Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
Vital Images, Minnetonka, USA.
Sci Rep. 2019 Apr 12;9(1):6009. doi: 10.1038/s41598-019-42340-5.
109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen's Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.
109 个经病理证实的亚实性结节（SSN）由 2 位读者在非薄层胸部 CT 上使用肺结节分析软件进行分割，然后提取 CT 衰减直方图和几何特征。直方图的功能数据分析提供了数据驱动的特征（FPC1、2、3），用于进一步的模型构建。结节被分为侵袭前（P1，非典型性腺瘤性增生和原位腺癌）、微侵袭（P2）和浸润性腺癌（P3）。P1 和 P2 一起分为一组（T1），与 P3 （T2）相对。使用多元逻辑回归和非线性分类器，比较了各种特征组合在用于二元结节分类（T1/T2）的预测模型中的性能。ROC 曲线下面积（AUC）被用作诊断性能标准。使用 Cohen 的 Kappa 和组内系数（ICC）评估读者间的可变性。根据 AUC 选择了三个预测 SSN 侵袭性的模型。第一个模型包括 CT 病变衰减的 87.5 百分位数（Q.875）、四分位距（IQR）、体积和最大/最小直径比（AUC：0.89，95%CI：[0.75 1]）。第二个模型包括 FPC1、体积和直径比（AUC：0.91，95%CI：[0.77 1]）。第三个模型包括 FPC1、FPC2 和体积（AUC：0.89，95%CI：[0.73 1]）。读者间的可变性很好（Kappa：0.95，ICC：0.98）。使用直方图和几何特征的简约模型可以区分侵袭性与微侵袭/侵袭前 SSN，在非薄层 CT 上具有良好的预测性能。
