A Prospective Observational Study of Clinical Acceptability of Deep Learning Model for the Automated Segmentation of Organs at Risk for Head and Neck Radiotherapy Treatment Planning

被引：0

作者：

Lucido, J. ^{[1
]}

DeWees, T. A. ^{[2
]}

Leavitt, T. ^{[3
]}

Anand, A. ^{[4
]}

Beltran, C. ^{[5
]}

Brooke, M. ^{[6
]}

Buroker, J. ^{[7
]}

Foote, R. L. ^{[8
]}

Foss, O. R. ^{[1
]}

Hughes, C. O. ^{[6
]}

Hunzeker, A. ^{[5
]}

Laack, N. N., II ^{[5
]}

Lenz, T. ^{[9
]}

Morigami, M. ^{[6
]}

Moseley, D. J. ^{[5
]}

Patel, Y. ^{[6
]}

Tryggestad, E. J. ^{[5
]}

Wilson, M. Z. ^{[6
]}

Zverovitch, A. ^{[6
]}

Patel, S. H. ^{[10
]}

机构：

[1] Mayo Clin, Rochester, MN USA

[2] Mayo Clin, Scottsdale, AZ USA

[3] Mayo Clin, Dept Quantitat Hlth Sci, Scottsdale, AZ USA

[4] Mayo Clin, Dept Radiat Oncol, Phoenix, AZ USA

[5] Mayo Clin, Dept Radiat Oncol, Rochester, MN USA

[6] Google Hlth, Mountain View, CA USA

[7] Mayo Clin Rochester, Rochester, MN USA

[8] Mayo Clin Rochester, Dept Radiat Oncol, Rochester, MN USA

[9] Mayo Clin, Rochester Campus, Rochester, MN USA

[10] Mayo Clin Arizona, Dept Radiat Oncol, Phoenix, AZ USA

来源：

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS | 2022年 / 114卷 / 03期

关键词：

D O I：

暂无

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

2267

引用

页码：E121 / E121

页数：1

共 50 条

[41] FDG-PET in radiotherapy treatment planning of advanced head and neck cancer -: A prospective clinical analysis
Dietl, Barbara
Marienhagen, Jorg
Kuehnel, Thomas
Schaefer, Christof
Koelbl, Oliver
AURIS NASUS LARYNX, 2006, 33 (03) : 303 - 309
[42] Auto-segmentation of head and neck organs at risk in radiotherapy and its dependence on anatomic similarity
Ayyalusamy, Anantharaman
Vellaiyan, Subramani
Subramanian, Shanmuga
Ilamurugu, Arivarasan
Satpathy, Shyama
Nauman, Mohammed
Katta, Gowtham
Madineni, Aneesha
RADIATION ONCOLOGY JOURNAL, 2019, 37 (02): : 134 - 142
[43] Challenges and chances for deep-learning based target and organ at risk segmentation in radiotherapy of head and neck cancer
Nijkamp, Jasper
PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2022, 23 : 150 - 152
[44] Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring
van Dijk, Lisanne V.
Van den Bosch, Lisa
Aljabar, Paul
Peressutti, Devis
Both, Stefan
Steenbakkers, Roel. J. H. M.
Langendijk, Johannes A.
Gooding, Mark J.
Brouwer, Charlotte L.
RADIOTHERAPY AND ONCOLOGY, 2020, 142 : 115 - 123
[45] Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning
Shujun Liang
Fan Tang
Xia Huang
Kaifan Yang
Tao Zhong
Runyue Hu
Shangqing Liu
Xinrui Yuan
Yu Zhang
European Radiology, 2019, 29 : 1961 - 1967
[46] Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning
Liang, Shujun
Tang, Fan
Huang, Xia
Yang, Kaifan
Zhong, Tao
Hu, Runyue
Liu, Shangqing
Yuan, Xinrui
Zhang, Yu
EUROPEAN RADIOLOGY, 2019, 29 (04) : 1961 - 1967
[47] Deep Learning for Head and Neck Segmentation in MR: A Tool for the MR-Guided Radiotherapy
Anderson, B.
Elsohari, B.
Cardenas, C.
Mohamed, A.
Yang, P.
Fuller, C.
Chung, C.
Brock, K.
MEDICAL PHYSICS, 2018, 45 (06) : E634 - E634
[48] Towards interactive deep-learning for tumour segmentation in head and neck cancer radiotherapy
Wei, Zixiang
Ren, Jintao
Korreman, Stine Sofia
Nijkamp, Jasper
PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2023, 25
[49] Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions
Chen, Linda
Platzer, Patricia
Reschl, Christian
Schafasand, Mansure
Nachankar, Ankita
Hajdusich, Christoph Lukas
Kuess, Peter
Stock, Markus
Habraken, Steven
Carlino, Antonio
PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2024, 29
[50] Clinical evaluation of a deep learning based auto-segmentation model for intracranial organs at risk
Poel, Robert
Ruefenacht, Elias
Fix, Michael
Scheib, Stefan
Reyes, Mauricio
Ermis, Ekin
RADIOTHERAPY AND ONCOLOGY, 2024, 194 : S821 - S822

← 1 2 3 4 5 →