ArticleThe British journal of radiology2018
Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study.
Article in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 3 of them syntheses that pooled it.
What it found
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Who cites it
81 citing papers in PubMed, 3 syntheses or guidelines pooled it, 149 citations in OpenAlex.
- The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment.Neuroradiology · 2022Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Quantitative diffusion magnetic resonance imaging for prediction of human papillomavirus status in head and neck squamous-cell carcinoma: A systematic review and meta-analysis.The neuroradiology journal · 2019Pooled it
- Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images.BMC medical imaging · 2021Trial
- Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer.Scientific reports · 2019Trial
- Imaging the hallmarks of cancer.Nature reviews. Cancer · 2026Review
- Advancing Coronary Risk Assessment Through Combined Radiomic, Mechanical, and Hemodynamic Analysis.Annals of biomedical engineering · 2026Article
- Review
- HPV Detection in Oropharyngeal Cancer: A Narrative Review of Diagnostic and Emerging Molecular Approaches.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting homologous recombination deficiency and treatment responses using a computed tomography-based foundation model: a preclinical study.NPJ precision oncology · 2026Article
- Review
- Advances in oral disease models: a mini-review of developments from 2015 to 2025.Frontiers in dental medicine · 2026Review
- Human Papillomavirus-Positive Oropharyngeal Cancer: Clinical and Radiologic Characteristics.Journal of the Korean Society of Radiology · 2026Review
- Radiomic-Based Machine Learning Classifiers for HPV Status Prediction in Oropharyngeal Cancer: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Radiomics and Machine Learning in Early Detection of Oral Potentially Malignant Disorders.Journal of pharmacy & bioallied sciences · 2025Review
- CT Texture Patterns Reflect HPV Status but Not Histological Differentiation in Oropharyngeal Squamous Cell Carcinoma.Cancers · 2025Article
- CT Imaging-based radiomics predicts the pain relief of Strontium-89 in treating tumor-induced bone metastases.Journal of applied clinical medical physics · 2025Article
- Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma.Scientific reports · 2025Article
- Molecular Imaging Biomarkers for Early Cancer Detection: A Systematic Review of Emerging Technologies and Clinical Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images.Scientific reports · 2024Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesHuman papillomavirus (HPV) positive oropharyngeal cancer (oropharyngeal squamous cell carcinoma, OPSCC) is biologically and clinically different from HPV negative OPSCC. Here, we evaluate the use of a radiomic approach to identify the HPV status of OPSCC.
methodsFour independent cohorts, totaling 778 OPSCC patients with HPV determined by p16 were collected. We randomly assigned 80% of all data for model training (N = 628) and 20% for validation (N = 150). On the pre-treatment CT images, 902 radiomic features were calculated from the gross tumor volume. Multivariable modeling was performed using least absolute shrinkage and selection operator. To assess the impact of CT artifacts in predicting HPV (p16), a model was developed on all training data (M
resultsThe area under the receiver operator curve for M
conclusionThis study provides proof of concept that molecular information can be derived from standard medical images and shows potential for radiomics as imaging biomarker of HPV status. Advances in knowledge: Radiomics has the potential to identify clinically relevant molecular phenotypes.
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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.