CT Radiomic Features Are Associated with DNA Copy Number Alterations of Head and Neck Squamous Cell Carcinomas

While a larger fraction of head and neck squamous cell carcinoma (HNSCC) genomes is characterized by a high prevalence of copy number alterations (CNA-positive), a smaller subset with more favorable oncologic outcome is instead driven by somatic mutations (CNA-negative). We aimed to investigate the radiomic phenotypes of CNA-positive and -negative HNSCCs in contrast CT images.

Single nucleotide polymorphism-array copy number data were utilized and CNA-based hierarchical clustering of patients was performed to define CNA subclasses. Radiomic features (n=1037) quantifying shape, first-order intensity, and texture were extracted from HNSCC primary tumors in pretherapeutic neck CTs. We performed univariate association analyses and trained, optimized, and validated radiomics-based CNA prediction models by combining feature selection algorithms with machine learning classifiers.

A total of 522 and 114 patients were included in the copy number and radiomic analyses, respectively. Univariate analysis revealed 190 features from all feature subtypes (shape, first-order, texture) were significantly associated with the CNA status; after multiple testing correction, 29 texture or first-order features remained significant. The best-performing CNA status prediction model utilized a support vector machine classifier, achieving an AUC of 0.71 (95% CI, 0.60-0.83).

CNA subgroups exhibit distinct radiomic phenotypes, primarily reflected in texture and intensity characteristics. These findings enhance our understanding of the biologic significance of radiomic information in HNSCC. In the clinical setting, as CNA-positive and -negative HNSCCs may emerge as distinct subclasses with unique staging schemes and treatment implications, improved CT radiomics-based prediction models could offer a noninvasive, cost-effective method for CNA subtyping.