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Original Articles: NACP Symposium on Radiophysics

Development of a national deep learning-based auto-segmentation model for the heart on clinical delineations from the DBCG RT nation cohort

ORCID Icon, , , , ORCID Icon, , , , , , & show all
Pages 1201-1207 | Received 29 Apr 2023, Accepted 16 Aug 2023, Published online: 15 Sep 2023

References

  • Groom N, Wilson E, Faivre-Finn C. Effect of accurate heart delineation on cardiac dose during the CONVERT trial. Br J Radiol. 2017;90(1073):20170036. doi: 10.1259/bjr.20170036.
  • Chung SY, Chang JS, Choi MS, et al. Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery | radiation oncology | full text. Radiat Oncol. 2021;16(1):44. doi: 10.1186/s13014-021-01771-z.
  • Almberg SS, Lervåg C, Frengen J, et al. Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer. Radiother Oncol. 2022;173:62–68. doi: 10.1016/j.radonc.2022.05.018.
  • Choi MS, Choi BS, Chung SY, et al. Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer. Radiother Oncol. 2020;153:139–145. doi: 10.1016/j.radonc.2020.09.045.
  • Fang Y, Wang J, Ou X, et al. The impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients. Phys Med Biol. 2021;66(18):185012. doi: 10.1088/1361-6560/ac2206.
  • van Mourik AM, Elkhuizen PHM, Minkema D, et al. Multiinstitutional study on target volume delineation variation in breast radiotherapy in the presence of guidelines. Radiother Oncol. 2010;94(3):286–291. doi: 10.1016/j.radonc.2010.01.009.
  • Refsgaard L, Skarsø ER, Ravkilde T, et al. End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a danish breast cancer group cohort. Phys Imaging Radiat Oncol. 2023;27:100484.
  • Refsgaard L, Skarsø ER, Ravkilde T, et al. Impact of guidelines on nationwide breast cancer treatment planning practices (DBCG RT nation study). Radiother Oncol. 2022;170: s 832–S834. doi: 10.1016/S0167-8140(22)02721-9.
  • Refsgaard L, Ravkilde T, Skarsø ER, et al. OC-0425 dosimetric effects of national guidelines in breast cancer radiotherapy 2008-2016 (DBCG RT-Nation). Radiother Oncol. 2021;161: s 324–S325. doi: 10.1016/S0167-8140(21)06912-7.
  • Nielsen MH, Berg M, Pedersen AN, et al. Delineation of target volumes and organs at risk in adjuvant radiotherapy of early breast cancer: national guidelines and contouring atlas by the danish breast cancer cooperative group. Acta Oncol. 2013;52(4):703–710. doi: 10.3109/0284186X.2013.765064.
  • Feng M, Moran JM, Koelling T, et al. Development and validation of a heart atlas to study cardiac exposure to radiation following treatment for breast cancer. Int J Radiat Oncol Biol Phys. 2011;79(1):10–18. doi: 10.1016/j.ijrobp.2009.10.058.
  • Milo MLH, Offersen BV, Bechmann T, et al. Delineation of whole heart and substructures in thoracic radiation therapy: national guidelines and contouring atlas by the danish multidisciplinary cancer groups. Radiother Oncol. 2020;150:121–127. doi: 10.1016/j.radonc.2020.06.015.
  • Offersen BV, Boersma LJ, Kirkove C, et al. ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer. Radiother Oncol. 2015;114(1):3–10. doi: 10.1016/j.radonc.2014.11.030.
  • Francolini G, Thomsen MS, Yates ES, et al. Quality assessment of delineation and dose planning of early breast cancer patients included in the randomized skagen trial 1. Radiother Oncol. 2017;123(2):282–287. doi: 10.1016/j.radonc.2017.03.011.
  • Holm Milo ML, Slot Møller D, Bisballe Nyeng T, et al. Radiation dose to heart and cardiac substructures and risk of coronary artery disease in early breast cancer patients: a DBCG study based on modern radiation therapy techniques. Radiother Oncol. 2023;180:109453. doi: 10.1016/j.radonc.2022.109453.
  • Milo MLH, Nyeng TB, Lorenzen EL, et al. Atlas-based auto-segmentation for delineating the heart and cardiac substructures in breast cancer radiation therapy. Acta Oncol. 2022;61(2):247–254. doi: 10.1080/0284186X.2021.1967445.
  • Phil T, Albrecht T, Gay S, et al. Sikerdebaard/dcmrtstruct2nii: v5 [Internet]. Zenodo; 2023 [cited 2023 Mar 10]. Available from: https://zenodo.org/record/7705311
  • Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203–211. doi: 10.1038/s41592-020-01008-z.
  • Panchal A, Couture G, Bot Pyup Io, et al. dicompyler/dicompyler-core v0.5.5 [Internet]. Zenodo; 2019 [cited 2022 Dec 9]. Available from: https://zenodo.org/record/3236628.
  • Cha E, Elguindi S, Onochie I, et al. Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy. Radiother Oncol. 2021;159:1–7. doi: 10.1016/j.radonc.2021.02.040.
  • Sherer MV, Lin D, Elguindi S, et al. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: a critical review. Radiother Oncol. 2021;160:185–191. doi: 10.1016/j.radonc.2021.05.003.
  • Eldesoky AR, Yates ES, Nyeng TB, et al. Internal and external validation of an ESTRO delineation guideline - dependent automated segmentation tool for loco-regional radiation therapy of early breast cancer. Radiother Oncol. 2016;121(3):424–430. doi: 10.1016/j.radonc.2016.09.005.
  • Lorenzen EL, Taylor CW, Maraldo M, et al. Inter-observer variation in delineation of the heart and left anterior descending coronary artery in radiotherapy for breast cancer: a multi-Centre study from Denmark and the UK. Radiother Oncol. 2013;108(2):254–258. doi: 10.1016/j.radonc.2013.06.025.

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