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.

Choi MS, Chang JS, Kim K, Kim JH, Kim TH, Kim S, Cha H, Cho O, Choi JH, Kim M, Kim J, Kim TG, Yeo SG, Chang AR, Ahn SJ, Choi J, Kang KM, Kwon J, Koo T, Kim MY, Choi SH, Jeong BK, Jang BS, Jo IY, Lee H, Kim N, Park HJ, Im JH, Lee SW, Cho Y, Lee SY, Chang JH, Chun J, Lee EM, Kim JS, Shin KH, Kim YB

Open source

DOI: 10.1016/j.breast.2023.103599
Published: 2024 Feb
Container: Breast (Edinburgh, Scotland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1016/j.breast.2023.103599,
  title = {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.},
  author = {Choi MS and Chang JS and Kim K and Kim JH and Kim TH and Kim S and Cha H and Cho O and Choi JH and Kim M and Kim J and Kim TG and Yeo SG and Chang AR and Ahn SJ and Choi J and Kang KM and Kwon J and Koo T and Kim MY and Choi SH and Jeong BK and Jang BS and Jo IY and Lee H and Kim N and Park HJ and Im JH and Lee SW and Cho Y and Lee SY and Chang JH and Chun J and Lee EM and Kim JS and Shin KH and Kim YB},
  year = {2024},
  journal = {Breast (Edinburgh, Scotland)},
  doi = {10.1016/j.breast.2023.103599},
  url = {https://doi.org/10.1016/j.breast.2023.103599}
}

RIS

TY  - JOUR
TI  - 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.
AU  - Choi MS
AU  - Chang JS
AU  - Kim K
AU  - Kim JH
AU  - Kim TH
AU  - Kim S
AU  - Cha H
AU  - Cho O
AU  - Choi JH
AU  - Kim M
AU  - Kim J
AU  - Kim TG
AU  - Yeo SG
AU  - Chang AR
AU  - Ahn SJ
AU  - Choi J
AU  - Kang KM
AU  - Kwon J
AU  - Koo T
AU  - Kim MY
AU  - Choi SH
AU  - Jeong BK
AU  - Jang BS
AU  - Jo IY
AU  - Lee H
AU  - Kim N
AU  - Park HJ
AU  - Im JH
AU  - Lee SW
AU  - Cho Y
AU  - Lee SY
AU  - Chang JH
AU  - Chun J
AU  - Lee EM
AU  - Kim JS
AU  - Shin KH
AU  - Kim YB
PY  - 2024
JO  - Breast (Edinburgh, Scotland)
DO  - 10.1016/j.breast.2023.103599
UR  - https://doi.org/10.1016/j.breast.2023.103599
ER  -

APA

MS, C., JS, C., K, K., JH, K., TH, K., S, K., H, C., O, C., JH, C., M, K., J, K., TG, K., SG, Y., AR, C., SJ, A., J, C., KM, K., J, K., T, K., MY, K., SH, C., BK, J., BS, J., IY, J., H, L., N, K., HJ, P., JH, I., SW, L., Y, C., SY, L., JH, C., J, C., EM, L., JS, K., KH, S., & YB, K. (2024). 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.. Breast (Edinburgh, Scotland). https://doi.org/10.1016/j.breast.2023.103599

Source records

pubmed · retrieved 2026-09-25T19:31:28.874Z
europe-pmc · retrieved 2026-09-25T19:31:28.886Z
doaj · retrieved 2026-09-25T19:31:28.907Z