Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma
Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma
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DOI：
10.1002/acm2.12795
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发表时间：
2019-12-27
影响因子：
2.1
通讯作者：
Ghaderi, Reza
中科院分区：
文献类型：
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作者：
Moradmand, Hajar;Aghamir, Seyed Mahmood Reza;Ghaderi, Reza
目的 研究图像预处理（强度不均匀性校正和噪声过滤）对多模态 MR 图像 (mMRI) 中胶质母细胞瘤 (GBM) 肿瘤提取的放射组学特征的鲁棒性和再现性的影响。在本研究中，从 mMRI（即 FLAIR、T1、T1C 和 T2）体积的 GBM 子区域（即水肿、坏死、增强和肿瘤）中提取每位患者的 1461 个放射组学特征，用于五种预处理组合（总共 116 880 个放射组学特征）。通过四种比较评估了放射组学特征的稳健性和再现性：(a) 基线与修改后的偏差场； (b) 基线与经过噪声过滤的修改后的偏置场； (c) 基线与修正噪声的比较，以及 (d) 偏置场校正后的基线与修正噪声的比较。使用一致性相关系数（CCC）、动态范围（DR）和类间相关系数（ICC）作为指标。在所有测量中，形状特征以及随后的局部二值模式 (LBP) 滤波图像对于偏置场校正和噪声过滤都具有高度稳定性和可重复性。在所有 MRI 模式中，与水肿 (ED: n 296/1461, 20%)、增强 (EN: n 281/1461, 19%) 和活动肿瘤区域 (TM: n 254/1461, 17%）。坏死区域（NC：n超过条449/1461，30％）具有比水肿（ED：n超过条296/1461，20％）、增强（EN：n超过条281/1461，19％）和活动肿瘤（TM：n超过条254/1461， 17%）跨所有模式的地区。此外，我们的结果发现，与噪声平滑和噪声平滑后进行偏差校正相比，偏置场校正后 (23.2%) 以及偏置场校正后进行噪声过滤 (22.4%) 的 ICC >= 0.9 的高可再现特征的百分比更高。这些初步研究结果表明，预处理序列也可以对基于 mMRI 的放射组学特征的鲁棒性和再现性产生重大影响，并且在将放射组学生物标志物应用于 GBM 患者临床之前，识别通用且一致的预处理算法是关键步骤。
To investigate the effect of image preprocessing, in respect to intensity inhomogeneity correction and noise filtering, on the robustness and reproducibility of the radiomics features extracted from the Glioblastoma (GBM) tumor in multimodal MR images (mMRI). In this study, for each patient 1461 radiomics features were extracted from GBM subregions (i.e., edema, necrosis, enhancement, and tumor) of mMRI (i.e., FLAIR, T1, T1C, and T2) volumes for five preprocessing combinations (in total 116 880 radiomics features). The robustness and reproducibility of the radiomics features were assessed under four comparisons: (a) Baseline versus modified bias field; (b) Baseline versus modified bias field followed by noise filtering; (c) Baseline versus modified noise, and (d) Baseline versus modified noise followed bias field correction. The concordance correlation coefficient (CCC), dynamic range (DR), and interclass correlation coefficient (ICC) were used as metrics. Shape features and subsequently, local binary pattern (LBP) filtered images were highly stable and reproducible against bias field correction and noise filtering in all measurements. In all MRI modalities, necrosis regions (NC: n 449/1461, 30%) had the highest number of highly robust features, with CCC and DR >= 0.9, in comparison with edema (ED: n 296/1461, 20%), enhanced (EN: n 281/1461, 19%) and active-tumor regions (TM: n 254/1461, 17%). The necrosis regions (NC: n over bar 449/1461, 30%) had a higher number of highly robust features (CCC and DR >= 0.9) than edema (ED: n over bar 296/1461, 20%), enhanced (EN: n over bar 281/1461, 19%) and active-tumor (TM: n over bar 254/1461, 17%) regions across all modalities. Furthermore, our results identified that the percentage of high reproducible features with ICC >= 0.9 after bias field correction (23.2%), and bias field correction followed by noise filtering (22.4%) were higher in contrast with noise smoothing and also noise smoothing follow by bias correction. These preliminary findings imply that preprocessing sequences can also have a significant impact on the robustness and reproducibility of mMRI-based radiomics features and identification of generalizable and consistent preprocessing algorithms is a pivotal step before imposing radiomics biomarkers into the clinic for GBM patients.