Lu Lin, Ahmed Firas S, Akin Oguz, Luk Lyndon, Guo Xiaotao, Yang Hao, Yoon Jin, Hakimi A Aari, Schwartz Lawrence H, Zhao Binsheng
Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States.
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Front Oncol. 2021 May 27;11:638185. doi: 10.3389/fonc.2021.638185. eCollection 2021.
We aimed to explore potential confounders of prognostic radiomics signature predicting survival outcomes in clear cell renal cell carcinoma (ccRCC) patients and demonstrate how to control for them.
Preoperative contrast enhanced abdominal CT scan of ccRCC patients along with pathological grade/stage, gene mutation status, and survival outcomes were retrieved from The Cancer Imaging Archive (TCIA)/The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database, a publicly available dataset. A semi-automatic segmentation method was applied to segment ccRCC tumors, and 1,160 radiomics features were extracted from each segmented tumor on the CT images. Non-parametric principal component decomposition (PCD) and unsupervised hierarchical clustering were applied to build the radiomics signature models. The factors confounding the radiomics signature were investigated and controlled sequentially. Kaplan-Meier curves and Cox regression analyses were performed to test the association between radiomics signatures and survival outcomes.
183 patients of TCGA-KIRC cohort with available imaging, pathological, and clinical outcomes were included in this study. All 1,160 radiomics features were included in the first radiomics signature. Three additional radiomics signatures were then modelled in successive steps removing redundant radiomics features first, removing radiomics features biased by CT slice thickness second, and removing radiomics features dependent on tumor size third. The final radiomics signature model was the most parsimonious, unbiased by CT slice thickness, and independent of tumor size. This final radiomics signature stratified the cohort into radiomics phenotypes that are different by cancer-specific and recurrence-free survival; HR (95% CI) = 3.0 (1.5-5.7), p <0.05 and HR (95% CI) = 6.6 (3.1-14.1), p <0.05, respectively.
Radiomics signature can be confounded by multiple factors, including feature redundancy, image acquisition parameters like slice thickness, and tumor size. Attention to and proper control for these potential confounders are necessary for a reliable and clinically valuable radiomics signature.
我们旨在探索预测透明细胞肾细胞癌（ccRCC）患者生存结局的预后放射组学特征的潜在混杂因素，并演示如何对其进行控制。
从公开可用的数据集癌症影像存档（TCIA）/癌症基因组图谱-肾透明细胞癌（TCGA-KIRC）数据库中检索ccRCC患者的术前腹部增强CT扫描以及病理分级/分期、基因突变状态和生存结局。应用半自动分割方法分割ccRCC肿瘤，并从CT图像上的每个分割肿瘤中提取1160个放射组学特征。应用非参数主成分分解（PCD）和无监督层次聚类来构建放射组学特征模型。依次研究并控制影响放射组学特征的因素。进行Kaplan-Meier曲线分析和Cox回归分析，以检验放射组学特征与生存结局之间的关联。
本研究纳入了183例具有可用影像、病理和临床结局的TCGA-KIRC队列患者。第一个放射组学特征包含所有1160个放射组学特征。随后在连续步骤中建立了另外三个放射组学特征模型，首先去除冗余放射组学特征，其次去除受CT切片厚度影响的放射组学特征，第三去除依赖肿瘤大小的放射组学特征。最终的放射组学特征模型是最简约的，不受CT切片厚度影响，且与肿瘤大小无关。这个最终的放射组学特征将队列分为不同的放射组学表型，其癌症特异性生存率和无复发生存率不同；HR（95%CI）=3.0（从1.5至5.7），p<0.05以及HR（95%CI）=6.6（从3.1至14.1），p<0.05。
放射组学特征可能受到多种因素的混杂影响，包括特征冗余、诸如切片厚度等图像采集参数以及肿瘤大小。对于可靠且具有临床价值的放射组学特征而言，关注并适当控制这些潜在的混杂因素是必要的。
