Descriptive overview of AI applications in x-ray imaging and radiotherapy Page 1 Journal of
Radiological Protection ACCEPTED MANUSCRIPT • OPEN ACCESS Descriptive overview of AI …

Radiation therapy (RT) is a highly digitized field relying heavily on computational methods
and, as such, has a high affinity for the automation potential afforded by modern artificial …

Purpose Artificial intelligence (AI) and machine learning present an opportunity to enhance
clinical decision-making in radiation oncology. This study aims to evaluate the competency …

Advancements in radiotherapy auto-segmentation necessitate reliable and efficient
workflows. Therefore, a standardized fully automatic workflow was developed for three …

Background and purpose During the ESTRO 2023 physics workshop on “AI for the fully
automated radiotherapy treatment chain”, the topic of deep learning (DL) segmentation was …

As yet, no systematic review on commercial deep learning-based auto-segmentation (DLAS)
software for breast cancer radiation therapy (RT) planning has been published, although …

Introduction Artificial intelligence (AI)-based technologies embody countless solutions in
radiation oncology, yet translation of AI-assisted software tools to actual clinical …

Background MR-Linac allows for daily online treatment adaptation to the observed geometry
of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer …

To propose a clinically oriented quantitative metric, Hu similarity coefficient (HSC), to
evaluate contour quality, gauge the performance of auto contouring methods, and aid …

Purpose This study evaluates the impact of integrating a novel, in-house developed
electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology …