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nnDoseNet: Intuitive and flexible deep learning framework to train and evaluate radiotherapy dose prediction models.

Ho-Hsin Chang¹, Joseph Harms², Rex A Cardan³

¹Department of Biomedical Engineering, University of Alabama at Birmingham, AL, USA; Department of Radiation Oncology, University of Alabama at Birmingham, AL, USA.

Computers in Biology and Medicine

|October 28, 2025

Summary

nnDoseNet automates radiotherapy dose prediction using deep learning, streamlining a labor-intensive process. This AI framework offers a flexible and performant solution for developing and comparing advanced dose prediction methods.

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Area of Science:

Medical Physics
Radiotherapy
Artificial Intelligence

Background:

Radiotherapy (RT) dose optimization is a complex, manual process requiring significant time and expertise.
Achieving clinically acceptable RT plans often involves iterative adjustments.

Purpose of the Study:

To introduce nnDoseNet, a deep learning framework for automated radiotherapy dose prediction.
To streamline and accelerate the process of RT dose optimization.

Main Methods:

nnDoseNet adapts the nnU-Net segmentation architecture for dose regression.
Utilizes specialized loss functions, including dose-volume histogram (DVH) terms.
Incorporates multi-channel input (CT, targets, organs-at-risk, body mask) and supports clinical evaluation metrics.

Main Results:

Achieved competitive performance on the OpenKBP challenge dataset for head-and-neck cancer.
Demonstrated good agreement with clinical dose distributions in prostate cancer patients.
The best configuration yielded a dose score of 2.526 and a DVH score of 1.55 on the test set.

Conclusions:

nnDoseNet simplifies the development and testing of dose prediction models through automated preprocessing, training, and evaluation.
The framework is flexible, accommodating diverse clinical scenarios and hardware.
Aims to accelerate the integration of AI in radiotherapy dose prediction, serving as a benchmark for multi-institutional research.