Structural Quality Indicators in Radiation Oncology: Insights and Implications.

Aims: Over the past two decades, significant progress has been made in the development of quality indicators (QIs) within the field of radiotherapy (RT). However, most of the literature has predominantly focused on process and outcome-related QIs, with limited attention given to structural QIs. This review aims to address this gap by providing insights into structural QIs in RT and examining the complexities and considerations involved in establishing universal standards for these indicators across RT facilities globally.

An artificial neural network based approach for predicting the proton beam spot dosimetric characteristics of a pencil beam scanning technique.

Utilising Machine Learning (ML) models to predict dosimetric parameters in pencil beam scanning proton therapy presents a promising and practical approach. The study developed Artificial Neural Network (ANN) models to predict proton beam spot size and relative positional errors using 9000 proton spot data. The irradiation log files as input variables and corresponding scintillation detector measurements as the label values.

Magnetic Seed Localization (Magseed) for excision of impalpable breast lesions-The North Wales experience.

The breast surgical community has faced huge challenges due to close working relationships with radiologists who are in short supply. Breast lesion localization is moving away from the traditional wire technique, which requires an on-site radiologist to support theater scheduling. In North Wales, the introduction of Magseed for impalpable breast lesion localization has facilitated theater scheduling in the absence of same day radiology presence.

Mean parotid dose prediction model using machine learning regression method for intensity-modulated radiotherapy in head and neck cancer.

Parotids are considered one of the major organs at risk in Head and Neck (HN) intensity-modulated radiotherapy (IMRT). Achieving proper target coverage with reduced mean parotid dose demands an elaborate time-consuming IMRT plan optimization. A parotid mean dose prediction model based on a machine-learning linear regression was developed and validated in this study.

Experimental determination of thermal neutron fluence around Elekta Versa HD linear accelerator for various photon energies.

A complex neutron spectrum generated along with a useful photon beam imposes an additional radiation protection risk around medical linear accelerators (linac). The thermal neutron component of this complex neutron spectrum formed during different photon modes of operation of Elekta Versa HD linac has been quantified using Indium foil activation technique. The thermal neutron fluence (Φ ) at isocenter for 15 MV, 10 MV and 10 MV FFF beams was found to be 2.