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). 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. 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. 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. Accurate prediction of MU per CP is feasible in VMAT prostate cancer treatment.
Gaudreault et al. (Fri,) studied this question.