Wang Zixing, Yang Cuihong, Han Wei, Sui Xin, Zheng Fuling, Xue Fang, Xu Xiaoli, Wu Peng, Chen Yali, Gu Wentao, Song Wei, Jiang Jingmei
Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China.
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Insights Imaging. 2022 Apr 28;13(1):82. doi: 10.1186/s13244-022-01204-9.
Radiomics-based image metrics are not used in the clinic despite the rapidly growing literature. We selected eight promising radiomic features and validated their value in decoding lung cancer heterogeneity.
CT images of 236 lung cancer patients were obtained from three different institutes, whereupon radiomic features were extracted according to a standardized procedure. The predictive value for patient long-term prognosis and association with routinely used semantic, genetic (e.g., epidermal growth factor receptor (EGFR)), and histopathological cancer profiles were validated. Feature measurement reproducibility was assessed.
All eight selected features were robust across repeat scans (intraclass coefficient range: 0.81-0.99), and were associated with at least one of the cancer profiles: prognostic, semantic, genetic, and histopathological. For instance, "kurtosis" had a high predictive value of early death (AUC at first year: 0.70-0.75 in two independent cohorts), negative association with histopathological grade (Spearman's r: - 0.30), and altered expression levels regarding EGFR mutation and semantic characteristics (solid intensity, spiculated shape, juxtapleural location, and pleura tag; all p < 0.05). Combined as a radiomic score, the features had a higher area under curve for predicting 5-year survival (train: 0.855, test: 0.780, external validation: 0.760) than routine characteristics (0.733, 0.622, 0.613, respectively), and a better capability in patient death risk stratification (hazard ratio: 5.828, 95% confidence interval: 2.915-11.561) than histopathological staging and grading.
We highlighted the clinical value of radiomic features. Following confirmation, these features may change the way in which we approach CT imaging and improve the individualized care of lung cancer patients.
尽管基于放射组学的图像指标相关文献迅速增多，但尚未应用于临床。我们选取了八个有前景的放射组学特征，并验证了它们在解读肺癌异质性方面的价值。
从三个不同机构获取了236例肺癌患者的CT图像，然后按照标准化程序提取放射组学特征。对患者长期预后的预测价值以及与常规使用的语义、基因（如表皮生长因子受体（EGFR））和组织病理学癌症特征的相关性进行了验证。评估了特征测量的可重复性。
所有八个选定特征在重复扫描中均表现稳定（组内相关系数范围：0.81 - 0.99），并且与至少一种癌症特征相关：预后、语义、基因和组织病理学。例如，“峰度”对早期死亡具有较高的预测价值（在两个独立队列中，第一年的AUC为0.70 - 0.75），与组织病理学分级呈负相关（Spearman相关系数r：-0.30），并且在EGFR突变和语义特征（实性密度、毛刺状形态、胸膜下位置和胸膜牵拉；所有p < 0.05）方面表达水平发生改变。作为放射组学评分综合起来，这些特征在预测5年生存率方面的曲线下面积（训练集：0.855，测试集：0.780，外部验证：0.760）高于常规特征（分别为0.733、0.622、0.613），并且在患者死亡风险分层方面（风险比：5.828，95%置信区间：2.915 - 11.561）比组织病理学分期和分级具有更好的能力。
我们强调了放射组学特征的临床价值。经确认后，这些特征可能会改变我们处理CT成像的方式，并改善肺癌患者的个体化治疗。
