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Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

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Deep learning-based dose prediction for prostate cancer with empty bladder protocol: a framework for efficient and

Byongsu Choi¹, Deepak K Shrestha¹, Albert Attia¹

¹Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, United States.

Frontiers in Oncology

|January 2, 2026

Summary

Deep learning models for prostate cancer radiotherapy show improved accuracy when fine-tuned with empty bladder (EB) data. This enhances treatment planning and patient care for radiation therapy (RT).

Keywords:

deep learning dose prediction empty bladder particle therapy prostate radiation therapy

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Area of Science:

Radiation Oncology
Medical Physics
Artificial Intelligence in Medicine

Background:

Radiation therapy (RT) is crucial for prostate cancer, traditionally using a full bladder (FB) protocol.
Maintaining consistent bladder filling for FB protocols is challenging, causing workflow issues and variable outcomes.
Emerging evidence suggests empty bladder (EB) protocols offer comparable toxicity and improved patient comfort.

Purpose of the Study:

To develop and evaluate a deep learning (DL) model for accurate dose prediction in prostate cancer RT using EB protocols.
To address the anatomical variability inherent in EB protocols through model fine-tuning.

Main Methods:

A conditional generative adversarial network (cGAN) with a 3D U-Net architecture was employed.
The model was initially trained on 90 FB cases and subsequently fine-tuned on 20 EB cases (stratified for SBRT/IMRT).
Performance was assessed using mean absolute percentage error (MAPE) and dose-volume histogram (DVH) metrics against manual plans.

Main Results:

The EB fine-tuned model achieved superior accuracy (MAPE 3.53 ± 0.40%) compared to the FB-trained model (MAPE 4.87 ± 0.86%).
DVH analysis showed improved agreement with manual plans for target volumes and organs at risk (discrepancies within 2.5 Gy or 3%).

Conclusions:

Fine-tuning DL models with EB-specific data significantly enhances prediction accuracy and clinical relevance for prostate cancer RT.
The developed framework supports efficient EB treatment planning, quality assurance, and facilitates wider adoption of patient-centered radiotherapy.