Journal of applied clinical medical physics · 2025
Single- versus multi-model in the deep learning prediction of monitor units per control point for automated treatment planning in prostate cancer.
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Gaudreault Mathieu, McIntosh Lachlan, Woodford Katrina, Li Jason, Harden Susan, Porceddu Sandro, Panettieri Vanessa, Hardcastle Nicholas
Аннотация
BACKGROUND: In contemporary radiation therapy, the radiation is modulated to conform the prescription dose to the tumor and spare organs at risk. The modulation results from a complex mathematical calculation that requires several iterations to reach a satisfactory solution, delaying treatment. The monitor units (MU) per control point (CP) control the dose magnitude and may be predicted by deep learning, a type of artificial intelligence (AI).
PURPOSE: To introduce deep learning methods to predict the MU per CP in the context of AI volumetric modulated arc therapy (VMAT) treatment plan prediction for prostate cancer.
METHODS: Patients treated for prostate cancer with 60 Gy in 20 fractions between 01/2019 and 06/2024 were considered for inclusion. Two approaches were considered: a single-model approach, trained on all samples, and a multi-model approach, with separate models trained by CP. The inputs were either the three-dimensional (3D) dose per CP (3D single-model / 3D multi-model) or the two-dimensional (2D) average dose intensity projection per CP (2D single-model / 2D multi-model). The outputs were the MU per CP, which were converted to meterset weight per CP and MU per beam to create an AI-Radiation Therapy Plan (AI-RTPlan) with other clinical parameters retained. Clinical goals achieved with the calculated dose distribution from the AI-RTPlan and clinical plan were compared.
RESULTS: The cohort was split into 220/40/42 homogeneous plans in the training/validation/testing dataset. Relative to the clinical case, the errors in meterset weight per CP were mean ± SD = -0.4 ± 3.8%/-0.2 ± 4.8% in 2D/3D single-model and 0.01 ± 3.9%/-0.1 ± 5.0% in 2D/3D multi-model. The errors in MU per beam were -0.9 ± 5.5%/-1.2 ± 4.5% in 2D/3D single-model and 0.4 ± 4.8%/0.5 ± 5.2% in 2D/3D multi-model. In 2D/3D models, at least 93%/81% of patients had the same or more clinical goals achieved with AI-RTPlans.
CONCLUSIONS: Accurate prediction of MU per CP is feasible in VMAT prostate cancer treatment.
PURPOSE: To introduce deep learning methods to predict the MU per CP in the context of AI volumetric modulated arc therapy (VMAT) treatment plan prediction for prostate cancer.
METHODS: Patients treated for prostate cancer with 60 Gy in 20 fractions between 01/2019 and 06/2024 were considered for inclusion. Two approaches were considered: a single-model approach, trained on all samples, and a multi-model approach, with separate models trained by CP. The inputs were either the three-dimensional (3D) dose per CP (3D single-model / 3D multi-model) or the two-dimensional (2D) average dose intensity projection per CP (2D single-model / 2D multi-model). The outputs were the MU per CP, which were converted to meterset weight per CP and MU per beam to create an AI-Radiation Therapy Plan (AI-RTPlan) with other clinical parameters retained. Clinical goals achieved with the calculated dose distribution from the AI-RTPlan and clinical plan were compared.
RESULTS: The cohort was split into 220/40/42 homogeneous plans in the training/validation/testing dataset. Relative to the clinical case, the errors in meterset weight per CP were mean ± SD = -0.4 ± 3.8%/-0.2 ± 4.8% in 2D/3D single-model and 0.01 ± 3.9%/-0.1 ± 5.0% in 2D/3D multi-model. The errors in MU per beam were -0.9 ± 5.5%/-1.2 ± 4.5% in 2D/3D single-model and 0.4 ± 4.8%/0.5 ± 5.2% in 2D/3D multi-model. In 2D/3D models, at least 93%/81% of patients had the same or more clinical goals achieved with AI-RTPlans.
CONCLUSIONS: Accurate prediction of MU per CP is feasible in VMAT prostate cancer treatment.