Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study

Purpose: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potential IOV. Methods and materials: In phase 1, two brea...

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Principais autores:	Min Seo Choi, Jee Suk Chang, Kyubo Kim, Jin Hee Kim, Tae Hyung Kim, Sungmin Kim, Hyejung Cha, Oyeon Cho, Jin Hwa Choi, Myungsoo Kim, Juree Kim, Tae Gyu Kim, Seung-Gu Yeo, Ah Ram Chang, Sung-Ja Ahn, Jinhyun Choi, Ki Mun Kang, Jeanny Kwon, Taeryool Koo, Mi Young Kim, Seo Hee Choi, Bae Kwon Jeong, Bum-Sup Jang, In Young Jo, Hyebin Lee, Nalee Kim, Hae Jin Park, Jung Ho Im, Sea-Won Lee, Yeona Cho, Sun Young Lee, Ji Hyun Chang, Jaehee Chun, Eung Man Lee, Jin Sung Kim, Kyung Hwan Shin, Yong Bae Kim
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Elsevier 2024-02-01
סדרה:	Breast
נושאים:	RTQA Inter-observer variation Auto-contouring Breast cancer Deep learning
גישה מקוונת:	http://www.sciencedirect.com/science/article/pii/S0960977623007257
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