ArticleEBioMedicine2019
Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed, 92 citations in OpenAlex.
- Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer.Journal of translational medicine · 2021Trial
- Biomarkers for oesophageal squamous cell carcinoma and the role of HPV: Multi‑omics approaches and current evidence (Review).International journal of oncology · 2026Review
- Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Radiomic-Based Machine Learning Classifiers for HPV Status Prediction in Oropharyngeal Cancer: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Predicting tumor micronecrosis in hepatocellular carcinoma using pretreatment contrast-enhanced CT radiomics model: a multi-center study.NPJ precision oncology · 2025Article
- Epidemiology and Prognostic Role of HPV Infection in Head and Neck Cancer: A Population-Based Study of the SEER Database.Cancer medicine · 2025Article
- Review
- Spatial Radiomic Graphs for Outcome Prediction in Radiation Therapy-treated Head and Neck Squamous Cell Carcinoma Using Pretreatment CT.Radiology. Imaging cancer · 2025Article
- Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma.Frontiers in medicine · 2025Article
- Investigation of Machine and Deep Learning Techniques to Detect HPV Status.Journal of personalized medicine · 2024Article
- Radiomics Features in Predicting Human Papillomavirus Status in Oropharyngeal Squamous Cell Carcinoma: A Systematic Review, Quality Appraisal, and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- Prediction by a multiparametric magnetic resonance imaging-based radiomics signature model of disease-free survival in patients with rectal cancer treated by surgery.Frontiers in oncology · 2024Article
- Genetic Mutations Associated with Inflammatory Response Caused by HPV Integration in Oropharyngeal Squamous Cell Carcinoma.Biomedicines · 2023Article
- A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.Cancers · 2023Article
- Pyroptosis-related gene signatures are associated with prognosis and tumor microenvironment infiltration in head and neck cancer.Health science reports · 2023Article
- Machine learning with multimodal data for COVID-19.Heliyon · 2023Review
- CT radiomic signature predicts survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma.NPJ precision oncology · 2023Article
- Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.Cancer research communications · 2023Article
- Review
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundRadiomics-based non-invasive biomarkers are promising to facilitate the translation of therapeutically related molecular subtypes for treatment allocation of patients with head and neck squamous cell carcinoma (HNSCC).
methodsWe included 113 HNSCC patients from The Cancer Genome Atlas (TCGA-HNSCC) project. Molecular phenotypes analyzed were RNA-defined HPV status, five DNA methylation subtypes, four gene expression subtypes and five somatic gene mutations. A total of 540 quantitative image features were extracted from pre-treatment CT scans. Features were selected and used in a regularized logistic regression model to build binary classifiers for each molecular subtype. Models were evaluated using the average area under the Receiver Operator Characteristic curve (AUC) of a stratified 10-fold cross-validation procedure repeated 10 times. Next, an HPV model was trained with the TCGA-HNSCC, and tested on a Stanford cohort (N = 53).
findingsOur results show that quantitative image features are capable of distinguishing several molecular phenotypes. We obtained significant predictive performance for RNA-defined HPV+ (AUC = 0.73), DNA methylation subtypes MethylMix HPV+ (AUC = 0.79), non-CIMP-atypical (AUC = 0.77) and Stem-like-Smoking (AUC = 0.71), and mutation of NSD1 (AUC = 0.73). We externally validated the HPV prediction model (AUC = 0.76) on the Stanford cohort. When compared to clinical models, radiomic models were superior to subtypes such as NOTCH1 mutation and DNA methylation subtype non-CIMP-atypical while were inferior for DNA methylation subtype CIMP-atypical and NSD1 mutation.
interpretationOur study demonstrates that radiomics can potentially serve as a non-invasive tool to identify treatment-relevant subtypes of HNSCC, opening up the possibility for patient stratification, treatment allocation and inclusion in clinical trials. FUND: Dr. Gevaert reports grants from National Institute of Dental & Craniofacial Research (NIDCR) U01 DE025188, grants from National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIBIB), R01 EB020527, grants from National Cancer Institute (NCI), U01 CA217851, during the conduct of the study; Dr. Huang and Dr. Zhu report grants from China Scholarship Council (Grant NO:201606320087), grants from China Medical Board Collaborating Program (Grant NO:15-216), the Cyrus Tang Foundation, and the Zhejiang University Education Foundation during the conduct of the study; Dr. Cintra reports grants from São Paulo State Foundation for Teaching and Research (FAPESP), during the conduct of the study.
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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 Socratic graph.