Sex-Specific Prognostic Value of Automated Epicardial Adipose Tissue Quantification on Serial Lung Cancer Screening Chest CT.

Aims: Epicardial adipose tissue (EAT) is a metabolically active fat depot associated with coronary atherosclerosis and cardiovascular (CV) risk. While EAT is a known prognostic marker in lung cancer screening, its sex-specific prognostic value remains unclear. This study investigated sex differences in the prognostic utility of serial EAT measurements on low-dose chest CTs.

Foundation model embeddings for quantitative tumor imaging biomarkers.

Foundation models are increasingly used in medical imaging, yet their ability to extract reliable quantitative radiographic phenotypes of cancer across diverse clinical contexts lacks systematic evaluation. Here, we introduce TumorImagingBench, a curated benchmark comprising six public datasets (3,244 scans) with varied oncological endpoints. We evaluate ten medical imaging foundation models, representing diverse architectures and pre-training strategies developed between 2020 and 2025, assessing their performance in deriving deep learning-based radiographic phenotypes.

Association of Epicardial Adipose Tissue Changes on Serial Chest CT Scans with Mortality: Insights from the National Lung Screening Trial.

Background Individuals eligible for lung cancer screening with low-dose CT face a higher cardiovascular mortality risk. Purpose To investigate the association between changes in epicardial adipose tissue (EAT) at the 2-year interval and mortality in individuals undergoing serial low-dose CT lung cancer screening. Materials and Methods This secondary analysis of the National Lung Screening Trial obtained EAT volume and density from serial low-dose CT scans using a validated automated deep learning algorithm.

Skeletal muscle adiposity, coronary microvascular dysfunction, and adverse cardiovascular outcomes.

Background And Aims: Skeletal muscle (SM) fat infiltration, or intermuscular adipose tissue (IMAT), reflects muscle quality and is associated with inflammation, a key determinant in cardiometabolic disease. Coronary flow reserve (CFR), a marker of coronary microvascular dysfunction (CMD), is independently associated with body mass index (BMI), inflammation and risk of heart failure, myocardial infarction, and death. The relationship between SM quality, CMD, and cardiovascular outcomes is not known.

Automated anonymization of radiology reports: comparison of publicly available natural language processing and large language models.

Purpose: Medical reports, governed by HIPAA regulations, contain personal health information (PHI), restricting secondary data use. Utilizing natural language processing (NLP) and large language models (LLM), we sought to employ publicly available methods to automatically anonymize PHI in free-text radiology reports.

Materials And Methods: We compared two publicly available rule-based NLP models (spaCy; NLP, accuracy-optimized; NLP, speed-optimized; iteratively improved on 400 free-text CT-reports (test set)) and one offline LLM approach (LLM-model, LLaMa-2, Meta-AI) for PHI-anonymization.

Foundation model for cancer imaging biomarkers.

Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce.

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers.

Background: Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium.

Methods: We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.

Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging.

Foundation models represent a recent paradigm shift in deep learning, where a single large-scale model trained on vast amounts of data can serve as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labeled datasets are often scarce.

Frequency-Domain-Based Structure Losses for CycleGAN-Based Cone-Beam Computed Tomography Translation.

Research exploring CycleGAN-based synthetic image generation has recently accelerated in the medical community due to its ability to leverage unpaired images effectively. However, a commonly established drawback of the CycleGAN, the introduction of artifacts in generated images, makes it unreliable for medical imaging use cases. In an attempt to address this, we explore the effect of structure losses on the CycleGAN and propose a generalized frequency-based loss that aims at preserving the content in the frequency domain.

Artificial Intelligence in Radiation Therapy.

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow.

Morpho-physiological diversity between Lingzhi or Reishi medicinal mushroom Ganoderma lucidium (W. Curt.:Fr.) P. Karst. and G. carnosum pat.

Two weakly differentiated taxa, Ganoderma lucidum and G. carnosum, were compared in their sufficient morphological and physiological features. The obtained results showed that dimensions of basidiospores and pileocystidia were insignificantly different, while pore shape and dimensions have shown greater diversity with average diameter of 138.