Establishing Relevant ADC-based Texture Analysis Metrics for Quantifying Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinomas

Purpose: The purpose of this study is to identify which texture analysis metrics calculated from apparent diffusion coefficient (ADC) maps from patients with head and neck squamous cell carcinomas (HNSCC) provide quantifiable measures of tumor physiology changes. We discerned which imaging metrics were relevant using baseline agreement and variations during early treatment. Methods: For selective patients with stages II-IV HNSCC, ADC maps were generated from two baselines, taken 1 week apart, and one early treatment scan, obtained during the 2nd week of curative-intent chemoradiation therapy. Regions of interest (ROI), consisting of primary and nodal disease were drawn onto resampled ADC maps. Four 3D texture matrices describing local and regional relationships between voxel intensities in the ROIs were generated. From these, 38 texture metrics and 7 histogram features were calculated for each patient, including the mean and median ADC. Agreement between the two baseline measures was estimated with the intra-class correlation coefficient (ICC). For each metric with an ICC≥0.80, the Wilcoxon signed-rank test was used to test if the difference between the mean of the baselines and the early treatment was non-zero. Results: Texture analysis was implemented on nine patients that had both baselines and early treatment images. Due to baseline agreement, only 9 of the 45 metrics had an ICC ≥0.80, including ADC mean and median. Six of these 9 metrics had a p-value \u3c 0.05. Only 1 of the 9 metrics remained of interest, after applying the Holm correction to the alpha levels: the run length non-uniformity metric (p = 0.004) in the Gray Level Run Length Matrix. Conclusion: The feasibility of texture analysis is dependent on the baseline agreement of each metric, which disqualifies many texture characteristics. However, metrics with high ICC have potential to provide additional quantitative information for the assessment of early treatment changes for HNSCC