In:
Journal of Applied Clinical Medical Physics, Wiley
Kurzfassung:
Deep learning‐based virtual patient‐specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefore, a deep learning‐based model that uses multileaf collimator (MLC) information per control point and dose distribution in patient's CT as inputs was developed. Methods Overall, 96 volumetric‐modulated arc therapy plans generated for prostate cancer treatment were used. We developed a model (Model 1) that can predict measurement‐based gamma passing rate (GPR) for a treatment plan using data stored as a map reflecting the MLC leaf position at each control point (MLPM) and data of the dose distribution in patient's CT as inputs. The evaluation of the model was based on the mean absolute error (MAE) and Pearson's correlation coefficient ( r ) between the measured and predicted GPR. For comparison, we also analyzed models trained with the dose distribution in patient's CT alone (Model 2) and with dose distributions recalculated on a virtual phantom CT (Model 3). Results At the 2%/2 mm criterion, MAE[%] and r for Model 1, Model 2, and Model 3 were 2.32% ± 0.43% and 0.54 ± 0.03, 2.70% ± 0.26%, and 0.32 ± 0.08, and 2.96% ± 0.23% and 0.24 ± 0.22, respectively; at the 3%/3 mm criterion, these values were 1.25% ± 0.05% and 0.36 ± 0.18, 1.57% ± 0.35% and 0.19 ± 0.20, and 1.39% ± 0.32% and 0.17 ± 0.22, respectively. This result showed that Model 1 exhibited the lowest MAE and highest r at both criteria of 2%/2 mm and 3%3 mm. Conclusions These findings showed that a model that combines the MLPM and dose distribution in patient's CT exhibited a better GPR prediction performance compared with the other two studied models.
Materialart:
Online-Ressource
ISSN:
1526-9914
,
1526-9914
Sprache:
Englisch
Verlag:
Wiley
Publikationsdatum:
2023
ZDB Id:
2010347-5
