Huang Zhiwei, Lyu Mo, Ai Zhu, Chen Yirong, Liang Yuying, Xiang Zhiming
Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Front Surg. 2021 Oct 18;8:736737. doi: 10.3389/fsurg.2021.736737. eCollection 2021.
The aims of this study were to combine CT images with Ki-67 expression to distinguish various subtypes of lung adenocarcinoma and to pre-operatively predict the Ki-67 expression level based on CT radiomic features. Data from 215 patients with 237 pathologically proven lung adenocarcinoma lesions who underwent CT and immunohistochemical Ki-67 from January 2019 to April 2021 were retrospectively analyzed. The receiver operating curve (ROC) identified the Ki-67 cut-off value for differentiating subtypes of lung adenocarcinoma. A chi-square test or -test analyzed the differences in the CT images between the negative expression group ( = 132) and the positive expression group ( = 105), and then the risk factors affecting the expression level of Ki-67 were evaluated. Patients were randomly divided into a training dataset ( = 165) and a validation dataset ( = 72) in a ratio of 7:3. A total of 1,316 quantitative radiomic features were extracted from the Analysis Kinetics (A.K.) software. Radiomic feature selection and radiomic classifier were generated through a least absolute shrinkage and selection operator (LASSO) regression and logistic regression analysis model. The predictive capacity of the radiomic classifiers for the Ki-67 levels was investigated through the ROC curves in the training and testing groups. The cut-off value of the Ki-67 to distinguish subtypes of lung adenocarcinoma was 5%. A comparison of clinical data and imaging features between the two groups showed that histopathological subtypes and air bronchograms could be used as risk factors to evaluate the expression of Ki-67 in lung adenocarcinoma ( = 0.005, = 0.045, respectively). Through radiomic feature selection, eight top-class features constructed the radiomic model to pre-operatively predict the expression of Ki-67, and the area under the ROC curves of the training group and the testing group were 0.871 and 0.8, respectively. Ki-67 expression level with a cut-off value of 5% could be used to differentiate non-invasive lung adenocarcinomas from invasive lung adenocarcinomas. It is feasible and reliable to pre-operatively predict the expression level of Ki-67 in lung adenocarcinomas based on CT radiomic features, as a non-invasive biomarker to predict the degree of malignant invasion of lung adenocarcinoma, and to evaluate the prognosis of the tumor.
本研究的目的是将CT图像与Ki-67表达相结合，以区分肺腺癌的不同亚型，并基于CT影像组学特征术前预测Ki-67表达水平。回顾性分析了2019年1月至2021年4月期间215例经病理证实的肺腺癌病变患者的资料，这些患者均接受了CT检查及Ki-67免疫组化检测。采用受试者工作特征曲线（ROC）确定区分肺腺癌亚型的Ki-67临界值。采用卡方检验或t检验分析阴性表达组（n = 132）和阳性表达组（n = 105）CT图像的差异，进而评估影响Ki-67表达水平的危险因素。患者按7:3的比例随机分为训练数据集（n = 165）和验证数据集（n = 72）。通过分析动力学（A.K.）软件共提取了1316个定量影像组学特征。通过最小绝对收缩和选择算子（LASSO）回归及逻辑回归分析模型进行影像组学特征选择并生成影像组学分类器。通过训练组和测试组的ROC曲线研究影像组学分类器对Ki-67水平的预测能力。区分肺腺癌亚型的Ki-67临界值为5%。两组临床资料和影像特征比较显示，组织病理学亚型和空气支气管征可作为评估肺腺癌Ki-67表达的危险因素（分别为P = 0.005，P = 0.045）。通过影像组学特征选择，8个顶级特征构建了术前预测Ki-67表达的影像组学模型，训练组和测试组的ROC曲线下面积分别为0.871和0.8。Ki-67表达水平临界值为5%可用于区分非侵袭性肺腺癌和侵袭性肺腺癌。基于CT影像组学特征术前预测肺腺癌Ki-67表达水平作为预测肺腺癌恶性侵袭程度及评估肿瘤预后的非侵入性生物标志物是可行且可靠的。
