ArticleAJNR. American journal of neuroradiology2017
CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy.
Article in AJNR. American journal of neuroradiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The application of radiomics in laryngeal cancer.The British journal of radiology · 2021Pooled it
- Diagnostic Accuracy of Combined PET/CT with MRI, 18F-FDG PET/MRI, and 18F-FDG PET/CT in Patients with Oropharyngeal and Hypopharyngeal Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.Contrast media & molecular imaging · 2021Pooled it
- Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography.European radiology · 2021Trial
- Machine Learning of Echocardiographic Left Ventricular Myocardial Radiomics for Identification of Transthyretin Amyloid Cardiomyopathy.JACC. Advances · 2026Article
- Determining Individualized Prognosis of Head and Neck Squamous Cell Carcinoma Patients Treated with Radiotherapy Using a Prediction Model Based on Body Composition Features: Introducing a Probability Calculator.Journal of imaging informatics in medicine · 2026Article
- Integrating CT radiomics and morphologic features for preoperative risk stratification of gastrointestinal stromal tumours.Scientific reports · 2026Article
- Radiomics in laryngeal squamous cell carcinoma: state of the art.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Multimodal Radiogenomic Imaging in Oropharyngeal Squamous Cell Carcinoma: Implications for Dentomaxillofacial Radiology.Medical sciences (Basel, Switzerland) · 2026Review
- Review
- Machine learning-based clinical-radiomics model for predicting recurrence risk after radical surgery in sinonasal squamous cell carcinoma: a preliminary 2-year follow-up study.BMC medical imaging · 2025Article
- Improving outcome prediction in oropharyngeal carcinoma through the integration of diffusion-weighted magnetic resonance imaging radiomics.Physics and imaging in radiation oncology · 2025Article
- Application of Radiomics in Predicting the Prognosis of Medulloblastoma in Children.Children (Basel, Switzerland) · 2025Article
- Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions.Journal of imaging · 2024Review
- Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis.Journal of clinical medicine · 2024Article
- Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date.Infectious agents and cancer · 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
- Magnetic resonance imaging in naso-oropharyngeal carcinoma: role of texture analysis in the assessment of response to radiochemotherapy, a preliminary study.La Radiologia medica · 2023Observational
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Abstract
background and purposeThe accurate prediction of prognosis and failure is crucial for optimizing treatment strategies for patients with cancer. The purpose of this study was to assess the performance of pretreatment CT texture analysis for the prediction of treatment failure in primary head and neck squamous cell carcinoma treated with chemoradiotherapy. MATERIALS AND
methodsThis retrospective study included 62 patients diagnosed with primary head and neck squamous cell carcinoma who underwent contrast-enhanced CT examinations for staging, followed by chemoradiotherapy. CT texture features of the whole primary tumor were measured using an in-house developed Matlab-based texture analysis program. Histogram, gray-level co-occurrence matrix, gray-level run-length, gray-level gradient matrix, and Laws features were used for texture feature extraction. Receiver operating characteristic analysis was used to identify the optimal threshold of any significant texture parameter. We used multivariate Cox proportional hazards models to examine the association between the CT texture parameter and local failure, adjusting for age, sex, smoking, primary tumor stage, primary tumor volume, and human papillomavirus status.
resultsTwenty-two patients (35.5%) developed local failure, and the remaining 40 (64.5%) showed local control. Multivariate analysis revealed that 3 histogram features (geometric mean [hazard ratio = 4.68,
conclusionsIndependent primary tumor CT texture analysis parameters are associated with local failure in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.
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