Performance stability evaluation of atlas-based machine learning radiation therapy treatment planning in prostate cancer.

Conroy, Leigh; Khalifa, Aly; Berlin, Alejandro; McIntosh, Chris; Purdie, Thomas G

Conroy, Leigh; Khalifa, Aly; Berlin, Alejandro; McIntosh, Chris; Purdie, Thomas G.

Affiliation

Conroy L; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.
Khalifa A; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
Berlin A; Techna Institute, University Health Network, Toronto, Canada.
McIntosh C; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Canada.
Purdie TG; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.

Phys Med Biol ; 66(13)2021 06 22.

Article in En | MEDLINE | ID: mdl-34156354

Subject(s)

Prostatic Neoplasms; Radiotherapy, Intensity-Modulated; Humans; Machine Learning; Male; Organs at Risk; Prostatic Neoplasms/radiotherapy; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted

Key words

atlas-selection; automated treatment planning; benchmarking; dose prediction; external beam radiotherapy; machine learning; quality assurance

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Prostatic Neoplasms / Radiotherapy, Intensity-Modulated Type of study: Etiology_studies / Guideline / Prognostic_studies Limits: Humans / Male Language: En Journal: Phys Med Biol Year: 2021 Type: Article Affiliation country: Canada

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