  In high-quality radiotherapy delivery, precise segmentation of targets and
healthy structures is essential. This study proposes Radiomics features as a
superior measure for assessing the segmentation ability of physicians and
auto-segmentation tools, in comparison to the widely used Dice Similarity
Coefficient (DSC). The research involves selecting reproducible radiomics
features for evaluating segmentation accuracy by analyzing radiomics data from
2 CT scans of 10 lung tumors, available in the RIDER Data Library. Radiomics
features were extracted using PyRadiomics, with selection based on the
Concordance Correlation Coefficient (CCC). Subsequently, CT images from 10
patients, each segmented by different physicians or auto-segmentation tools,
were used to assess segmentation performance. The study reveals 206 radiomics
features with a CCC greater than 0.93 between the two CT images, indicating
robust reproducibility. Among these features, seven exhibit low Intraclass
Correlation Coefficients (ICC), signifying increased sensitivity to
segmentation differences. Notably, ICCs of original shape features, including
sphericity, elongation, and flatness, ranged from 0.1177 to 0.995. In contrast,
all DSC values exceeded 0.778. This research demonstrates that radiomics
features, particularly those related to shape and energy, can capture subtle
variations in tumor segmentation characteristics, unlike DSC. As a result,
Radiomics features with ICC prove superior for evaluating a physician's tumor
segmentation ability and the performance of auto-segmentation tools. The
findings suggest that these new metrics can be employed to assess novel
auto-segmentation methods and enhance the training of individuals in medical
segmentation, thus contributing to improved radiotherapy practices.
