Cardiovascular

Quantification of PET activation in adipose tissue from non-contrast CT scans.

Brown adipose tissue (BAT) plays a key role in energy metabolism and cardiometabolic health. Its detection typically relies on 18F-FDG PET, which is costly, radiation-intensive, and impractical for large-scale screening. We propose a deep learning model to estimate regional metabolic activity in adipose tissue from standard non-contrast CT, enabling PET-like insights without radiotracers. Using pa...

CT Radiation Dose Reduction With Preserved Diagnostic Performance: How Far Have We Come Over 25 Years?

Concerns about the risk of radiation from CT have driven a spectrum of major advances in radiation dose reduction technology since the 2000s, including added beam filtration, dynamic z-axis collimation, automatic tube current modulation and tube potential selection, advanced iterative reconstruction, and deep learning-based reconstructions. The introduction of photon-counting detector CT further i...

Prognostic Significance of Lymph Node Ratio for Overall Survival in Patients with Lymph Node-Positive Bladder Cancer Post-Radical Cystectomy and Chemotherapy: A Multicenter Cohort Analysis.

BACKGROUND: The lymph node ratio (LNR) is gaining recognition as a prognostic biomarker for various malignant neoplasms. However, its prognostic role ...

Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.

Pulmonary embolism (PE) is a life-threatening condition for which computed tomography pulmonary angiography (CTPA) is the standard diagnostic modality...

Comparison of Image Quality Reconstructed Using Iterative Reconstruction and Deep Learning Algorithms Under Varying Dose Reductions in Dual-Energy Carotid CT Angiography.

Carotid CT angiography (CTA) is valuable for diagnosing carotid artery disease but involves radiation and contrast agent risks. Deep Learning Image Re...

Pilot clinical evaluation of artificial intelligence-driven guiding catheter simulation for optimizing percutaneous coronary intervention.

AIMS: In percutaneous coronary intervention (PCI), a suboptimal choice of guiding catheter may compromise coaxial alignment and backup support, prolon...

Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research.

PURPOSE: Artificial Intelligence (AI) and Machine Learning (ML) are being explored to improve systematic evidence gathering and to identify patterns a...

Beyond the dose: a clearance-enabled in vitro platform for evaluating local therapies.

Inclusion of physiologically relevant clearance mechanisms into organ-on-a-chip models is essential to reproduce tissue exposure and predict therapeut...

AI-Assisted Chromosome Aberration Analysis for Rapid Biological Dose Estimation of Ionizing Radiation.

Radiation dose assessment in exposed individuals relies on the dicentric assay, the gold-standard cytogenetic biodosimeter that quantifies radiation-i...

Evaluating dose distribution in prostate IMRT patients using deep learning: the influence of loss function on model performance.

To estimate the influence of various loss functions on the performance of deep learning (DL) models for dose prediction in intensity-modulated radioth...

Interpretable MRI-Based Machine Learning Model for Noninvasive Prediction of Axillary Lymph Node Metastasis After Neoadjuvant Chemotherapy in Breast Cancer.

RATIONALE AND OBJECTIVES: Accurate prediction of axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) remains challenging in bre...

EXPRESS: Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra.

Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed profile of the molecular composition of a sample. T...

SH3BP5L triggers the RAB11A-regulated integrin recycling network implicated in breast cancer metastasis.

Metastatic progression in aggressive breast cancer (BC) depends on a tightly controlled vesicular recycling network regulated by RAB11, a small guanos...

Molecular classification of radiation-induced meningiomas.

PURPOSE: Radiation-induced meningiomas (RIMs) are an uncommon late complication of cranial irradiation that frequently display aggressive behavior. Al...

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data.

INTRODUCTION: Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in...

Single‑center weakly supervised deep learning prediction of KRAS, NRAS, BRAF, and HER2 status in colorectal cancer from histopathology images using internal cross‑validation.

Machine learning-based prognostic model for local control prediction in lung cancer patients undergoing stereotactic body radiation therapy: a preliminary study.

PURPOSE: Predicting local recurrence after stereotactic body radiation therapy (SBRT) for lung cancer remains challenging. This study aims to develop ...

Integrating network toxicology, machine learning, and molecular dynamics simulations to reveal tanshinone iia's dual mechanisms in TNBC and doxorubicin-induced cardiotoxicity.

Doxorubicin (Dox)-induced cardiotoxicity remains a critical barrier to optimizing breast cancer (BC) treatment, highlighting the urgent need to dissec...

Overcoming Resistance in EGFR-Mutant Cancers: A Comprehensive Review of Inhibitor Evolution and SAR-Based Design.

The epidermal growth factor receptor (EGFR) is a key target in cancer therapy, mainly in non-small cell lung cancer (NSCLC). Though, the efficacy of E...

Multi-omics predicts radiotherapy response in small cell lung cancer patients receiving whole brain irradiation.

OBJECTIVE: Dosiomics and radiomics elaborate the low-and high-order features extracted from images to predict clinical outcomes. Whole-brain radiother...