A deep learning-driven automated treatment planning framework for patient treated with radiotherapy in cervical cancer

Background and purpose: The rapid and efficient generation of high-quality, dose-consistency volumetric modulated arc therapy (VMAT) plans remains challenging in radiotherapy. This study proposes a deep learning (DL) end-to-end (E2E) auto-planning framework and validate its practicality and feasibility for clinical implementation. Materials and methods: A total of 458 cervical cancer VMAT plans were enrolled and split into training, validation, and test cohorts. An E2E auto-planning framework with a two-stage cascaded deep learning (DL) network was developed: Stage 1 predicted coarse dose from CT and structure masks, and Stage 2 refined it using four beam-band priors and a composite loss. Dose-volume histogram (DVH) endpoints from refined predicted dose were converted into Monaco objectives via a scripting module for iterative optimization. Performance was evaluated with Dose, DVH, and snDVH scores, ablations, and comparisons with manual plans in terms of quality, clinical evaluation and deliverability. Results: The proposed DL method achieved the best performance, with Dose score, DVH score and snDVH score of 2.114 ± 0.218 Gy, 1.194 ± 0.295 Gy and 2.027 ± 0.586, respectively. Compared with manual plans, E2E auto-plans preserved target volume coverage while reducing all DVH metrics for bladder, rectum, small intestine, and spinal cord by 2% - 35% (all p < 0.05). The gamma passing rate of E2E auto-plans was higher than manual plans in the 3%/3 mm gamma criterion (98.1% vs 97.9%). Conclusion: The proposed auto-planning framework demonstrated a high level of automation and clinical applicability, offering a reliable and promising tool to support radiotherapy workflows.