Artificial intelligence in radiation oncology
Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is
practised. AI platforms excel in recognizing complex patterns in medical data and provide a …
practised. AI platforms excel in recognizing complex patterns in medical data and provide a …
Artificial intelligence in radiation oncology: a specialty-wide disruptive transformation?
Artificial intelligence (AI) is emerging as a technology with the power to transform
established industries, and with applications from automated manufacturing to advertising …
established industries, and with applications from automated manufacturing to advertising …
Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning
J Wong, A Fong, N McVicar, S Smith… - Radiotherapy and …, 2020 - Elsevier
Background Deep learning-based auto-segmented contours (DC) aim to alleviate labour
intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most …
intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most …
Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks
K Men, J Dai, Y Li - Medical physics, 2017 - Wiley Online Library
Purpose Delineation of the clinical target volume (CTV) and organs at risk (OAR s) is very
important for radiotherapy but is time‐consuming and prone to inter‐observer variation …
important for radiotherapy but is time‐consuming and prone to inter‐observer variation …
Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study
Background Artificial intelligence (AI) and deep learning have shown great potential in
streamlining clinical tasks. However, most studies remain confined to in silico validation in …
streamlining clinical tasks. However, most studies remain confined to in silico validation in …
NRG oncology updated international consensus atlas on pelvic lymph node volumes for intact and postoperative prostate cancer
Purpose In 2009, the Radiation Therapy Oncology Group (RTOG) genitourinary members
published a consensus atlas for contouring prostate pelvic nodal clinical target volumes …
published a consensus atlas for contouring prostate pelvic nodal clinical target volumes …
[HTML][HTML] Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy
WJ Zabel, JL Conway, A Gladwish, J Skliarenko… - Practical Radiation …, 2021 - Elsevier
Purpose Auto-contouring may reduce workload, interobserver variation, and time associated
with manual contouring of organs at risk. Manual contouring remains the standard due in …
with manual contouring of organs at risk. Manual contouring remains the standard due in …
Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy
MHF Savenije, M Maspero, GG Sikkes… - Radiation …, 2020 - Springer
Background Structure delineation is a necessary, yet time-consuming manual procedure in
radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and …
radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and …
Machine learning in radiation oncology: opportunities, requirements, and needs
Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but
there is much work to be done. In this article, we approach the radiotherapy process from a …
there is much work to be done. In this article, we approach the radiotherapy process from a …
Clinical evaluation of atlas-and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer
Manual segmentation is the gold standard method for radiation therapy planning; however, it
is time-consuming and prone to inter-and intra-observer variation, giving rise to interests in …
is time-consuming and prone to inter-and intra-observer variation, giving rise to interests in …