ArticleJCO clinical cancer informatics2019
Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive.
Article in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Multimodal Radiogenomic Imaging in Oropharyngeal Squamous Cell Carcinoma: Implications for Dentomaxillofacial Radiology.Medical sciences (Basel, Switzerland) · 2026Review
- Habitat Radiomics Predict HPV Status in Oropharyngeal Cancer.Cancer medicine · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- 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
- Article
- Investigating the Feasibility of Predicting KRAS Status, Tumor Staging, and Extramural Venous Invasion in Colorectal Cancer Using Inter-Platform Magnetic Resonance Imaging Radiomic Features.Diagnostics (Basel, Switzerland) · 2023Article
- A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.Cancers · 2023Article
- Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.Cancer research communications · 2023Article
- Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis.Journal of orthopaedic surgery and research · 2023Article
- Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- Bayesian feature selection for radiomics using reliability metrics.Frontiers in genetics · 2023Article
- Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma.Frontiers in oncology · 2022Article
- Article
- Review
- Radiomic biomarkers of tumor immune biology and immunotherapy response.Clinical and translational radiation oncology · 2021Review
- Are Artificial Intelligence Challenges Becoming Radiology's New "Bee's Knees"?Radiology. Artificial intelligence · 2021Article
- Differentiating low and high grade mucoepidermoid carcinoma of the salivary glands using CT radiomics.Gland surgery · 2021Article
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19 authors.
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Abstract
purposeRecent data suggest that imaging radiomic features of a tumor could be indicative of important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. We performed a comprehensive study to discover the imaginggenomic associations in head and neck squamous cell carcinoma (HNSCC) and explore the potential of predicting tumor genomic alternations using radiomic features.
methodsOur retrospective study integrated whole-genome multiomics data from The Cancer Genome Atlas with matched computed tomography imaging data from The Cancer Imaging Archive for the same set of 126 patients with HNSCC. Linear regression and gene set enrichment analysis were used to identify statistically significant associations between radiomic imaging and genomic features. Random forest classifier was used to predict the status of two key HNSCC molecular biomarkers, human papillomavirus and disruptive TP53 mutation, on the basis of radiomic features.
resultsWidespread and statistically significant associations were discovered between genomic features (including microRNA expression, somatic mutations, and transcriptional activity, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of human papillomavirus and TP53 mutation status using radiomic features achieved areas under the receiver operating characteristic curve of 0.71 and 0.641, respectively.
conclusionOur exploratory study suggests that radiomic features are associated with genomic characteristics at multiple molecular layers in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.
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