Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer
| Autor: | Silvia Takanen, Gustavo Nader Marta, Icro Meattini, Orit Kaidar-Person, Philip Poortmans |
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| Rok vydání: | 2020 |
| Předmět: |
Organs at Risk
Artificial intelligence medicine.medical_treatment Planning target volume Breast Neoplasms Dose distribution lcsh:RC254-282 03 medical and health sciences Breast cancer 0302 clinical medicine medicine Humans 030212 general & internal medicine Precision Medicine Contouring Auto-segmentation business.industry Radiotherapy Planning Computer-Assisted Radiation dose Radiotherapy Dosage Deep learning General Medicine Limiting Virtual special issue: Artificial Intelligence in Breast Cancer Care Edited by Nehmat Houssami Maria João Cardoso Giuseppe Pozzi and Brigitte Seroussi lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease Neural network Radiation therapy 030220 oncology & carcinogenesis Female Surgery Outcome data business |
| Zdroj: | Breast, Vol 49, Iss, Pp 194-200 (2020) The Breast : official journal of the European Society of Mastology |
| ISSN: | 0960-9776 |
| Popis: | 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. Highlights • Artificial intelligence is used in target delineation and treatment planning. • Benefits are expected from individualising dose based on recurrence patterns. • Collaboration between different expertises is essential to generate models. |
| Databáze: | OpenAIRE |
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