Machine learning‐generated decision boundaries for prediction and exploration of patient‐specific quality assurance failures in stereotactic radiosurgery plans-Reference-Cited by-同舟云学术

Machine learning‐generated decision boundaries for prediction and exploration of patient‐specific quality assurance failures in stereotactic radiosurgery plans

Published:2022-02-04 Issue:3 Volume:49 Page:1955-1963
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Medical Physics

Author:

Braun Jeremy¹²³,Quirk Sarah¹²³,Tchistiakova Ekaterina¹²³

Affiliation:

1. Department of Physics and Astronomy University of Calgary Calgary Alberta Canada

2. Tom Baker Cancer Centre Calgary Alberta Canada

3. Department of Oncology University of Calgary Calgary Alberta Canada

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15454

Reference18 articles.

1. SU-FF-T-301: Clinical Implementation and Commission of Volumetric Modulated Arc Therapy

2. Predicting deliverability of volumetric-modulated arc therapy (VMAT) plans using aperture complexity analysis

3. Modulation indices for volumetric modulated arc therapy

4. The role of complexity metrics in a multi-institutional dosimetry audit of VMAT

5. Complexity metric as a complement to measurement based IMRT/VMAT patient-specific QA

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