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.

该研究使用放射组学特征作为评估医生和自动分割工具的分割能力的最优指标，与 Dice 相似系数（DSC）相比较。研究选择可再现的放射组学特征来评估分割准确性，并使用 CT 扫描数据从 10 个肺肿瘤中提取的放射组学特征进行分析。研究发现，放射组学特征能够捕捉肿瘤分割特征的细微变化，尤其与形状和能量相关的特征表现出较高的敏感度，因此可用于评估医生的分割能力和自动分割工具的性能。这些发现可用于评估新的自动分割方法，提高医学分割培训水平，从而改进放射治疗实践。