<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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%.</p></abstract>

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%.

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

Hiroshima High-Precision Radiotherapy Cancer Center

Department of Radiation Oncology, Institute of Biomedical & Health Science

Polish Journal of Medical Physics and Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

We investigated the gantry-angle classifier performance with a fluence map using three machine-learning algorithms, and compared it with human performance....

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

Data publikacji: 24 gru 2018

Zakres stron: 165 - 169

Otrzymano: 01 lut 2018

Przyjęty: 31 paź 2018

DOI: https://doi.org/10.2478/pjmpe-2018-0023

Słowa kluczowe
classification, machine learning, fluence map, IMRT

© 2018 Hideharu Miura et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Data publikacji: 24 gru 2018

Zakres stron: 165 - 169

Otrzymano: 01 lut 2018

Przyjęty: 31 paź 2018

DOI: https://doi.org/10.2478/pjmpe-2018-0023

Słowa kluczoweclassification, machine learning, fluence map, IMRT

© 2018 Hideharu Miura et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
classification, machine learning, fluence map, IMRT