Effects of model size and composition on quality of head-and-neck knowledge-based plans.
Autor: | Kaderka R; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA., Dogan N; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA., Jin W; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA., Bossart E; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA. |
---|---|
Jazyk: | angličtina |
Zdroj: | Journal of applied clinical medical physics [J Appl Clin Med Phys] 2024 Feb; Vol. 25 (2), pp. e14168. Date of Electronic Publication: 2023 Oct 05. |
DOI: | 10.1002/acm2.14168 |
Abstrakt: | Purpose: Knowledge-based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head-and-neck KBP models were trained to address these questions. Methods: The six models differed in training size and plan composition: The KBP Results: Compared to manual plans, KBP Conclusions: Overall, all models were shown to produce high-quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head-and-neck KBP models. (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.) |
Databáze: | MEDLINE |
Externí odkaz: |