AUTHOR=Huang Zhiwei , Lyu Mo , Ai Zhu , Chen Yirong , Liang Yuying , Xiang Zhiming 

TITLE=Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features

JOURNAL=Frontiers in Surgery

VOLUME=8

YEAR=2021

URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2021.736737

DOI=10.3389/fsurg.2021.736737

ISSN=2296-875X

ABSTRACT=<p><bold>Purpose:</bold> 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.</p><p><bold>Methods:</bold> 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 <italic>t</italic>-test analyzed the differences in the CT images between the negative expression group (<italic>n</italic> = 132) and the positive expression group (<italic>n</italic> = 105), and then the risk factors affecting the expression level of Ki-67 were evaluated. Patients were randomly divided into a training dataset (<italic>n</italic> = 165) and a validation dataset (<italic>n</italic> = 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.</p><p><bold>Results:</bold> 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 (<italic>p</italic> = 0.005, <italic>p</italic> = 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.</p><p><bold>Conclusion:</bold> 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.</p>