Zhuang Mingzan, Li Xianru, Qiu Zhifen, Guan Jitian
Department of Nuclear Medicine, Meizhou People's Hospital, Meizhou, China.
Guangdong Engineering Technological Research Center of Clinical Molecular Diagnosis and Antibody Drugs, Meizhou People's Hospital, Meizhou, China.
EJNMMI Phys. 2024 Jun 6;11(1):48. doi: 10.1186/s40658-024-00652-0.
The purpose of our study is to validate the robustness and accuracy of consensus contour in 2-deoxy-2-[ F]fluoro-D-glucose ( F-FDG) PET radiomic features.
225 nasopharyngeal carcinoma (NPC) and 13 extended cardio-torso (XCAT) simulated data were enrolled. All segmentation were performed with four segmentation methods under two different initial masks, respectively. Consensus contour (ConSeg) was then developed using the majority vote rule. 107 radiomic features were extracted by Pyradiomics based on segmentation and the intraclass correlation coefficient (ICC) was calculated for each feature between masks or among segmentation, respectively. In XCAT ICC between segmentation and simulated ground truth were also calculated to access the accuracy.
ICC varied with the dataset, segmentation method, initial mask and feature type. ConSeg presented higher ICC for radiomic features in robustness tests and similar ICC in accuracy tests, compared with the average of four segmentation results. Higher ICC were also generally observed in irregular initial masks compared with rectangular masks in both robustness and accuracy tests. Furthermore, 19 features (17.76%) had ICC ≥ 0.75 in both robustness and accuracy tests for any of the segmentation methods or initial masks. The dataset was observed to have a large impact on the correlation relationships between radiomic features, but not the segmentation method or initial mask.
The consensus contour combined with irregular initial mask could improve the robustness and accuracy in radiomic analysis to some extent. The correlation relationships between radiomic features and feature clusters largely depended on the dataset, but not segmentation method or initial mask.
本研究旨在验证2-脱氧-2-[¹⁸F]氟-D-葡萄糖（¹⁸F-FDG）PET影像组学特征中一致性轮廓的稳健性和准确性。
纳入225例鼻咽癌（NPC）和13例扩展型心脏躯干（XCAT）模拟数据。所有分割分别在两种不同的初始掩码下使用四种分割方法进行。然后使用多数投票规则生成一致性轮廓（ConSeg）。基于分割，通过Pyradiomics提取107个影像组学特征，并分别计算每个特征在掩码之间或分割之间的组内相关系数（ICC）。在XCAT中，还计算了分割与模拟真实值之间的ICC以评估准确性。
ICC随数据集、分割方法、初始掩码和特征类型而变化。与四种分割结果的平均值相比，ConSeg在稳健性测试中呈现出更高的影像组学特征ICC，在准确性测试中呈现出相似的ICC。在稳健性和准确性测试中，与矩形掩码相比，在不规则初始掩码中通常也观察到更高的ICC。此外，对于任何分割方法或初始掩码，在稳健性和准确性测试中均有19个特征（17.76%）的ICC≥0.75。观察到数据集对影像组学特征之间的相关关系有很大影响，但对分割方法或初始掩码没有影响。
一致性轮廓结合不规则初始掩码在一定程度上可以提高影像组学分析的稳健性和准确性。影像组学特征与特征簇之间的相关关系在很大程度上取决于数据集，而不是分割方法或初始掩码。
