Deep Learning-Based Dose Prediction for Prostate Cancer with Empty Bladder Protocol: A Framework for Efficient and Personalized Radiotherapy Planning

Byongsu Choi¹Deepak Shrestha¹Albert Attia¹Brad Stish²James Leenstra³Jean Claude Rwigema⁴Jiansen Ma⁵Sung Uk Lee⁶Jong Hwi Jeong^6*Jongeun Kim⁶JeongHeon Kim¹Chris Beltran¹Justin C. Park¹

• ¹Mayo Clinic Florida, Jacksonville, United States
• ²Mayo Clinic Minnesota, Rochester, United States
• ³Mayo Clinic, Northfield, United States
• ⁴Mayo Clinic Arizona, Arizona, United States
• ⁵Mayo Clinic Department of Quantitative Health Sciences, Rochester, United States
• ⁶Proton Therapy Center, National Cancer Center, Goyang-si, Republic of Korea

Radiation therapy (RT) is a cornerstone in the management of localized and locally advanced prostate cancer, traditionally delivered with a full bladder (FB) protocol to reduce radiation exposure to surrounding organs. However, consistent bladder filling is difficult to maintain, leading to workflow delays, anatomical inconsistencies, and variable toxicity outcomes. Recent evidence, including the ongoing RELIEF trial at Mayo Clinic, suggests that an empty bladder (EB) protocol provides comparable toxicity outcomes to FB while improving patient comfort and treatment consistency. To address the increased anatomical variability associated with EB protocols, we developed a deep learning (DL)-based dose prediction model tailored to EB patients. A conditional generative adversarial network (cGAN) with a modified 3D U-Net architecture was trained on 90 FB cases and fine-tuned on 20 EB cases stratified into stereotactic body radiotherapy (SBRT) and intensity-modulated radiotherapy (IMRT). Model performance was evaluated against clinical manual plans using mean absolute percentage error (MAPE) and dose-volume histogram (DVH) metrics. The EB Fine-tuning model(SBRT/IMRT) achieved superior accuracy compared with the general FB-trained model, with an average MAPE of 3.53 ± 0.40% versus 4.87 ± 0.86%. DVH analyses demonstrated improved agreement with manual plans for planning target volumes and organs at risk, with discrepancies consistently within 2.5 Gy or 3%. These results demonstrate that fine-tuning with EB-specific data enhances prediction accuracy and clinical relevance of the DL-based model. The proposed framework supports efficient EB treatment planning, provides reference dose distributions for quality assurance, and offers educational value to clinicians adopting EB protocols. By combining automation with clinical applicability, this approach facilitates broader adoption of EB radiotherapy in prostate cancer while improving workflow reproducibility and patient-centered care.