ArticleTechnology in cancer research & treatment
The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [European journal of nuclear medicine and molecular imaging · 2022Trial
- Comparative evaluation of CNN models for nasopharyngeal carcinoma classification on pathology data.Scientific reports · 2026Article
- Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2024Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Automatic detection and recognition of nasopharynx gross tumour volume (GTVnx) by deep learning for nasopharyngeal cancer radiotherapy through magnetic resonance imaging.Radiation oncology (London, England) · 2023Article
- Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.Journal of clinical medicine · 2023Review
- Automatic segmentation of the gross target volume in radiotherapy for lung cancer using transresSEUnet 2.5D Network.Journal of translational medicine · 2022Article
- Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection.Journal of imaging · 2022Article
- Multiscale Local Enhancement Deep Convolutional Networks for the Automated 3D Segmentation of Gross Tumor Volumes in Nasopharyngeal Carcinoma: A Multi-Institutional Dataset Study.Frontiers in oncology · 2022Article
- Automatic segmentation of nasopharyngeal carcinoma on CT images using efficient UNet-2.5D ensemble with semi-supervised pretext task pretraining.Frontiers in oncology · 2022Article
- Artificial Intelligence for Classifying and Archiving Orthodontic Images.BioMed research international · 2022Article
- Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management - A Systematic Review.Cancer management and research · 2022Review
- A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging.Diagnostics (Basel, Switzerland) · 2021Review
- AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution.Frontiers in oncology · 2021Article
- A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences.Computational and mathematical methods in medicine · 2020Article
- Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning.Technology in cancer research & treatmentArticle
- Gross Tumor Volume Segmentation for Stage III NSCLC Radiotherapy Using 3D ResSE-Unet.Technology in cancer research & treatmentArticle
- Radiotherapy Treatment Planning in the Age of AI: Are We Ready Yet?Technology in cancer research & treatmentArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiotherapy is the main treatment strategy for nasopharyngeal carcinoma. A major factor affecting radiotherapy outcome is the accuracy of target delineation. Target delineation is time-consuming, and the results can vary depending on the experience of the oncologist. Using deep learning methods to automate target delineation may increase its efficiency. We used a modified deep learning model called U-Net to automatically segment and delineate tumor targets in patients with nasopharyngeal carcinoma. Patients were randomly divided into a training set (302 patients), validation set (100 patients), and test set (100 patients). The U-Net model was trained using labeled computed tomography images from the training set. The U-Net was able to delineate nasopharyngeal carcinoma tumors with an overall dice similarity coefficient of 65.86% for lymph nodes and 74.00% for primary tumor, with respective Hausdorff distances of 32.10 and 12.85 mm. Delineation accuracy decreased with increasing cancer stage. Automatic delineation took approximately 2.6 hours, compared to 3 hours, using an entirely manual procedure. Deep learning models can therefore improve accuracy, consistency, and efficiency of target delineation in T stage, but additional physician input may be required for lymph nodes.
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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.