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

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