NRG Oncology assessment of artificial intelligence deep learning–based auto-segmentation for radiation therapy: Current developments, clinical considerations, and future directions Review


Authors: Rong, Y.; Chen, Q.; Fu, Y.; Yang, X.; Al-Hallaq, H. A.; Wu, Q. J.; Yuan, L.; Xiao, Y.; Cai, B.; Latifi, K.; Benedict, S. H.; Buchsbaum, J. C.; Qi, X. S.
Review Title: NRG Oncology assessment of artificial intelligence deep learning–based auto-segmentation for radiation therapy: Current developments, clinical considerations, and future directions
Abstract: Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations. © 2023
Keywords: radiotherapy; oncology; radiation oncology; artificial intelligence; benchmarking; radiotherapy planning, computer-assisted; artificial neural network; treatment planning systems; automatic segmentations; terminology; medical informatics; contouring; humans; human; deep learning; petroleum reservoir evaluation; auto segmentation; 'current; neural networks, computer; intelligence models; compliance control; learning neural networks; model-based method; segmentation tool; traditional models
Journal Title: International Journal of Radiation Oncology, Biology, Physics
Volume: 119
Issue: 1
ISSN: 0360-3016
Publisher: Elsevier Inc.  
Date Published: 2024-05-01
Start Page: 261
End Page: 280
Language: English
DOI: 10.1016/j.ijrobp.2023.10.033
PUBMED: 37972715
PROVIDER: scopus
PMCID: PMC11023777
DOI/URL:
Notes: Source: Scopus
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  1. Yabo Fu
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