Cardiovascular

Beyond the Coronaries: Utility of Extra-Coronary Calcification for Cardiovascular Risk Assessment in Women.

PURPOSE OF REVIEW: Globally, cardiovascular disease (CVD) is the leading cause of mortality in women. Traditional risk calculators underestimate atherosclerotic cardiovascular disease risk (ASCVD), particularly in women classified as low or intermediate risk. While coronary artery calcium scoring improves risk stratification, it remains underutilized in practice. Extra-coronary calcification (ECC)...

DREAMER-S: Deep leaRning-Enabled Attention-based Multiple-instance approaches with Explainable Representations for Spatial biology.

Identifying image features that associate strongly with diagnostic or prognostic classes in large-scale, multi-channel spatial imaging is challenging without pixel-level annotations. We present DREAMER-S, an attention-based multiple-instance learning (MIL) framework that, using only image- or slide-level labels, learns spatial features within 3D imaging hypercubes that are most informative for dow...

Online adaptive radiotherapy: International strategies for AI-enabled workflow efficiency and radiation therapist-led delivery for sustainable practice.

Online adaptive radiotherapy (oART) represents a significant advancement in personalised radiation cancer treatment, offering improved daily dose to t...

Exploring osteoporosis risk in breast cancer patients after comprehensive treatment via explainable artificial intelligence algorithms.

BACKGROUND: Osteoporosis is a common complication among long-term breast cancer (BC) survivors. Assessment of osteoporosis risk of patients before the...

Development of a clinical prediction model using digital device data for interstitial lung disease in patients with unresectable stage III non-small cell lung cancer treated with durvalumab.

BACKGROUND: Early detection of interstitial lung disease including radiation pneumonitis (ILD/RP) is crucial in consolidative durvalumab therapy after...

Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer.

PURPOSE: 18 F-FDG PET/CT is the standard modality for monitoring treatment response in metastatic breast cancer. This study aims to evaluate the predi...

Generating Labeled Low-Heterogeneity Transcriptomes Using CRISPRa and CRISPRi Can Improve Phenotype Prediction by Deep Learning.

The development of deep learning (DL) methods in biology holds great promise for fundamental knowledge, biomedicine, and agriculture. The most global ...

Evaluating artificial intelligence-generated synthesized mammography as a standalone alternative to digital mammography with or without tomosynthesis.

PURPOSE: To evaluate whether standalone synthesized mammography (SM) can maintain or improve diagnostic accuracy while reducing reading time and radia...

Whole-body reduced-dose dual-energy CT with deep learning image reconstruction for detection of osteolytic lesions in multiple myeloma.

INTRODUCTION: Deep learning image reconstruction (DLIR) has been incorporated into dual-energy CT (DECT) to improve image quality. However, its applic...

Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer.

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression...

Development of a model for design radiation shielding composite aprons using machine learning.

This study presents the development and evaluation of a novel lead-free composite for radiation shielding, designed using an artificial neural network...

Predicting response to neoadjuvant chemotherapy combined with immunotherapy in gastric cancer based on habitat imaging and peritumoral radiomics: a two-center study.

BACKGROUND: Predicting pathological response to neoadjuvant chemotherapy combined with immunotherapy (NACI) in locally advanced gastric cancer (LAGC) ...

Predicting fire consequences with the transformer model based on multimodal feature fusion.

Accurate prediction of fire consequences is fundamental to process safety management and quantitative risk assessment in the chemical process industri...

Machine learning-based prediction of 3-month mortality in elderly patients with non-small cell lung cancer and bone metastases.

Elderly patients with non-small cell lung cancer (NSCLC) and bone metastases face a dire prognosis, creating an urgent need for accurate short-term mo...

Molecular and spatial differences in the tumor microenvironment of high-grade serous ovarian cancers with short versus long-term survival.

OBJECTIVE: Survival of patients diagnosed with advanced-stage high-grade serous ovarian cancer (HGSOC) varies widely. Understanding the biological and...

From data-rich to evidence-ready: A narrative review of generative artificial intelligence as a statistical scaffold in medical radiation sciences research.

BACKGROUND: Medical radiation science (MRS) research faces a growing asymmetry between a small body of high-rigour, statistically robust studies and a...

Estimating temporal treatment-effect patterns of radiotherapy and chemotherapy in lower-grade gliomas using causal machine learning.

Treatment decisions for lower-grade gliomas (WHO grades 2-3) rest on trial averages, which lack temporal resolution. We applied Causal Analysis of Sur...

Unraveling the molecular landscape of chronic radiation injury: From oxidative stress signaling to translational modeling (Review).

The expanding footprint of human radiation exposure, driven by advances in interventional diagnostics, the resurgence of the nuclear industry and the ...

Integrative single-cell and bulk transcriptomic analysis identifies lesion-associated gene signatures for prognostic stratification and therapeutic guidance in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis and variable response to therapy. E...

Empowering researchers and healthcare professionals in academic publishing: key insights and practical lessons from the British Society of Antimicrobial Chemotherapy (BSAC) workshop 2025.

The British Society of Antimicrobial Chemotherapy (BSAC) Academic Publishing Workshop, held at the Birmingham City Event Centre in the UK from 19 to 2...