Radiomic feature variability on cone-beam CT images for lung SBRT

This study aims to (1) investigate methodology for harmonization of radiomics features between planning CT and on-board CBCT and establish a workflow to harmonize images taken from different scanning protocols and over the course of radiotherapy treatments using normalization, and (2) examine feature variability of longitudinal cone-beam CT radiomics for 3 different fractionation schemes and a time-point during treatment indicative of early treatment response.

All CBCT images acquired over the course of lung SBRT for each patient were registered with corresponding planning CT. A volume-of-interest (VOI) in a homogeneous soft-tissue region that would not change over the course of radiotherapy was selected on the planning CT. The VOI was applied to all CBCT images of the same patient taken at different days. The first CBCT was normalized to the planning CT using the ratio of their respective mean VOI pixel values. Subsequent CBCT images were normalized using the ratio of that CBCT’s mean VOI pixel value to the first CBCT’s mean VOI pixel value. Forty-three features characterizing image intensity and morphology in fine and coarse textures were extracted from the planning CT, all original CBCT images, and all normalized CBCT images. T-test on extracted features from CBCT images with and without normalization indicates the effect of normalization on the distribution of various features. Mutual information between the planning CT and the first CBCT with and without normalization was calculated to assess the effectiveness of normalization on harmonizing radiomics features.

Of 72 NSCLC patients treated with lung SBRT, 18 received 15-18 Gy / fraction for 3 fractions; 36 received 12-12.5 Gy / fraction for 4 fractions; 18 received 8-10 Gy / fraction for 5 fractions. We studied 7 sets of CBCT images from the same treatment fraction as a ‘test-retest’ baseline to study the additional daily CBCT images. Fifty-five gray level intensity and textural features were extracted from the CBCT images. Test-retest images were used to determine the smallest detectable change (C=1.96*SD) indicating significant variation with a 95% confidence level. Here, the significance of feature variation depended on the choice of a minimum number of patients for which a feature changed more than ’C’. Analysis of which features change at which moment during treatment with different fractionation schemes was used to investigate a time-point indicative of early tumor response.

T-test on planning CT and CBCT images of the 72 patients indicated that normalization with a soft tissue VOI reduced the number of features with significant variation (p<0.05) by 55%. Following lung SBRT, 30 features changed significantly for at least 10% of all patients. For patients treated with 3 fractions, 49 features changed at Fraction 2, and 49 at Fraction 3; there was 100% overlap between features at both fractions. For patients treated with 4 fractions, 45, 45, and 48 features changed at Fraction 2-4 respectively; there was 92% overlap between features at Fraction 2 and the remaining fractions. For patients treated with 5 fractions, 12, 18, 14, and 25 features changed at Fraction 2-5; there was 36%, 48%, and 48% overlap between features at Fraction 2-4 and the remaining fractions respectively.

Normalization can potentially harmonize radiomics features on both planning CT and on-board CBCT. Feature variability depends on the selection of normalization VOI and extraction VOI. Significant changes in gray level radiomic features were observed over the course of lung SBRT. Different fractionation schemes corresponded to different patterns of feature variation. Higher fractional dose corresponded to a larger number of variable features and high overlap of variable features at an earlier time-point.