Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

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Abstract

We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) with an electric portal imaging device (EPID).

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Author and article information

Journal

Journal ID (iso-abbrev): Med Phys

Title: Medical physics

Publisher: Wiley

ISSN (Electronic): 2473-4209

ISSN (Print): 0094-2405

Publication date (Electronic): Mar 2021

Volume: 48

Issue: 3

Affiliations

[1 ] Department of Radiation Oncology, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-dori, Chuo-ku, Niigata, 951-8520, Japan.

[2 ] Radiation Therapy Section, Department of Clinical Support, Hiroshima University Hospital, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.

[3 ] Niigata Neurosurgical Hospital, 3057 Yamada, Nishi-ku, Niigata, 950-1101, Japan.

[4 ] Department of Radiological Technology, Niigata University Graduate School of Health Sciences, 2-746 Asahimachi-dori, Chuo-ku, Niigata, 951-8518, Japan.

[5 ] Section of Radiology, Department of Clinical Support, Niigata University Medical and Dental Hospital, 1-754 Asahimachi-dori, Chuo-ku, Niigata, 951-8520, Japan.

[6 ] Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, 1-757 Asahimachi-dori, Chuo-ku, Niigata, 951-8510, Japan.

[7 ] Department of Radiation Oncology, Faculty of Medicine, Hokkaido University, N15 W7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.

Article

DOI: 10.1002/mp.14699

PubMed ID: 33382467

SO-VID: d05d55a8-6604-43cd-a527-587cf5d61ffa

History

Keywords: IMRT QA,machine learning,quality assurance,radiomics

Data availability:

Keywords: IMRT QA, machine learning, quality assurance, radiomics

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