ArticleJapanese journal of radiology2021
Convolutional neural network in nasopharyngeal carcinoma: how good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI?
Article in Japanese journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of AI-Driven Applications for Screening Nasopharyngeal Carcinoma Using MRI.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- Deep Invasion Volume of the Primary Nasopharyngeal Carcinoma Predicts Treatment Outcome.AJNR. American journal of neuroradiology · 2025Article
- Stereotactic arrhythmia radioablation for ventricular tachycardia: a review of clinical trials and emerging roles of imaging.Journal of radiation research · 2025Review
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2024Review
- Revolutionizing radiation therapy: the role of AI in clinical practice.Journal of radiation research · 2024Review
- Fairness of artificial intelligence in healthcare: review and recommendations.Japanese journal of radiology · 2024Review
- Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review.Cancers · 2023Review
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.European radiology · 2023Article
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.Journal of clinical medicine · 2023Review
- Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion.Briefings in bioinformatics · 2022Article
- Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management - A Systematic Review.Cancer management and research · 2022Review
- A convolutional neural network combined with positional and textural attention for the fully automatic delineation of primary nasopharyngeal carcinoma on non-contrast-enhanced MRI.Quantitative imaging in medicine and surgery · 2021Article
- A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeConvolutional neural networks (CNNs) show potential for delineating cancers on contrast-enhanced MRI (ce-MRI) but there are clinical scenarios in which administration of contrast is not desirable. We investigated performance of the CNN for delineating primary nasopharyngeal carcinoma (NPC) on non-contrast-enhanced images and compared the performance to that on ce-MRI. MATERIALS AND
methodsWe retrospectively analyzed primary NPC in 195 patients using a well-established CNN, U-Net, for tumor delineation on the non-contrast-enhanced fat-suppressed (fs)-T2W, ce-T1W and ce-fs-T1W images. The CNN-derived delineations were compared to manual delineations to obtain Dice similarity coefficient (DSC) and average surface distance (ASD). The DSC and ASD on fs-T2W were compared to those on ce-MRI. Primary tumor volumes (PTVs) of CNN-derived delineations were compared to that of manual delineations.
resultsThe CNN for NPC delineation on fs-T2W images showed similar DSC (0.71 ± 0.09) and ASD (0.21 ± 0.48 cm) to those on ce-T1W images (0.71 ± 0.09 and 0.17 ± 0.19 cm, respectively) (p > 0.05), and lower DSC but similar ASD to ce-fs-T1W images (0.73 ± 0.09, p < 0.001; and 0.17 ± 0.20 cm, p > 0.05). The CNN overestimated PTVs on all sequences (p < 0.001).
conclusionThe CNN showed promise for NPC delineation on fs-T2W images in cases where it is desirable to avoid contrast agent injection. The CNN overestimated PTVs on all sequences.
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Registered trials
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