ArticleEuropean radiology2019
Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it, 116 citations in OpenAlex.
- Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign.BMC cancer · 2023Trial
- Multi-regional radiomics based on planning CT and information complementarity between GTVp and GTVn: an explainable model for predicting 5-year recurrence risk in HPV-positive oropharyngeal cancer.Radiation oncology (London, England) · 2026Article
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Deep Learning-Based Detection of Malignant and Equivocal Cervical Lymph Nodes on CT Imaging Using a 2D U-Net++ Model.Maedica · 2026Article
- Predictive Value of a Radiomics-Derived Risk Score for Local Progression in T3 Laryngeal Cancer: A 10-Year Single-Center Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- CT-based machine learning radiomics modeling to screen for lumbar spine osteoporosis.Frontiers in medicine · 2026Article
- Machine Learning based Radiomics from Multi-parametric Magnetic Resonance Imaging for Predicting Lymph Node Metastasis in Cervical Cancer.Current medical imaging · 2025Article
- Development and Validation of a Computed Tomography-based Radiomics Nomogram for Diagnosing Cervical Lymph Node Metastasis in Oropharyngeal Squamous Cell Carcinomas.Advances in radiation oncology · 2025Article
- Discovery and validation of cell-free DNA methylation markers for specific diagnosis, differentiation from benign tumors, and prognosis of breast cancer.Breast cancer research : BCR · 2025Article
- Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.The Laryngoscope · 2025Article
- Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2024Article
- Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer.European radiology · 2024Article
- Impact of multi-parameter images obtained from dual-energy CT on radiomics to predict pathological grading of bladder urothelial carcinoma.Abdominal radiology (New York) · 2024Article
- Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.Abdominal radiology (New York) · 2024Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Assessing muscle invasion in bladder cancer via virtual biopsy: a study on quantitative parameters and classical radiomics features from dual-energy CT imaging.BMC medical imaging · 2024Article
- Article
- Radiomics-based quantitative contrast-enhanced CT analysis of abdominal lymphadenopathy to differentiate tuberculosis from lymphoma.Precision clinical medicine · 2024Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 3 countries.
Funding
Abstract
objectivesThis study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction.
methodsEighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck.
resultsDepending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy.
conclusionsMulti-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone. KEY POINTS: • Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.
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Registered trials
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.