Cardiovascular

Radiation Dose Optimisation Through Artificial Intelligence (AI)-based Auto-Thorax Collimation.

INTRODUCTION: Artificial intelligence (AI) is increasingly adopted in digital radiography to enhance workflow efficiency, standardisation and support for radiation dose optimisation. Auto-thorax collimation (ATC) has been introduced to automate field size selection in chest X-ray (CXR) imaging; however, its clinical performance and impact on collimation consistency in routine chest X-ray practice ...

ECG-Gated Retrospective Coronary CT Angiography With Deep Learning Reconstruction on 8-cm Detector CT Scanners: Pushing Radiation Dose Towards Prospective Acquisition on 16-cm Wide-Detector Scanners.

PURPOSE: Although 16-cm wide-detector CT scanners with prospective ECG-gating enable coronary artery imaging within a single cardiac cycle at a low radiation dose, many institutions still rely on scanners with detector widths <16 cm. These scanners typically use retrospective scanning, resulting in higher radiation exposure. This study tests the feasibility of lowering the radiation dose of ECG-ga...

A novel cuproptosis-related prognostic gene signature is identified by machine learning and integrative analyses in gliomas.

Recent studies have highlighted the impact of copper-induced cell death (cuproptosis) on cancer progression, prognosis, and treatment, but it remains ...

IGH::FENDRR and specific KRAS mutations define a novel B-ALL molecular subtype with poor chemotherapy response.

Large scale sequencing efforts have defined up to 27 diagnostic entities in B-ALL, leaving few samples without subtype assignment. Extended genomic an...

Development and validation of a highly accurate multigene gene expression biomarker to predict chemotherapy response in primary triple-negative breast cancer.

PURPOSE: Triple-negative breast cancer (TNBC) is an aggressive subtype lacking estrogen and progesterone receptors and HER2 amplification. Representin...

A comparative evaluation of commercial software programs for automatic segmentation in breast cancer radiotherapy planning.

Commercial software tools for automatic segmentation have been adopted in breast cancer radiotherapy planning. In this study, we directly compared com...

Path forward: Cryoablation for colorectal liver metastases.

PURPOSE: Colorectal liver metastases (CRLM) are a leading cause of mortality in patients with colorectal cancer. While surgical resection is the stand...

Enhanced mechanical and shielding properties of heavy concrete: A machine learning approach to mix proportion optimization.

Shielding concrete is a multifunctional material that combines structural support with radiation shielding, offering significant practical value. Howe...

Improving severity grading of chemotherapy-induced myelosuppression in AML via data-driven and model-based deep learning.

Chemotherapy-induced myelosuppression in acute myeloid leukemia (AML) frequently leads to life-threatening complications, yet current assessment stand...

Overall risk of cancer incidence attributable to adult body CT examinations: impact of a seven-year continuous quality improvement program.

OBJECTIVES: To estimate the impact of a continuous dose reduction and quality improvement program on radiation-induced cancer risk in adult computed t...

Imaging-Based Prediction of Key Breast Cancer Biomarkers Using Deep Learning on Digital Breast Tomosynthesis.

OBJECTIVE: To evaluate the feasibility of using deep learning models applied to digital breast tomosynthesis (DBT) images for non-invasive prediction ...

MRI-Based Synthetic CT Shows Promise as a Radiation-Free Alternative to Conventional CT in Orthopaedics.

➢ Computed tomography (CT) remains the gold standard for bone imaging, but radiation risks, especially in children, are driving interest in alternativ...

Predicting the performance of a graphene-based patch antenna using a machine learning model for terahertz applications.

This article presents the design of a graphene-based microstrip patch antenna, operating frequency range: (1-5) THz for terahertz (THz) applications. ...

Comparative study of the image quality and radiation dose in paranasal-sinus CT with different tube voltages and reconstruction algorithms.

ObjectivesTo evaluate the application of different tube voltages and image-reconstruction algorithms in paranasal-sinus computed tomography (CT) and o...

In silico discovery of selective TPX2 and BUB1B inhibitors as novel antimitotic agents in breast cancer therapy.

Breast cancer (BC) is one of the most common malignancies in women globally, characterized by significant genetic and clinical heterogeneity. This com...

Precision oncology meets nanotechnology: AI-driven liposomal nanocarriers for enhanced cancer immunotherapy.

The integration of artificial intelligence (AI) with nanotechnology is redefining therapeutic strategies in cancer immunotherapy and chemotherapy. The...

AI chatbots for patient education in localized prostate cancer radiotherapy: A comparative quality and readability analysis.

OBJECTIVES: This study aimed to conduct a comparative quality assessment of information provided by widely used artificial intelligence chatbots (AICs...

Optimizing pediatric chest CT: superior image quality and lower radiation dose with deep learning reconstruction.

OBJECTIVES: To compare image quality and radiation dose between deep learning reconstruction (DLIR) and hybrid iterative reconstruction (HIR) algorith...

The impact of emerging technologies on education, roles and career progression of radiation therapists: a delphi consensus based on the ESTRO RTT workshop.

Rapid technological advances in radiation oncology, including artificial intelligence (AI), online adaptive radiotherapy, and advanced imaging, are tr...

Deciphering lung adenocarcinoma heterogeneity: a multi-omics approach reveals nuclear division fibroblasts as prognosticators and therapeutic targets.

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant contributor to cancer‑related mortality globally. Lung‑associated fibroblasts (LAFs) are intri...