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A deep learning method for predicting proton beam range and spread-out Bragg peak in passive scattering mode

Journal of the Korean Physical Society, ISSN: 1976-8524, Vol: 85, Issue: 3, Page: 256-266
2024
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Article Description

It is difficult to calculate monitor units in the proton treatment planning system due to the complexity of using this system in the double scattering mode of proton therapy. Moreover, the range and spread-out Bragg peak (SOBP) values using the conversion algorithm (CONVALGO) provided by IBA (C, C) are different from the actual measured range (M) and SOBP (M) values. In this regard, the CONVALGO (FC) value (FC, FC) should be measured according to the quality assurance (QA) of patient treatment, which requires physical effort and time. This study, therefore, aimed to reduce the time and effort spent on QA. The predictive model was trained using six parameters. Main option, sub-option, M and M were used as input values, and FC and FC were used as label. The trained model predicted the CONVALGO (PC) values of PC and PC. The test dataset has 261 patient data that were not used for training. Difference, mean absolute error (MAE), and root mean square error (RMSE) values were used for comparison. Compared to the FC value, the maximum difference was − 2.2 mm for PC and − 3.4 mm for C. The acceptable standard of patient QA in our institute is within 1 mm and the number of data points that met the acceptable standard was 196 for PC and 191 for C. For the MAE of PC, options 1, 2, and 3 showed values within 1 mm. In the MAE of C, the values were > 1 mm for all options.

Bibliographic Details

Young Kyu Lee; Wonjoong Cheon; Young Nam Kang; Sang Hee Ahn; Chankyu Kim; Haksoo Kim; Se Byeong Lee; Young Kyung Lim; Jong Hwi Jeong; Dongho Shin

Springer Science and Business Media LLC

Physics and Astronomy

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