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=Volume 8 - 2021

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=Purpose: Aim to combine CT images with Ki-67 expression to distinguish various subtypes of lung adenocarcinoma and to preoperatively predict the Ki-67 expression level based on CT radiomic features. 
Methods: 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. The Chi-square test or t-test analyzed the differences in CT images between the negative expression group (n = 132) and the positive expression group (n = 105), and then the risk factors affecting the expression level of Ki-67 were evaluated. Patients were randomly divided into a training dataset (n = 165) and a validation dataset (n = 72) in a ratio of 7:3. A total of 1316 quantitative radiomic features were extracted from Analysis Kinetics (A.K.) software. Radiomic feature selection and radiomic classifier were generated through 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 ROC curves in the training and testing groups.
Results: The cut-off value of the Ki-67 to distinguish subtypes of lung adenocarcinoma was 5%. Comparison of clinical data and imaging features between the two groups showed that histopathological subtypes and air bronchogram could be used as risk factors to evaluate the expression of Ki-67 in lung adenocarcinoma (P = 0.005, P = 0.045, respectively). Through the radiomic feature selection, 8 top-class features constructed the radiomic model to preoperatively predict the expression of Ki-67, the area under the ROC curves of the training group and the testing group were 0.871 and 0.800. 
Conclusion: Ki-67 expression level with a cut-off value of 5% could be used to differentiate non-invasive lung adenocarcinoma from invasive lung adenocarcinoma. It is feasible and reliable to preoperatively predict the expression level of Ki-67 in lung adenocarcinoma 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.