ArticleContrast media & molecular imaging2018
Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study.
Article in Contrast media & molecular imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
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- Global and regional accuracy of deep learning-based tumor segmentation from whole-body [EJNMMI research · 2025Article
- MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Automated Segmentation of Sacral Chordoma and Surrounding Muscles Using Deep Learning Ensemble.International journal of radiation oncology, biology, physics · 2023Article
- Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers.Tomography (Ann Arbor, Mich.) · 2023Article
- Deep Learning Architecture to Improve Edge Accuracy of Auto-Contouring for Head and Neck Radiotherapy.Diagnostics (Basel, Switzerland) · 2023Article
- Role of PET/CT in Oropharyngeal Cancers.Cancers · 2023Review
- External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions onFrontiers in medicine · 2023Article
- A Deep Learning-Aided Automated Method for Calculating Metabolic Tumor Volume in Diffuse Large B-Cell Lymphoma.Cancers · 2022Article
- Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2022Article
- Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry.Clinical and translational radiation oncology · 2022Article
- BID-Net: An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma Using Deep Learning.Computational intelligence and neuroscience · 2022Article
- Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer.Frontiers in oncology · 2022Article
- How molecular imaging will enable robotic precision surgery : The role of artificial intelligence, augmented reality, and navigation.European journal of nuclear medicine and molecular imaging · 2021Review
- Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.European journal of nuclear medicine and molecular imaging · 2021Article
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Authors and funding
11 authors.
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Abstract
Purpose: In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods: PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results: A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm ( Conclusion: A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.