ESTRO 2024 - Abstract Book

S3068

Physics - Autosegmentation

ESTRO 2024

Conclusion:

Our best performing model (3DFR) provided fast accurate auto-generated OARs and HR CTV contours with a large clinical acceptance rate.

Keywords: Deep learning, brachytherapy, cervical cancer

References:

1. Ngwa W, Addai BW, Adewole I, et al. Cancer in sub-Saharan Africa: a Lancet Oncology Commission. Lancet Oncol. 2022;23(6):e251-e312. doi:10.1016/S1470-2045(21)00720-8

2. Vanderpuye V, Hammad N, Martei Y, et al. Cancer care workforce in Africa: Perspectives from a global survey. Infect Agent Cancer. 2019;14(1). doi:10.1186/s13027-019-0227-8

3. Balogun O, Rodin D, Ngwa W, Grover S, Longo J. Challenges and Prospects for Providing Radiation Oncology Services in Africa. Semin Radiat Oncol. 2017;27(2):184-188. doi:10.1016/j.semradonc.2016.11.011

4. Cokelek M, Holt E, Kelly F, et al. Automation: The Future of Radiotherapy. Int J Radiat Oncol Biol Phys. 2020;108(3):e314. doi:10.1016/j.ijrobp.2020.07.750

5. Aliotta E, Nourzadeh H, Choi W, Leandro Alves VG, Siebers J v. An Automated Workflow to Improve Efficiency in Radiation Therapy Treatment Planning by Prioritizing Organs at Risk. Adv Radiat Oncol. 2020;5(6):1324-1333.

1666

Digital Poster

Deep learning-based breast cancer nodal structure segmentations: Clinical evaluation and QA

Shrikant Deshpande 1,2,3 , Phillip Chlap 1,2,3 , Robert Finnegan 2,3,4 , Daniel Al Mouiee 1,2,3 , Vicky Chin 1,2,3 , Shalini Vinod 1,2,3 , Lois Holloway 1,2,3