Work

Dosimetric features‐driven machine learning model for DVH prediction in VMAT treatment planning

Year: 2018

Type: article

Source: Medical Physics

DOI: 10.1002/mp.13334

Authors: Ming Ma, Nataliya Kovalchuk, Mark K. Buyyounouski, Lei Xing, Yong Yang

Institutions: Stanford Medicine, Stanford University

Language: English

Topic: Advanced Radiotherapy Techniques

Subfield: Radiation

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: Google, Varian Medical Systems, National Institutes of Health, National Cancer Institute

Award: DASSIM-RT and Compressed Sensing-Based Inverse Planning

OpenAlex ID: W2903626699

FWCI: 6.642

Cites: 21

Cited by: 53

Related to: 10