Huang Qiao, Lu Lin, Dercle Laurent, Lichtenstein Philip, Li Yajun, Yin Qian, Zong Min, Schwartz Lawrence, Zhao Binsheng
Columbia University Medical Center, Department of Radiology, New York, New York, United States.
J Med Imaging (Bellingham). 2018 Jan;5(1):011005. doi: 10.1117/1.JMI.5.1.011005. Epub 2017 Oct 20.
Radiomic features characterize tumor imaging phenotype. Nonsmall cell lung cancer (NSCLC) tumors are known for their complexity in shape and wide range in density. We explored the effects of variable tumor contouring on the prediction of epidermal growth factor receptor (EGFR) mutation status by radiomics in NSCLC patients treated with a targeted therapy (Gefitinib). Forty-six early stage NSCLC patients (EGFR mutant:wildtype = 20:26) were included. Three experienced radiologists independently delineated the tumors using a semiautomated segmentation software on a noncontrast-enhanced baseline and three-week post-therapy CT scan images that were reconstructed using 1.25-mm slice thickness and lung kernel. Eighty-nine radiomic features were computed on both scans and their changes (radiomic delta-features) were calculated. The highest area under the curves (AUCs) were 0.87, 0.85, and 0.80 for the three radiologists and the number of significant features ([Formula: see text]) was 3, 5, and 0, respectively. The AUCs of a single feature significantly varied among radiologists (e.g., 0.88, 0.75, and 0.73 for run-length primitive length uniformity). We conclude that a three-week change in tumor imaging phenotype allows identifying the EGFR mutational status of NSCLC. However, interobserver variability in tumor contouring translates into a significant variability in radiomic metrics accuracy.
放射组学特征可表征肿瘤的影像表型。非小细胞肺癌（NSCLC）肿瘤以其形状复杂和密度范围广泛而闻名。我们探讨了在接受靶向治疗（吉非替尼）的NSCLC患者中，可变肿瘤轮廓对通过放射组学预测表皮生长因子受体（EGFR）突变状态的影响。纳入了46例早期NSCLC患者（EGFR突变型：野生型 = 20:26）。三名经验丰富的放射科医生使用半自动分割软件，在非增强基线和治疗后三周的CT扫描图像上独立勾勒肿瘤轮廓，这些图像使用1.25毫米层厚和肺内核重建。在两次扫描上计算了89个放射组学特征，并计算了它们的变化（放射组学差值特征）。三位放射科医生的曲线下面积（AUC）最高分别为0.87、0.85和0.80，显著特征数量（[公式：见原文]）分别为3、5和0。单个特征的AUC在放射科医生之间有显著差异（例如，行程原始长度均匀性的AUC分别为0.88、0.75和0.73）。我们得出结论，肿瘤影像表型的三周变化有助于识别NSCLC的EGFR突变状态。然而，肿瘤轮廓勾画的观察者间变异性转化为放射组学指标准确性的显著变异性。
