Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer

被引：5

作者：

Bakx, Nienke ^{[1
]}

van der Sangen, Maurice ^{[1
]}

Theuws, Jacqueline ^{[1
]}

Bluemink, Johanna ^{[1
]}

Hurkmans, Coen ^{[1
,2
,3
]}

机构：

[1] Catharina Hosp, Dept Radiat Oncol, Eindhoven, Netherlands

[2] Tech Univ Eindhoven, Fac Appl Phys, Eindhoven, Netherlands

[3] Tech Univ Eindhoven, Fac Elect Engn, Eindhoven, Netherlands

来源：

PHYSICS & IMAGING IN RADIATION ONCOLOGY | 2023年 / 28卷

关键词：

Breast cancer; Clinical use; Deep learning; Radiotherapy; DOSE PREDICTION;

D O I：

10.1016/j.phro.2023.100496

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners were allowed to manually adapt them. Plans were evaluated based on clinical goals and DVH parameters. Thirty-seven of 50plans did fulfill all clinical goals without adjustments. Thirteen of these 37 plans were still adjusted but did not improve mean heart or lung dose. These results leave room for improvement of both the DL model as well as education on clinically relevant adjustments.

引用

页数：4

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