Colin Y Wang1, Joseph J Foy2, Tanguy Y Siewert3, Daniel J Haraf4, Daniel T Ginat2. 1. From the Pritzker School of Medicine. 2. Department of Radiology. 3. Section of Hematology-Oncology, Department of Medicine. 4. Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL.
Abstract
PURPOSE: To determine the relationship between computed tomography (CT) radiomic features and gene expression levels in head and neck squamous cell carcinoma (HNSCC). METHODS: This retrospective study included 66 patients with HNSCC primary lesions (36 oropharyngeal, 6 hypopharyngeal, 10 laryngeal, 14 oral cavity). Gene expression information for 6 targetable genes (fibroblast growth factor receptor [FGFR]1, epidermal growth factor receptor [EGFR], FGFR2, FGFR3, EPHA2, PIK3CA) was obtained via Agilent microarrays from samples collected between 1997 and 2010. Pretreatment contrast-enhanced soft tissue neck CT scans were reviewed, and 142 radiomics features were derived. R was used to calculate Pearson correlation coefficients were calculated between gene expression levels and each radiomic feature. P values were adjusted using the false discovery rate (FDR) method. RESULTS: There were significant correlations between FGFR1 and 5 gray level cooccurrence matrix (GLCM) features with FDR-adjusted P values less than 0.05: inertia (r = 0.366, FDR-adjusted P = 0.006), absolute value (r = 0.31, FDR-adjusted P = 0.024), contrast (r = 0.366, FDR-adjusted P = 0.006), difference average (r = 0.31, FDR-adjusted P = 0.024), and difference variance (r = 0.37, FDR-adjusted P = 0.005). There was 1 correlated feature for FGFR2 with an FDR-adjusted P value less than 0.05: fractal dimension box-coarse (r = 0.33, FDR-adjusted P = 0.018). There was 1 correlated feature for EPHA2 with an FDR-adjusted P value less than 0.05: GLCM entropy (r = -0.28, FDR-adjusted P = 0.049). Six of the 7 features that showed significant correlation belonged to the GLCM class of features. CONCLUSIONS: The CT radiomic features demonstrate correlations with FGFR1 status in HNSCC and should be further investigated for their potential to predict FGFR1 status.
PURPOSE: To determine the relationship between computed tomography (CT) radiomic features and gene expression levels in head and neck squamous cell carcinoma (HNSCC). METHODS: This retrospective study included 66 patients with HNSCC primary lesions (36 oropharyngeal, 6 hypopharyngeal, 10 laryngeal, 14 oral cavity). Gene expression information for 6 targetable genes (fibroblast growth factor receptor [FGFR]1, epidermal growth factor receptor [EGFR], FGFR2, FGFR3, EPHA2, PIK3CA) was obtained via Agilent microarrays from samples collected between 1997 and 2010. Pretreatment contrast-enhanced soft tissue neck CT scans were reviewed, and 142 radiomics features were derived. R was used to calculate Pearson correlation coefficients were calculated between gene expression levels and each radiomic feature. P values were adjusted using the false discovery rate (FDR) method. RESULTS: There were significant correlations between FGFR1 and 5 gray level cooccurrence matrix (GLCM) features with FDR-adjusted P values less than 0.05: inertia (r = 0.366, FDR-adjusted P = 0.006), absolute value (r = 0.31, FDR-adjusted P = 0.024), contrast (r = 0.366, FDR-adjusted P = 0.006), difference average (r = 0.31, FDR-adjusted P = 0.024), and difference variance (r = 0.37, FDR-adjusted P = 0.005). There was 1 correlated feature for FGFR2 with an FDR-adjusted P value less than 0.05: fractal dimension box-coarse (r = 0.33, FDR-adjusted P = 0.018). There was 1 correlated feature for EPHA2 with an FDR-adjusted P value less than 0.05: GLCM entropy (r = -0.28, FDR-adjusted P = 0.049). Six of the 7 features that showed significant correlation belonged to the GLCM class of features. CONCLUSIONS: The CT radiomic features demonstrate correlations with FGFR1 status in HNSCC and should be further investigated for their potential to predict FGFR1 status.