A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer

•A deep learning network facilitated dose calculation from CBCT.•A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.•Generation of synthe...

Full description

Saved in:
Bibliographic Details
Published in:Physics and imaging in radiation oncology Vol. 14; pp. 24 - 31
Main Authors: Maspero, Matteo, Houweling, Antonetta C., Savenije, Mark H.F., van Heijst, Tristan C.F., Verhoeff, Joost J.C., Kotte, Alexis N.T.J., van den Berg, Cornelis A.T.
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.04.2020
Elsevier
Subjects:
Adaptive radiotherapy
Artificial intelligence
CBCT
Deep learning
Dose calculation
Image-guided radiotherapy
Image-to-image translation
Machine learning
Original
Deep learning
Image-to-image translation
Image-guided radiotherapy
Dose calculation
Machine learning
Artificial intelligence
Adaptive radiotherapy
CBCT
ISSN:2405-6316, 2405-6316
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A deep learning network facilitated dose calculation from CBCT.•A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.•Generation of synthetic-CT can be achieved within 10 s, facilitating online adaptive radiotherapy scenarios. Background and purpose Adaptive radiotherapy based on cone-beam computed tomography (CBCT) requires high CT number accuracy to ensure accurate dose calculations. Recently, deep learning has been proposed for fast CBCT artefact corrections on single anatomical sites. This study investigated the feasibility of applying a single convolutional network to facilitate dose calculation based on CBCT for head-and-neck, lung and breast cancer patients. Materials and Methods Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. The CBCTs were registered to planning CT according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an unpaired manner on 15 patients per anatomical site generating synthetic-CTs (sCTs). Another network was trained with all the anatomical sites together. Performances of all four networks were compared and evaluated for image similarity against rescan CT (rCT). Clinical plans were recalculated on rCT and sCT and analysed through voxel-based dose differences and γ-analysis. Results A sCT was generated in 10 s. Image similarity was comparable between models trained on different anatomical sites and a single model for all sites. Mean dose differences <0.5% were obtained in high-dose regions. Mean gamma (3%, 3 mm) pass-rates >95% were achieved for all sites. Conclusion Cycle-GAN reduced CBCT artefacts and increased similarity to CT, enabling sCT-based dose calculations. A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2405-6316
2405-6316
DOI:10.1016/j.phro.2020.04.002