Gantry angle classification with a fluence map in intensity-modulated radiotherapy for prostate cases using machine learning

• Autorzy: Miura H. , Ozawa S. , Enosaki T. , Hayata M. , Yamada K. , Nagata Y.

Identyfikatory

DOI

10.2478/pjmpe-2018-0023

• Języki publikacji: EN

Abstrakty

EN

We investigated the gantry-angle classifier performance with a fluence map using three machine-learning algorithms, and compared it with human performance. Eighty prostate cases were investigated using a seven-field-intensity modulated radiotherapy treatment (IMRT) plan with beam angles of 0°, 50°, 100°, 155°, 205°, 260°, and 310°. The k- nearest neighbor (k-NN), logistic regression (LR), and support vector machine (SVM) algorithms were used. In the observer test, three radiotherapists assessed the gantry angle classification in a blind manner. The precision and recall rates were calculated for the machine learning and observer test. The average precision rate of the k-NN and LR algorithms were 94.8% and 97.9%, respectively. The average recall rate of the k-NN and LR algorithms were 94.3% and 97.9%, respectively. The SVM had 100% precision and recall rates. The gantry angles of 0°, 155°, and 205° had an accuracy of 100% in all algorithms. In the observer test, average precision and recall rates were 82.6% and 82.6%, respectively. All observers could easily classify the gantry angles of 0°, 155°, and 205° with a high degree of accuracy. Misclassifications occurred in gantry angles of 50°, 100°, 260°, and 310°. Machine learning could better classify gantry angles for prostate IMRT than human beings. In particular, the SVM algorithm had a perfect classification of 100%

Słowa kluczowe

EN

classification; machine learning; fluence map; IMRT

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2018
• Tom: Vol. 24, Iss. 4
• Strony: 165--169
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Miura H.

• Hiroshima High-Precision Radiotherapy Cancer Center, 3-2-2, Futabanosato, Higashi-ku Hiroshima 732-0057, Japan
• Department of Radiation Oncology, Institute of Biomedical & Health Science, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

autor

Ozawa S.

• Hiroshima High-Precision Radiotherapy Cancer Center, 3-2-2, Futabanosato, Higashi-ku Hiroshima 732-0057, Japan
• Department of Radiation Oncology, Institute of Biomedical & Health Science, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

autor

Enosaki T.

• Hiroshima High-Precision Radiotherapy Cancer Center, 3-2-2, Futabanosato, Higashi-ku Hiroshima 732-0057, Japan

autor

Hayata M.

• Hiroshima High-Precision Radiotherapy Cancer Center, 3-2-2, Futabanosato, Higashi-ku Hiroshima 732-0057, Japan

autor

Yamada K.

• Hiroshima High-Precision Radiotherapy Cancer Center, 3-2-2, Futabanosato, Higashi-ku Hiroshima 732-0057, Japan

autor

Nagata Y.

• Hiroshima High-Precision Radiotherapy Cancer Center, 3-2-2, Futabanosato, Higashi-ku Hiroshima 732-0057, Japan
• Department of Radiation Oncology, Institute of Biomedical & Health Science, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

Bibliografia

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