïve adaptive immune receptor repertoires in celiac disease assessed by machine learning; impact of the HLA-DQ2.5 allotype on the TCR repertoire

The adaptive immune receptor repertoire (AIRR) - the collection of an individual's B-cell and T-cell receptors (BCRs and TCRs, respectively) - encodes cumulative immune history and is shaped by both germline genetics and environmental exposures. Skewed repertoires have been linked to infections, vaccination responses and autoimmune diseases such as celiac disease (CeD) where biased usage of immunoglobulin and T-cell receptor genes reactive to disease relevant antigens has been reported. Motivated by evidence tha…

Health

Surprising AI breakthroughs raise soul-searching questions for mathematicians | Letter

I share Kasra Rafi and Bruce Schneier’s impression that recent mathematical breakthroughs by AI consist in clever recombination of existing ideas, not development of truly novel theory (No, AI doesn’t mean the end of mathematics – at least not yet, 25 August). The question is: what happens to mathematics if this changes? Like many mathematicians, I have done much soul-searching in recent weeks, especially since a key problem in my own field of group theory (the existence of non-sofic groups) was solved this mont…

Science

Surprising AI breakthroughs raise soul-searching questions for mathematicians | Letter

HARMONY IN THE SCALE: PROTECTION OF MUSICAL WORKS IN THE DIGITAL AGE

The article is devoted to a comprehensive analysis of modern problems of protecting musical works in the context of the rapid development of digital technologies. The author considers the transformation of the music industry from analog media to online platforms, focusing on the scale of legal and illegal distribution of content, as well as the emergence of new threats in the form of streaming piracy, illegal copying, the use of AI, and digital remixing. The work presents the evolution of the copyright system —…

Media & Arts

Pulmonary Artery-to-Vein Volume Difference: A New Imaging Biomarker for Risk Stratification in Acute Pulmonary Embolism

Rationale and objectives To propose a new imaging biomarker, the pulmonary artery-to-vein volume difference (PAVVD), and evaluate its efficacy in risk stratification of acute pulmonary embolism (APE) compared with traditional computed tomography pulmonary angiography (CTPA) parameters, as well as the impact of chronic pulmonary disease (CPD). Materials and methods This retrospective study included 134 patients with APE (high/intermediate-high risk, n = 46; intermediate-low/low risk, n = 88) from April 2023 to Ma…

Health

Pulmonary Artery-to-Vein Volume Difference: A New Imaging Biomarker for Risk Stratification in Acute Pulmonary Embolism

International Application of Artificial Intelligence for Lesion Detection on Digital Breast Tomosynthesis: Comparing Western and Eastern Databases

Rationale and objectives The international application of artificial intelligence (AI) for lesion detection based on digital breast tomosynthesis (DBT) is limited due to disease variations among populations. We hypothesized that lesion detection models trained on either the Western or Eastern DBT dataset would exhibit reduced performance on another dataset. We proposed transfer learning to enhance lesion detection across DBT databases. Materials and methods The Western database (94 patients) was obtained from th…

Health

International Application of Artificial Intelligence for Lesion Detection on Digital Breast Tomosynthesis: Comparing Western and Eastern Databases

Predicting Synchronous Liver Metastasis in Pancreatic Cancer Using CT Radiomics and Clinical Features: A Machine Learning Approach

Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis (LM) in pancreatic cancer (PC), we developed and validated machine learning models integrating clinical and computed tomography (CT) radiomics features, and compared the performance and interpretability of linear (linear discriminant analysis [LDA]) versus nonlinear (multilayer perceptron [MLP]) architectures. Materials and methods This retrospective study enrolled 340 patients with pancreatic ductal aden…

Health

Attitudes, perceptions, and UTAUT-based factors influencing the acceptance of medical artificial intelligence among Chinese oncology healthcare professionals: a national cross-sectional survey

Objectives To conduct a nationwide survey among professionals working in oncology departments in China to investigate their attitudes, perceptions, and experiences regarding medical artificial intelligence (AI), and to explore and compare the factors influencing AI behavioral intention (BI; willingness to adopt AI) between physicians and nurses using the Unified Theory of Acceptance and Use of Technology (UTAUT). Materials and methods A nationwide cross-sectional survey was conducted among professionals in oncol…

Health