Larue Ruben T H M, Van De Voorde Lien, van Timmeren Janna E, Leijenaar Ralph T H, Berbée Maaike, Sosef Meindert N, Schreurs Wendy M J, van Elmpt Wouter, Lambin Philippe
Department of Radiation Oncology (MAASTRO, the D-Lab), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Department of Radiation Oncology (MAASTRO, the D-Lab), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Radiother Oncol. 2017 Oct;125(1):147-153. doi: 10.1016/j.radonc.2017.07.023. Epub 2017 Aug 7.
Quantitative tissue characteristics derived from medical images, also called radiomics, contain valuable prognostic information in several tumour-sites. The large number of features available increases the risk of overfitting. Typically test-retest CT-scans are used to reduce dimensionality and select robust features. However, these scans are not always available. We propose to use different phases of respiratory-correlated 4D CT-scans (4DCT) as alternative.
In test-retest CT-scans of 26 non-small cell lung cancer (NSCLC) patients and 4DCT-scans (8 breathing phases) of 20 NSCLC and 20 oesophageal cancer patients, 1045 radiomics features of the primary tumours were calculated. A concordance correlation coefficient (CCC) >0.85 was used to identify robust features. Correlation with prognostic value was tested using univariate cox regression in 120 oesophageal cancer patients.
Features based on unfiltered images demonstrated greater robustness than wavelet-filtered features. In total 63/74 (85%) unfiltered features and 268/299 (90%) wavelet features stable in the 4D-lung dataset were also stable in the test-retest dataset. In oesophageal cancer 397/1045 (38%) features were robust, of which 108 features were significantly associated with overall-survival.
4DCT-scans can be used as alternative to eliminate unstable radiomics features as first step in a feature selection procedure. Feature robustness is tumour-site specific and independent of prognostic value.
源自医学图像的定量组织特征，也称为放射组学，在多个肿瘤部位包含有价值的预后信息。大量可用特征增加了过拟合风险。通常使用重复测试CT扫描来降低维度并选择稳健特征。然而，这些扫描并非总是可用。我们建议使用呼吸相关4D CT扫描（4DCT）的不同阶段作为替代。
在26例非小细胞肺癌（NSCLC）患者的重复测试CT扫描以及20例NSCLC和20例食管癌患者的4DCT扫描（8个呼吸阶段）中，计算了原发肿瘤的1045个放射组学特征。使用一致性相关系数（CCC）>0.85来识别稳健特征。在120例食管癌患者中使用单变量cox回归测试与预后价值的相关性。
基于未滤波图像的特征显示出比小波滤波特征更强的稳健性。在4D肺数据集中稳定的总共63/74（85%）个未滤波特征和268/299（90%）个小波特征在重复测试数据集中也稳定。在食管癌中，397/1045（38%）个特征是稳健的，其中108个特征与总生存期显著相关。
4DCT扫描可作为替代方法，作为特征选择程序的第一步来消除不稳定的放射组学特征。特征稳健性是肿瘤部位特异性的，且与预后价值无关。
