| Literature DB >> 31931265 |
Philip M P Poortmans1, Silvia Takanen2, Gustavo Nader Marta3, Icro Meattini4, Orit Kaidar-Person5.
Abstract
Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.Entities:
Keywords: Artificial intelligence; Auto-segmentation; Breast cancer; Deep learning; Neural network; Radiation therapy
Year: 2019 PMID: 31931265 DOI: 10.1016/j.breast.2019.11.011
Source DB: PubMed Journal: Breast ISSN: 0960-9776 Impact factor: 4.380