Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer

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...
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Gespeichert in:

Autor*in:	Philip M.P. Poortmans [verfasserIn] Silvia Takanen [verfasserIn] Gustavo Nader Marta [verfasserIn] Icro Meattini [verfasserIn] Orit Kaidar-Person [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Artificial intelligence Breast cancer Radiation therapy Neural network Deep learning Auto-segmentation

Übergeordnetes Werk:	In: Breast - Elsevier, 2020, 49(2020), Seite 194-200
Übergeordnetes Werk:	volume:49 ; year:2020 ; pages:194-200

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.breast.2019.11.011

Katalog-ID:	DOAJ042032873

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callnumber-first	R - Medicine
author	Philip M.P. Poortmans
spellingShingle	Philip M.P. Poortmans misc RC254-282 misc Artificial intelligence misc Breast cancer misc Radiation therapy misc Neural network misc Deep learning misc Auto-segmentation misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer
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topic_title	RC254-282 Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer Artificial intelligence Breast cancer Radiation therapy Neural network Deep learning Auto-segmentation
topic	misc RC254-282 misc Artificial intelligence misc Breast cancer misc Radiation therapy misc Neural network misc Deep learning misc Auto-segmentation misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc Artificial intelligence misc Breast cancer misc Radiation therapy misc Neural network misc Deep learning misc Auto-segmentation misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer
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title_full	Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer
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author_browse	Philip M.P. Poortmans Silvia Takanen Gustavo Nader Marta Icro Meattini Orit Kaidar-Person
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title_sort	winter is over: the use of artificial intelligence to individualise radiation therapy for breast cancer
callnumber	RC254-282
title_auth	Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer
url	https://doi.org/10.1016/j.breast.2019.11.011 https://doaj.org/article/787b71ffa51d40f9a78f7e49a3ee9ab7 http://www.sciencedirect.com/science/article/pii/S0960977619311038 https://doaj.org/toc/1532-3080
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up_date	2024-07-03T23:20:31.698Z
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