Abstract 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 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. Clinical trial number Not applicable.
Ning et al. (Tue,) studied this question.
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