ArticleAJNR. American journal of neuroradiology2015
MRI texture analysis predicts p53 status in head and neck squamous cell carcinoma.
Article in AJNR. American journal of neuroradiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 90 citations in OpenAlex.
- Whole-tumor histogram analysis of synthetic MRI for discriminating benign from malignant sinonasal tumors: correlations with histopathologic factors.Quantitative imaging in medicine and surgery · 2025Article
- Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.Cancer medicine · 2025Article
- Role of artificial intelligence in cancer detection using protein p53: A Review.Molecular biology reports · 2024Review
- Texture analysis using short-tau inversion recovery magnetic resonance images to differentiate squamous cell carcinoma of the gingiva from medication-related osteonecrosis of the jaw.Oral radiology · 2024Article
- Radiomics-based model for prediction of TGF-β1 expression in head and neck squamous cell carcinoma.American journal of nuclear medicine and molecular imaging · 2024Article
- Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date.Infectious agents and cancer · 2023Review
- Construction of an enhanced computed tomography radiomics model for non-invasively predicting granzyme A in head and neck squamous cell carcinoma by machine learning.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- Review
- Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features.Frontiers in oncology · 2023Article
- Article
- Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature.Cancers · 2022Review
- Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps.Biomedical engineering online · 2022Article
- MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain.European radiology · 2022Article
- Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image.Frontiers in oncology · 2022Article
- [Advances in head and neck squamous cell cancer research based on radiomics].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2021Review
- Differentiating TP53 Mutation Status in Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI-Derived Radiomics.Frontiers in oncology · 2021Article
- The Feasibility of Combining ADC Value With Texture Analysis of TFrontiers in oncology · 2021Article
- Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeHead and neck cancer is common, and understanding the prognosis is an important part of patient management. In addition to the Tumor, Node, Metastasis staging system, tumor biomarkers are becoming more useful in understanding prognosis and directing treatment. We assessed whether MR imaging texture analysis would correctly classify oropharyngeal squamous cell carcinoma according to p53 status. MATERIALS AND
methodsA cohort of 16 patients with oropharyngeal squamous cell carcinoma was prospectively evaluated by using standard clinical, histopathologic, and imaging techniques. Tumors were stained for p53 and scored by an anatomic pathologist. Regions of interest on MR imaging were selected by a neuroradiologist and then analyzed by using our 2D fast time-frequency transform tool. The quantified textures were assessed by using the subset-size forward-selection algorithm in the Waikato Environment for Knowledge Analysis. Features found to be significant were used to create a statistical model to predict p53 status. The model was tested by using a Bayesian network classifier with 10-fold stratified cross-validation.
resultsFeature selection identified 7 significant texture variables that were used in a predictive model. The resulting model predicted p53 status with 81.3% accuracy (P < .05). Cross-validation showed a moderate level of agreement (κ = 0.625).
conclusionsThis study shows that MR imaging texture analysis correctly predicts p53 status in oropharyngeal squamous cell carcinoma with ∼80% accuracy. As our knowledge of and dependence on tumor biomarkers expand, MR imaging texture analysis warrants further study in oropharyngeal squamous cell carcinoma and other head and neck tumors.
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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.