Leveraging Inter-Radiomic Feature Relationships for Enhanced Prediction of Distant Metastasis and Characterization of Heterogeneity in Head and Neck Cancer.
TL;DR
Modeling inter-radiomic feature relationships using the OmicsMap representation substantially improves CT-based prediction of DMFS and characterization of tumor heterogeneity in HN cancer, supporting precision risk stratification in clinical oncology.
Abstract
Purpose
Distant metastasis remains a major cause of treatment failure in head and neck (HN) cancer, highlighting the need for more accurate early risk stratification. This study developed and validated a deep radiomics framework to characterize tumor heterogeneity from pre-treatment CT images and improve prediction of distant metastasis-free survival (DMFS).
Materials And Methods
This multicenter study included 3,421 HN cancer patients from four cohorts across twelve institutions (RADCURE, HN1, HN-PET-CT and TCGA-HNSC). Radiomics features were extracted from primary tumors and transformed into OmicsMaps, a structured representation that spatially organizes inter-feature relationships to facilitate learning of complex prognostic patterns. A convolutional neural network was trained to derive prognostic signatures, which were integrated with key clinical variables to construct an OmicsMap-clinical fusion model for patient risk stratification. Model performance was assessed using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) in the RADCURE, HN1, and HN-PET-CT cohorts. Radiogenomic analyses using RNA-seq data were conducted in the TCGA-HNSC cohort to investigate biological characteristics associated with the imaging-defined risk groups.
Results
The OmicsMap achieved C-index values of 0.742, 0.768, and 0.671 in the RADCURE, HN1, and HN-PET-CT cohorts, outperforming conventional radiomics approach by 5.84-6.37%. Incorporating clinical variables further improved generalizability, yielding C-index of 0.864 (HN1) and 0.730 (HN-PET-CT), with time-dependent AUCs of 0.727-0.895. The fusion model consistently stratified patients into distinct high- and low-risk groups for both DMFS and overall survival across cohorts (P < 0.01). Radiogenomic analyses revealed enrichment of immune-related pathways in the low-risk group, whereas the high-risk group exhibited a more aggressive phenotype enriched for proliferation, hypoxia, and epithelial-mesenchymal transition pathways, along with a fibrosis-prone tumor microenvironment characterized by extracellular matrix remodeling.
Conclusion
Modeling inter-radiomic feature relationships using the OmicsMap representation substantially improves CT-based prediction of DMFS and characterization of tumor heterogeneity in HN cancer, supporting precision risk stratification in clinical oncology.