[HTML][HTML] Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance
Artificial Intelligence (AI) is currently being introduced into different domains, including
medicine. Specifically in radiation oncology, machine learning models allow automation and …
medicine. Specifically in radiation oncology, machine learning models allow automation and …
Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review
Advances in artificial intelligence-based methods have led to the development and
publication of numerous systems for auto-segmentation in radiotherapy. These systems …
publication of numerous systems for auto-segmentation in radiotherapy. These systems …
Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk
(OARs) and tumors. However, it is the most time-consuming step as manual delineation is …
(OARs) and tumors. However, it is the most time-consuming step as manual delineation is …
[HTML][HTML] A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy
Background and purpose Delineating organs at risk (OARs) on computed tomography (CT)
images is an essential step in radiation therapy; however, it is notoriously time-consuming …
images is an essential step in radiation therapy; however, it is notoriously time-consuming …
Adaptive proton therapy
Radiation therapy treatments are typically planned based on a single image set, assuming
that the patient's anatomy and its position relative to the delivery system remains constant …
that the patient's anatomy and its position relative to the delivery system remains constant …
Machine learning for auto-segmentation in radiotherapy planning
Manual segmentation of target structures and organs at risk is a crucial step in the
radiotherapy workflow. It has the disadvantages that it can require several hours of clinician …
radiotherapy workflow. It has the disadvantages that it can require several hours of clinician …
[HTML][HTML] Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy
F Vaassen, C Hazelaar, A Vaniqui, M Gooding… - Physics and Imaging in …, 2020 - Elsevier
Background and purpose In radiotherapy, automatic organ-at-risk segmentation algorithms
allow faster delineation times, but clinically relevant contour evaluation remains challenging …
allow faster delineation times, but clinically relevant contour evaluation remains challenging …
Automated contouring and planning in radiation therapy: what is 'clinically acceptable'?
Developers and users of artificial-intelligence-based tools for automatic contouring and
treatment planning in radiotherapy are expected to assess clinical acceptability of these …
treatment planning in radiotherapy are expected to assess clinical acceptability of these …
A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy
PJ Doolan, S Charalambous, Y Roussakis… - Frontiers in …, 2023 - frontiersin.org
Purpose/objective (s) Auto-segmentation with artificial intelligence (AI) offers an opportunity
to reduce inter-and intra-observer variability in contouring, to improve the quality of contours …
to reduce inter-and intra-observer variability in contouring, to improve the quality of contours …
Revolutionizing radiation therapy: the role of AI in clinical practice
M Kawamura, T Kamomae, M Yanagawa… - Journal of radiation …, 2024 - academic.oup.com
This review provides an overview of the application of artificial intelligence (AI) in radiation
therapy (RT) from a radiation oncologist's perspective. Over the years, advances in …
therapy (RT) from a radiation oncologist's perspective. Over the years, advances in …