Advances in Radiomics and Bioinformatics Integration Research for Head and Neck Malignancies

Head and neck malignancies represent a heterogeneous group of cancers with significant morbidity and mortality. Recent advances in radiomics and bioinformatics have opened new avenues for improving diagnosis, prognosis, and personalized treatment strategies. Radiomics extracts high-dimensional quantitative features from medical imaging, while bioinformatics deciphers complex molecular data, enabling a deeper understanding of tumor biology. The integration of these fields holds promise for uncovering novel biomarkers, enhancing predictive models, and guiding precision medicine.This Research Topic invites original research and reviews exploring the synergy between radiomics and bioinformatics in head and neck cancer. We encourage submissions addressing computational tools, multi-omics data fusion, AI-driven analysis, and clinical translation. Contributions may focus on imaging-genomics correlations, prognostic modeling, or therapeutic response prediction. By bridging imaging and molecular science, this collection aims to foster interdisciplinary collaboration and accelerate innovations in cancer research.The purpose of this Research Topic is to highlight cutting-edge developments in radiomics and bioinformatics for head and neck malignancies, addressing challenges in early detection, tumor heterogeneity, and treatment resistance. We seek to consolidate knowledge on integrating imaging and molecular data to improve clinical decision-making, ultimately advancing precision oncology.● 1.Multimodal Imaging-Genomics Correlation and Therapeutic Target DiscoveryFocus: Integrate CT/MRI/PET radiomic features with genomic/transcriptomic data (e.g., TCGA-HNSC) to identify associations between imaging phenotypes and specific genetic alterations (e.g., TP53, PIK3CA mutations) or immune microenvironment features (e.g., PD-L1 expression, T-cell infiltration).Methods: Use deep learning (e.g., CNNs) to extract tumor heterogeneity features, combined with pathway enrichment analysis (e.g., GSEA) to uncover novel therapeutic targets for personalized treatment (e.g., targeted therapy or immunotherapy).● 2. AI-Driven Predictive Models for Treatment ResponseFocus: Develop combined radiomic (e.g., texture features, peritumoral edema) and liquid biopsy (e.g., ctDNA) models to predict early response to chemoradiation or immunotherapy (evaluated by RECIST criteria).Methods: Apply time-series analysis (e.g., LSTM) to track imaging and molecular changes during treatment, with SHAP values identifying key predictors (e.g., metabolic tumor volume dynamics and EGFR copy number variations).● 3. Quantifying Tumor Heterogeneity for Risk StratificationFocus: Leverage radiomics (e.g., intratumoral/intertumoral heterogeneity indices) and single-cell sequencing to classify high-risk subgroups (e.g., EMT phenotype or radiation-resistant clones) for tailored therapy.Methods: Unsupervised clustering (e.g., K-means) for patient stratification, validated by Cox regression for survival impact, guiding treatment intensification (e.g., adjuvant therapy) or de-escalation strategies.● 4. Cross-Omics Validation of Radiosensitivity BiomarkersFocus: Correlate radiomic features (e.g., radiation dose distribution and texture patterns) with DNA damage repair gene expression (e.g., ATM, RAD51) to predict local control and radiation toxicity (e.g., mucositis).Methods: Multi-task learning frameworks to jointly optimize efficacy and toxicity prediction, validated in prospective cohorts (e.g., NRG Oncology trial data).● 5. Dynamic Treatment Monitoring and Adaptive Therapy OptimizationFocus: Serial imaging (e.g., DWI-MRI) and circulating tumor cell (CTC) analysis during treatment to refine prognostic models in real time, enabling adaptive interventions (e.g., switching targeted agents or boosting radiation).Methods: Federated learning for multi-institutional data integration; reinforcement learning to simulate optimal treatment pathways.Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Qiheng Gou, Yuxin Xie, Peter Huber Source: https://www.frontiersin.org/research-topics/72213/advances-in-radiomics-and-bioinformatics-integration-research-for-head-and-neck-malignancies