A qualitative, quantitative and dosimetric evaluation of a machine learning-based automatic segmentation method in treatment planning for gastric cancer.

Purpose: To investigate the performance of a machine learning-based segmentation method for treatment planning of gastric cancer.

Materials And Methods: Eighteen patients planned to be irradiated for gastric cancer were studied. The target and the surrounding organs-at-risk (OARs) were manually delineated on CT scans.

Comparison of radiation exposure from dual- and single-energy CT imaging protocols resulting in equivalent contrast-to-noise ratio of lesions for adults and children: a phantom study.

Objectives: To compare the radiation exposure from single-energy CT (SECT) against rapid kV-switching dual-energy CT (DECT) imaging in both adults and children when resulting image data offer equivalent lesion identification power.

Materials And Methods: Lesions in an adult and a 10-year-old-child body phantom were imitated using iodine solutions of different concentrations. Phantoms were subjected to several SECT and DECT thoracic and abdominal scans using a rapid kV-switching DECT scanner.

Descriptive overview of AI applications in x-ray imaging and radiotherapy.

Artificial intelligence (AI) is transforming medical radiation applications by handling complex data, learning patterns, and making accurate predictions, leading to improved patient outcomes. This article examines the use of AI in optimising radiation doses for x-ray imaging, improving radiotherapy outcomes, and briefly addresses the benefits, challenges, and limitations of AI integration into clinical workflows. In diagnostic radiology, AI plays a pivotal role in optimising radiation exposure, reducing noise, enhancing image contrast, and lowering radiation doses, especially in high-dose procedures like computed tomography (CT).

Implementing verifiable oncological imaging by quality assurance and optimization (i‑Violin) : Protocol for a European multicenter study.

Background: Advanced imaging techniques play a pivotal role in oncology. A large variety of computed tomography (CT) scanners, scan protocols, and acquisition techniques have led to a wide range in image quality and radiation exposure. This study aims at implementing verifiable oncological imaging by quality assurance and optimization (i-Violin) through harmonizing image quality and radiation dose across Europe.

Synthesis and Biological Evaluation of Novel Cationic Rhenium and Technetium-99m Complexes Bearing Quinazoline Derivative for Epidermal Growth Factor Receptor Targeting.

Epidermal growth factor receptor (EGFR) plays a vital role in cell proliferation and survival, with its overexpression linked to various malignancies, including non-small cell lung cancer (NSCLC). Although EGFR tyrosine kinase inhibitors (TKIs) are a key therapeutic strategy, acquired resistance and relapse remain challenges. This study aimed to synthesize and evaluate novel rhenium-based complexes incorporating EGFR TKIs to enhance anticancer efficacy, particularly in radiosensitization.