Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience-Reference-Cited by-同舟云学术

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Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience

Published:2023-08-19 Issue:10 Volume:128 Page:1250-1261
ISSN:1826-6983
Container-title:La radiologia medica
language:en
Short-container-title:Radiol med

Author:

Hou Zhen,Gao Shanbao,Liu Juan,Yin Yicai,Zhang Ling,Han Yongchao,Yan Jing,Li Shuangshuang

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11547-023-01690-x.pdf

Reference25 articles.

1. Chandra RA, Keane FK, Voncken FE, Thomas CR Jr (2021) Contemporary radiotherapy: present and future. The Lancet 398(10295):171–184

2. Lee NY, Lu JJ, Yu Y (2022) Target volume delineation and field setup: a practical guide for conformal and intensity-modulated radiation therapy. Springer, Heidelberg

3. Cardenas CE, Beadle BM, Garden AS, Skinner HD, Yang J, Rhee DJ et al (2021) Generating high-quality lymph node clinical target volumes for head and neck cancer radiation therapy using a fully automated deep learning-based approach. Int J Radiat Oncol *Biol* Phys 109(3):801–812

4. Cardenas CE, McCarroll RE, Court LE, Elgohari BA, Elhalawani H, Fuller CD et al (2018) Deep learning algorithm for auto-delineation of high-risk oropharyngeal clinical target volumes with built-in dice similarity coefficient parameter optimization function. Int J Radiat Oncol *Biol* Phys 101(2):468–478

5. Jin D, Guo D, Ho T-Y, Harrison AP, Xiao J, Tseng C-K et al (2021) DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy. Med Image Anal 68:101909

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