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arXiv · 2106.07825

Site-Agnostic 3D Dose Distribution Prediction with Deep Learning Neural Networks

Maryam Mashayekhi·Itzel Ramirez Tapia·Anjali Balagopal·Xinran Zhong·Azar Sadeghnejad Barkousaraie·Rafe McBeth·Mu-Han Lin·Steve Jiang·Dan Nguyen
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Abstract

Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance. We propose a site-agnostic, 3D dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine-tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset.

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Maryam Mashayekhi, Itzel Ramirez Tapia, Anjali Balagopal, Xinran Zhong, Azar Sadeghnejad Barkousaraie, Rafe McBeth, Mu-Han Lin, Steve Jiang, Dan Nguyen. 2021-06-15. Site-Agnostic 3D Dose Distribution Prediction with Deep Learning Neural Networks. https://doi.org/10.1002/mp.15461

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