Abstract
Accurate clinical target volume (CTV) delineation is important for head and neck intensity-modulated
radiation therapy. However, delineation is time-consuming and susceptible to interobserver
variability (IOV). Based on a manual contouring process commonly used in clinical
practice, we developed a deep learning (DL)-based method to delineate a low-risk CTV
with computed tomography (CT) and gross tumor volume (GTV) input and compared it with
a CT-only input. A total of 310 patients with oropharynx cancer were randomly divided
into the training set (250) and test set (60). The low-risk CTV and primary GTV contours
were used to generate label data for the input and ground truth. A 3D U-Net with a
two-channel input of CT and GTV (U-NetGTV) was proposed and its performance was compared with a U-Net with only CT input (U-NetCT). The Dice similarity coefficient (DSC) and average Hausdorff distance (AHD) were
evaluated. The time required to predict the CTV was 0.86 s per patient. U-NetGTV showed a significantly higher mean DSC value than U-NetCT (0.80 ± 0.03 and 0.76 ± 0.05) and a significantly lower mean AHD value (3.0 ± 0.5
mm vs 3.5 ± 0.7 mm). Compared to the existing DL method with only CT input, the proposed
GTV-based segmentation using DL showed a more precise low-risk CTV segmentation for
head and neck cancer. Our findings suggest that the proposed method could reduce the
contouring time of a low-risk CTV, allowing the standardization of target delineations
for head and neck cancer.
Keywords
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References
- Clinical implementation of intensity-modulated arc therapy.Int J Radiat Oncol Biol Phys. 2002; 53: 453-463
- Volumetric modulated arc therapy: A review of current literature and clinical use in practice.Br J Radiol. 2011; 84: 967-996
- Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach.Int J Radiat Oncol Biol Phys. 2007; 68: 1512-1521
- Interobserver variability in delineation of target volumes in head and neck cancer.Radiother Oncol. 2019; 137: 9-15
- Delineation of the neck node levels for head and neck tumors: A 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines.Radiother Oncol. 2014; 110: 172-181
- Selection of lymph node target volumes for definitive head and neck radiation therapy: A 2019 Update.Radiother Oncol. 2019; 134: 1-9
- An evaluation of atlas selection methods for atlas-based automatic segmentation in radiotherapy treatment planning.IEEE Trans Med Imaging. 2019; 38: 2654-2664
- Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer.Med Phys. 2015; 42: 5027-5034
- Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (swallowing/mastication) structures in the head and neck.Int J Radiat Oncol Biol Phys. 2011; 81: 950-957
- Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk.Radiat Oncol. 2014; 9: 173
- Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.Med Phys. 2019; 46: 2669-2682
- Clinically applicable segmentation of head and neck anatomy for radiotherapy: Deep learning algorithm development and validation study.J Med Internet Res. 2021; 23: e26151
- Boosting-based cascaded convolutional neural networks for the segmentation of CT organs-at-risk in nasopharyngeal carcinoma.Med Phys. 2019; 46: 5602-5611
- Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI.Med Phys. 2020; 47: 4294-4302
- Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images.Front Oncol. 2017; 7: 315
- Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.Radiother Oncol. 2020; 144: 152-158
- The role of computational methods for automating and improving clinical target volume definition.Radiother Oncol. 2020; 153: 15-25
- Prescribing, recording, and reporting electron beam therapy. ICRU Report 71.J ICRU. 2004; 4: 1
- Cervical nodal metastases in squamous cell carcinoma of the head and neck: What to expect.Head Neck. 2001; 23: 995-1005
- Radiomic biomarkers to refine risk models for distant metastasis in HPV-related Oropharyngeal Carcinoma.The Cancer Imaging Archive. 2019; https://doi.org/10.7937/tcia.2019.8dho2gls
- The cancer imaging archive (TCIA): Maintaining and operating a public information repository.J Digit Imaging. 2013; 26: 1045-1057
- 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation.in: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Cham Springer, 2016: 424-432
- Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index.Acad Radiol. 2004; 11: 178-189
- On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking.Eur Radiol Exp. 2021; 5: 4
- Investigation of the freely available easy-to-use software “EZR” for medical statistics.Bone Marrow Transplant. 2013; 48: 452-458
- Elective unilateral nodal irradiation in head and neck squamous cell carcinoma: A paradigm shift.Eur J Cancer. 2017; 82: 1-5
- Selection and delineation of lymph node target volumes in head and neck conformal radiotherapy. proposal for standardizing terminology and procedure based on the surgical experience.Radiother Oncol. 2000; 56: 135-150
- Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks.Phys Med Biol. 2018; 63215026
- Auto-segmentation of low-risk clinical target volume for head and neck radiation therapy.Pract Radiat Oncol. 2014; 4: 31-37
- Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: A clinical validation.Radiat Oncol. 2013; 8: 154
- Multi-atlas segmentation of biomedical images: A survey.Med Image Anal. 2015; 24: 205-219
- Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning.Radiat Oncol. 2016; 11: 1
- Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy.NeuroImage. 2009; 46: 726-738
- Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology.Int J Radiat Oncol Biol Phys. 2021; 109: 1054-1075
- The Effect of Image Resolution on Deep Learning in Radiography.Radiol Artif Intell. 2020; 2e190015
- OctNet: Learning Deep 3D Representations at High Resolutions.Proceeding of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017; : 6620-6629
Article info
Publication history
Published online: October 21, 2022
Accepted:
September 17,
2022
Received in revised form:
February 7,
2022
Received:
October 14,
2021
Identification
Copyright
© 2022 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.
