ArticleStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2019
Radiogenomics in head and neck cancer: correlation of radiomic heterogeneity and somatic mutations in TP53, FAT1 and KMT2D.
Article in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 38 citations in OpenAlex.
- Radiomics as a spatial context for treatment decision-making in head and neck cancer.British journal of cancer · 2026Review
- Review
- Stratifying tumor immune microenvironment in head and neck squamous cell carcinoma: from parsing out immune-subtype prognosis to clinically applicable CT-radiomics models.Journal of translational medicine · 2026Article
- Identification of IGF2BP2 and long non-coding RNA TUG1 for the prognosis and tumour microenvironment in head and neck squamous cell carcinoma.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2025Article
- Recent advances in the role of atypical cadherin FAT1 in tumorigenesis (Review).Oncology letters · 2025Review
- 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 analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging.Scientific reports · 2024Article
- A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.Cancers · 2023Article
- Review
- Circadian rhythm-related genes index: A predictor for HNSCC prognosis, immunotherapy efficacy, and chemosensitivity.Frontiers in immunology · 2023Article
- Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC.Scientific reports · 2022Article
- Radiomics from Various Tumour Volume Sizes for Prognosis Prediction of Head and Neck Squamous Cell Carcinoma: A Voted Ensemble Machine Learning Approach.Life (Basel, Switzerland) · 2022Article
- Dynamics of HMBG1 (High Mobility Group Box 1) during radiochemotherapy correlate with outcome of HNSCC patients.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2022Article
- Radiomic and gEnomic approaches for the enhanced DIagnosis of clear cell REnal Cancer (REDIRECt): a translational pilot methodological study.Translational andrology and urology · 2022Article
- AI in spotting high-risk characteristics of medical imaging and molecular pathology.Precision clinical medicine · 2021Review
- Differentiating low and high grade mucoepidermoid carcinoma of the salivary glands using CT radiomics.Gland surgery · 2021Article
- A computed tomography (CT)-derived radiomics approach for predicting primary co-mutations involvingAnnals of translational medicine · 2021Article
- Radiomic Model Predicts Lymph Node Response to Induction Chemotherapy in Locally Advanced Head and Neck Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Application of radiomics and machine learning in head and neck cancers.International journal of biological sciences · 2021Review
- Radiomics in Oncology: A 10-Year Bibliometric Analysis.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeGenetic tumour profiles and radiomic features can be used to complement clinical information in head and neck squamous cell carcinoma (HNSCC) patients. Radiogenomics imply the potential to investigate complementarity or interrelations of radiomic and genomic features, and prognostic factors might be determined. The aim of our study was to explore radiogenomics in HNSCC.
methodsFor 20 HNSCC patients treated with primary radiochemotherapy, next-generation sequencing (NGS) of tumour and corresponding normal tissue was performed. In total, 327 genes were investigated by panel sequencing. Radiomic features were extracted from computed tomography data. A hypothesis-driven approach was used for radiogenomic correlations of selected image-based heterogeneity features and well-known driver gene mutations in HNSCC.
resultsThe most frequently mutated driver genes in our cohort were TP53 (involved in cell cycle control), FAT1 (Wnt signalling, cell-cell contacts, migration) and KMT2D (chromatin modification). Radiomic features of heterogeneity did not correlate significantly with somatic mutations in TP53 or KMT2D. However, somatic mutations in FAT1 and smaller primary tumour volumes were associated with reduced radiomic intra-tumour heterogeneity.
conclusionThe landscape of somatic variants in our cohort is well in line with previous reports. An association of somatic mutations in FAT1 with reduced radiomic tumour heterogeneity could potentially elucidate the previously described favourable outcomes of these patients. Larger studies are needed to validate this exploratory data in the future.
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