Single- versus multi-model in the deep learning prediction of monitor units per control point for automated treatment planning in prostate cancer

Gaudreault, M; McIntosh, L; Woodford, K; Li, J; Harden, S; Porceddu, S; Panettieri, V; Hardcastle, N

Author(s): Gaudreault, M; McIntosh, L; Woodford, K; Li, J; Harden, S; Porceddu, S; Panettieri, V; Hardcastle, N;
Details: Publication Year 2025-09,Volume 26,Issue #9,Page e70229
Journal Title: Journal of Applied Clinical Medical Physics
Publication Type: Research article
Abstract: 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.
Publisher: Wiley
Keywords: Humans; Male; *Prostatic Neoplasms/radiotherapy; *Radiotherapy Planning, Computer-Assisted/methods; *Deep Learning; Radiotherapy Dosage; *Radiotherapy, Intensity-Modulated/methods; Organs at Risk/radiation effects; Algorithms; Artificial intelligence; Deep learning; Monitor units per control point; Prostate cancer
Department(s): Radiation Oncology; Physical Sciences; Laboratory Research; Radiation Therapy
Publisher's Version: https://doi.org/10.1002/acm2.70229
Open Access at Publisher's Site: https://doi.org/10.1002/acm2.70229
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2025-10-21 02:34:15

Last Modified: 2025-10-21 02:34:24