Reproducibility and Prognosis of Quantitative Features Extracted from CT Images
Reproducibility and Prognosis of Quantitative Features Extracted from CT Images
复制标题
DOI：
10.1593/tlo.13844
复制
发表时间：
2014-02-01
影响因子：
5
通讯作者：
Gillies, Robert J.
中科院分区：
文献类型：
--
作者：
Balagurunathan, Yoganand;Gu, Yuhua;Gillies, Robert J.
我们研究了用于描述非小细胞肺癌(NSCLC)CT扫描中肿瘤形状、大小和质地的定量成像特征的重复性。CT图像取决于各种扫描因素。我们专注于描述在由于患者因素和分割方法的差异存在的情况下可重现的图像特征。从参考图像数据库中获得32个非小细胞肺癌平扫肺CT扫描，以评估响应数据集。使用人工(放射科专家)和集成(软件自动)方法对肿瘤进行分割。计算了一组特征(219个三维和110个二维)，并对定量图像特征进行统计过滤，以识别可重现和非冗余特征的子集。用重测一致性相关系数(CCCTreT)测量重复实验的变异性。通过动态范围(DR)测量特征中归一化为方差的自然范围。在这项研究中，在CCCTreT和DR>；=0.9和R-2Bet>；=0.95的分割方法中发现了29个特征。这些可重复的特征被用于预测放射科医生的预后评分；一些纹理特征(游程和法律核)在曲线下的面积为0.9。使用一个独立的非小细胞肺癌数据集(59例肺腺癌)，对具有代表性的特征的预后能力进行了测试，其中纹理特征之一的游程长度灰度级不均匀在将样本划分为存活组方面具有统计学意义(P
We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 three-dimensional and 110 two-dimensional) was computed, and quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR >= 0.9 and R-2 Bet >= 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups (P