Harmonizing the pixel size in retrospective computed tomography radiomics studies.
Harmonizing the pixel size in retrospective computed tomography radiomics studies.
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DOI：
10.1371/journal.pone.0178524
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Court L
中科院分区：
文献类型：
--
作者：
Mackin D;Fave X;Zhang L;Yang J;Jones AK;Ng CS;Court L
在放射组学研究中，一致的像素大小对于评估与强度和空间信息相关的纹理特征至关重要。为了校正可变像素尺寸的影响，我们将图像重采样与频域Butterworth滤波相结合，并对肺癌患者的计算机断层扫描（CT）进行了校正测试，重建了5次像素尺寸从0.59到0.98 mm不等的CT扫描。每次预处理和视场组合计算150个放射组学特征。采用整体一致性相关系数（OCCC）比较患者内部和患者之间的一致性。为了进一步评估校正，使用分层聚类来识别校正前后的患者扫描。为了评估校正的一般适用性，他们应用于17个放射组学幻象的CT扫描。相对于非小细胞肺癌患者的扫描，扫描仪间变异性的减少被量化。相对于79%的特征的患者间变异性，像素大小的差异导致患者内变异性较大（OCCC <95%）。然而，通过重新采样和滤波校正，只有10%的特征的患者内部变异性相对较大。经过过滤校正后，8例患者中有8例被正确聚类，而未经校正的8例患者中只有2例被正确聚类。在幻影研究中，对橡胶颗粒盒的图像进行重新采样和过滤，大大降低了61%的放射组学特征的可变性，仅增加了6%的特征的可变性。令人惊讶的是，不加滤波的重采样往往会增加可变性。综上所述，在频域应用基于重采样和Butterworth低通滤波的校正可以有效地降低由于像素大小变化引起的CT放射组学特征的变异性。这种校正也可以减少其他CT扫描采集参数带来的可变性。
Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination. Intra-patient agreement and inter-patient agreement were compared using the overall concordance correlation coefficient (OCCC). To further evaluate the corrections, hierarchical clustering was used to identify patient scans before and after correction. To assess the general applicability of the corrections, they were applied to 17 CT scans of a radiomics phantom. The reduction in the inter-scanner variability relative to non–small cell lung cancer patient scans was quantified. The variation in pixel sizes caused the intra-patient variability to be large (OCCC <95%) relative to the inter-patient variability in 79% of the features. However, with the resampling and filtering corrections, the intra-patient variability was relatively large in only 10% of the features. With the filtering correction, 8 of 8 patients were correctly clustered, in contrast to only 2 of 8 without the correction. In the phantom study, resampling and filtering the images of a rubber particle cartridge substantially reduced variability in 61% of the radiomics features and substantially increased variability in only 6% of the features. Surprisingly, resampling without filtering tended to increase the variability. In conclusion, applying a correction based on resampling and Butterworth low-pass filtering in the frequency domain effectively reduced variability in CT radiomics features caused by variations in pixel size. This correction may also reduce the variability introduced by other CT scan acquisition parameters.
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