Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description-Reference-Cited by-同舟云学术

Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description

Published:2019-03-04 Issue:4 Volume:46 Page:1914-1921
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

El Naqa Issam¹,Irrer Jim¹,Ritter Tim A.²,DeMarco John³,Al‐Hallaq Hania⁴,Booth Jeremy⁵,Kim Grace⁶,Alkhatib Ahmad⁷,Popple Richard⁸,Perez Mario⁵,Farrey Karl⁴,Moran Jean M.¹

Affiliation:

1. Department of Radiation Oncology University of Michigan Ann Arbor MI 48103 USA

2. Department of Radiation Oncology Virginia Commonwealth University Richmond VA 23298 USA

3. Department of Radiation Oncology Cedars‐Sinai Medical Center Los Angeles California 90048 USA

4. University of Chicago Radiation and Cellular Oncology Chicago IL 60637 USA

5. Royal North Shore Hospital St Leonards New South Wales 2065 Australia

6. University of California at San Diego San Diego CA 92093 USA

7. Karmanos Cancer Institute McLaren‐Flint Flint MI 48532 USA

8. University of Alabama at Birmingham Birmingham AL 35249 USA

Funder

Varian Medical Systems

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13433

Reference42 articles.

1. Comprehensive QA for radiation oncology: Report of AAPM Radiation Therapy Committee Task Group 40

2. Task Group 142 report: Quality assurance of medical acceleratorsa)

3. AAPM Medical Physics Practice Guideline 8.a.: Linear accelerator performance tests

4. Credentialing for participation in clinical trials

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