Abstract
Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can be used to analyse new images, whereas models with non-robust features may fail to predict the outcome of interest accurately. Test-retest imaging is recommended to assess robustness, but may not be available for the phenotype of interest. We therefore investigated 18 methods to determine feature robustness based on image perturbations. Test-retest and perturbation robustness were compared for 4032 features that were computed from the gross tumour volume in two cohorts with computed tomography imaging: I) 31 non-small-cell lung cancer (NSCLC) patients; II): 19 head-and-neck squamous cell carcinoma (HNSCC) patients. Robustness was measured using the intraclass correlation coefficient (1,1) (ICC). Features with ICC$geq0.90$ were considered robust. The NSCLC cohort contained more robust features for test-retest imaging than the HNSCC cohort ($73.5\%$ vs. $34.0\%$). A perturbation chain consisting of noise addition, affine translation, volume growth/shrinkage and supervoxel-based contour randomisation identified the fewest false positive robust features (NSCLC: $3.3\%$; HNSCC: $10.0\%$). Thus, this perturbation chain may be used to assess feature robustness.
Abstract (translated)
图像特征需要在定位，采集和分割上的差异以确保可重复性。只包含鲁棒特征的辐射模型可用于分析新图像，而具有非鲁棒特征的模型可能无法准确预测感兴趣的结果。建议使用重测成像来评估健壮性，但可能无法用于感兴趣的表型。因此，我们研究了18种方法来确定基于图像扰动的特征鲁棒性。对4032个特征进行重测试和摄动稳健性比较，这些特征是根据两个队列中的总体肿瘤体积与计算机断层扫描成像计算出来的：I）31个非小细胞肺癌（NSCLC）患者; II）：19名头颈部鳞状细胞癌（HNSCC）患者。使用组内相关系数（1,1）（ICC）测量稳健性。 ICC $ geq0.90 $的功能被认为是健壮的。与HNSCC队列相比，NSCLC队列包含更强大的重测成像特征（73.5％ vs $ 34.0 ％$）。由噪声加法，仿射平移，体积增长/收缩和基于超体素的等高线随机化构成的扰动链确定了最少的假阳性鲁棒特征（NSCLC：$ 3.3 ％$; HNSCC：$ 10.0 ％$）。因此，这个摄动链可以用来评估特征的鲁棒性。
URL
https://arxiv.org/abs/1806.06719
