Room: Exhibit Hall | Forum 6
Purpose: Esophageal cancer patients undergoing radiotherapy usually receive daily or weekly cone-beam CT (CBCT) imaging to verify positioning before treatment. The purpose of this study is to evaluate the reproducibility of texture features extracted from CBCT and its correlation with CT features for their potential use as early predictors of esophageal cancer response during the course of RT.
Methods: Ten patients treated for esophageal cancer that received daily CBCT were retrospectively evaluated (Varian TrueBeam with same Thorax imaging technique: half-fan, full-trajectory, 125 kVp, 270 mAs). The planning CT (CTP) and the two initial CBCTs (day 1 and 2 of treatment) were exported from Eclipse TPS to an in-house processing pipeline. This included edge detection for couch removal, CBCT resampling and automatic 3D rigid-registration of CBCT to CTP using Mattes mutual information metric. GTVs for each patient were exported and texture features were extracted from CTP and the registered CBCTs using the Imaging Biomarker Explorer (IBEX) software. Thirty-three texture features using co-occurrence and run-length matrices were extracted. Texture reproducibility between consecutive CBCTs was evaluated using intraclass correlation (ICC). CTP and CBCTs were evaluated using Pearson's correlation. Significance level was corrected for multiple testing using Bonferroni adjustment.
Results: Registration results were deemed satisfactory using mutual information as well as visual inspection within the volume that encompassed the GTV. Out of the initial 33 texture features considered, 13 presented excellent (ICC>0.9) reproducibility between the two initial CBCTs. Out of these 13 features, 5 presented statistically significant Pearson's correlation adjusted for multiple statistical tests (p<0.004) ranging from 0.86 to 0.89 (Table).
Conclusion: Several texture features from CBCT showed reproducibility between the first two days of treatment and strongly correlated between CTP and CBCT. Therefore, derived texture features could be investigated as predictors of treatment response during the course of treatment.