Validation of a Method to Compensate Multicenter Effects Affecting CT Radiomics
Validation of a Method to Compensate Multicenter Effects Affecting CT Radiomics
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DOI：
10.1148/radiol.2019182023
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发表时间：
2019-04-01
期刊：
影响因子：
19.7
通讯作者：
Buvat, Irene
中科院分区：
文献类型：
--
作者：
Orlhac, Fanny;Frouin, Frederique;Buvat, Irene
背景：放射组学从医学图像中提取特征比视觉评估更精确和准确。目的：探讨一种补偿方法是否能纠正因使用不同的CT扫描方案而引起的放射学特征值的变化。材料和方法：回顾分析1组(男性19例；患者42例；平均年龄60.4岁；2013年9月至10月)和2例(男性16例；患者32例；平均年龄62.1岁；2007年1月至9月)使用不同CT扫描方法扫描的10种纹理类型的体模数据。对于任何放射学特征，补偿方法确定了特定于协议的转换，以表达在公共空间中没有协议影响的所有数据。采用Friedman检验评估补偿前后不同方案间统计分布的差异。对体模数据进行主成分分析，评价补偿后对纹理模式的区分能力。结果：在体模数据中，对于所有放射学特征和纹理模式，不同方案的特征的统计分布不同(P<0.05)。主成分分析表明，与补偿前观察到的不同，每种纹理模式不再显示为对应于不同成像协议的不同簇。补偿前P值100%(队列1的10个特征中的10个特征)和98%(队列2的特征中的87个)的P值小于0.05，而补偿后的P值分别为30%(3个)和15%(的13个)。结论：图像补偿成功地调整了不同CT成像方案计算的特征分布，为多中心放射组学研究奠定了基础。(C)RSNA，2019年
Background: Radiomics extracts features from medical images more precisely and more accurately than visual assessment. However, radiomics features are affected by CT scanner parameters such as reconstruction kernel or section thickness, thus obscuring underlying biologically important texture features.Purpose: To investigate whether a compensation method could correct for the variations of radiomic feature values caused by using different CT protocols.Materials and Methods: Phantom data involving 10 texture patterns and 74 patients in cohorts 1 (19 men; 42 patients; mean age, 60.4 years; September-October 2013) and 2 (16 men; 32 patients; mean age, 62.1 years; January-September 2007) scanned by using different CT protocols were retrospectively included. For any radiomic feature, the compensation approach identified a protocol-specific transformation to express all data in a common space that were devoid of protocol effects. The differences in statistical distributions between protocols were assessed by using Friedman tests before and after compensation. Principal component analyses were performed on the phantom data to evaluate the ability to distinguish between texture patterns after compensation.Results: In the phantom data, the statistical distributions of features were different between protocols for all radiomic features and texture patterns (P.05). Principal component analysis demonstrated that each texture pattern was no longer displayed as different clusters corresponding to different imaging protocols, unlike what was observed before compensation. The correction for scanner effect was confirmed in patient data with 100% (10 of 10 features for cohort 1) and 98% (87 of 89 features for cohort 2) of P values less than .05 before compensation, compared with 30% (three of 10) and 15% (13 of 89) after compensation.Conclusion: Image compensation successfully realigned feature distributions computed from different CT imaging protocols and should facilitate multicenter radiomic studies. (c) RSNA, 2019