To develop a deep learning model capable of producing clinically acceptable dose distributions for left-sided breast cancers for 3D-CRT while exploring the use of two-dimensional versus three-dimensional anatomical data.
Two deep learning models, a two-dimensional and three-dimensional model, based on U-net architecture were trained to predict dose distribution given anatomical information and dose prescription. The input consists of 6 channels including the patient CT along with binary masks for four OARs and one covering the volume receiving 95% dose (based on the clinical plan). A training set of 120 patients was compiled and used with 5-fold cross validation. The best performing model from the 5 folds was analyzed with a test set of 25 patients using cumulative DVH, mean differences in mean dose to OARs represented by box plots, and V20 of the left lung.
We have shown that both models are capable of producing clinically acceptable dose distributions, with the 3D outperforming the 2D model. The average dose difference for mean dose is within 0.02% of the dose prescription for both models. The V20 from the predicted dose distributions are comparable with the V20 from clinical plans, where predictions tend to be slightly under.
Based on the results, the models could be implemented clinically to produce dose distributions that can be used as a reference to ensure the most ideal plan is used. Each prediction is patient-specific while requiring minimal time and information creating a new standard in plan quality without hindering the planning process.
Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Radiation therapy dose prediction for left-sided breast cancers using two-dimensional and three-dimensional deep learning models.
