ArticleOral oncology2020
Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma.
Article in Oral oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06854107 (Intratumoral and Peritumoral Habitat Radiomics of MRI Predict Pathologic Complete Response to Neoadjuvant Chemoimmunotherapy in Oral Squamous Cell Carcinoma), which is not on this map. Cited by 26 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Intratumoral and Peritumoral Habitat Radiomics of MRI Predict Pathologic Complete Response to Neoadjuvant Chemoimmunotherapy in Oral Squamous Cell Carcinoma
Who cites it
26 citing papers in PubMed.
- Radiomics-based machine learning models decipher heterogeneity of anoikis-related molecular subtypes for prognostic stratification in head and neck squamous cell carcinoma.Translational cancer research · 2026Article
- Radiomics as a spatial context for treatment decision-making in head and neck cancer.British journal of cancer · 2026Review
- Preoperative circulating tumor cells integrated with imaging analysis for prognostic evaluation in head and neck squamous cell carcinoma.Molecular oncology · 2026Article
- Review
- Multimodal Radiogenomic Imaging in Oropharyngeal Squamous Cell Carcinoma: Implications for Dentomaxillofacial Radiology.Medical sciences (Basel, Switzerland) · 2026Review
- Cancer classification with radiomics in controlled preclinical models.Scientific reports · 2026Article
- Beyond Standard Radiotherapy: an AI-Driven Framework for Personalized Radiotherapy with a Four-Step Classification in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients.Current oncology reports · 2025Review
- Integrated Hyperparameter Optimization with Dimensionality Reduction and Clustering for Radiomics: A Bootstrapped Approach.Multimodal technologies and interaction · 2025Article
- Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma.Scientific reports · 2025Article
- Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction.Journal for immunotherapy of cancer · 2025Article
- Radiomic clustering using graph network techniques coupled with unbalanced optimal transport.Computational and structural biotechnology journal · 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
- Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology.BMC cancer · 2024Article
- Wasserstein HOG: Local Directionality Extraction via Optimal Transport.IEEE transactions on medical imaging · 2024Article
- A Potential Radiomics-Clinical Model for Predicting Failure of Lymph Node Control after Definite Radiotherapy in Locally Advanced Head and Neck Cancer.Medicina (Kaunas, Lithuania) · 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
- A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.Cancers · 2023Article
- CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.Journal of ovarian research · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
purposeTo identify whether radiomic features from pre-treatment computed tomography (CT) scans can predict molecular differences between head and neck squamous cell carcinoma (HNSCC) using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA).
methods77 patients from the TCIA with HNSCC had imaging suitable for analysis. Radiomic features were extracted and unsupervised consensus clustering was performed to identify subtypes. Genomic data was extracted from the matched patients in the TCGA database. We explored relationships between radiomic features and molecular profiles of tumors, including the tumor immune microenvironment. A machine learning method was used to build a model predictive of CD8 + T-cells. An independent cohort of 83 HNSCC patients was used to validate the radiomic clusters.
resultsWe initially extracted 104 two-dimensional radiomic features, and after feature stability tests and removal of volume dependent features, reduced this to 67 features for subsequent analysis. Consensus clustering based on these features resulted in two distinct clusters. The radiomic clusters differed by primary tumor subsite (p = 0.0096), HPV status (p = 0.0127), methylation-based clustering results (p = 0.0025), and tumor immune microenvironment. A random forest model using radiomic features predicted CD8 + T-cells independent of HPV status with R
conclusionRadiomic analysis can identify biologic features of tumors such as HPV status and T-cell infiltration and may be able to provide other information in the near future to help with patient stratification.
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