Highlights
- •A deep learning-based method was developed for VMAT dose distribution.
- •A structure loss (Lstruct) was proposed to calculate errors per input contour labels.
- •Model performance of Lstruct was compared with a standard L2 loss.
- •Lstruct model yielded more accurate dose distributions and outperformed L2 loss.
Abstract
Purpose
Deep learning (DL)-based dose distribution prediction can potentially reduce the cost
of inverse planning process. We developed and introduced a structure-focused loss
(Lstruct) for 3D dose prediction to improve prediction accuracy. This study investigated the
influence of Lstruct on DL-based dose prediction for patients with prostate cancer. The proposed Lstruct, which is similar in concept to dose–volume histogram (DVH)-based optimization in
clinical practice, has the potential to provide more interpretable and accurate DL-based
optimization.
Methods
This study involved 104 patients who underwent prostate radiotherapy. We used 3D U-Net-based
architecture to predict dose distributions from computed tomography and contours of
the planning target volume and organs-at-risk. We trained two models using different
loss functions: L2 loss and Lstruct. Predicted doses were compared in terms of dose–volume parameters and the Dice similarity
coefficient of isodose volume.
Results
DVH analysis showed that the Lstruct model had smaller errors from the ground truth than the L2 model. The Lstruct model achieved more consistent dose distributions than the L2 model, with errors
close to zero. The isodose Dice score of the Lstruct model was greater than that of the L2 model by >20% of the prescribed dose.
Conclusions
We developed Lstruct using labels of inputted contours for DL-based dose prediction for prostate radiotherapy.
Lstruct can be generalized to any DL architecture, thereby enhancing the dose prediction
accuracy.
Keywords
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Article info
Publication history
Published online: February 10, 2023
Accepted:
February 3,
2023
Received in revised form:
January 18,
2023
Received:
October 6,
2022
Identification
Copyright
© 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
