Abstract
Purpose: 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.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. A chi-square test or t-test analyzed the differences in the 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 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.Results: 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 (p = 0.005, p = 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.Conclusion: 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.
Highlights
Lung adenocarcinoma is the most commonly diagnosed histological subtype of non-small-cell lung cancer (NSCLC), which is the leading cause of cancer-related deaths worldwide [1]
This study aimed to investigate the correlation between Ki67 expression level and the subtypes of lung adenocarcinoma and to assess whether CT-based radiomic features could serve as non-invasive predictors of the Ki-67 levels in patients with lung adenocarcinoma
The inclusion criteria were: [1] patients confirmed with lung adenocarcinoma by surgical resection, [2] maximum diameter of tumor ≤3 cm, [3] complete clinicopathological data, [4] IHC examination of Ki-67 expression levels, and [5] complete CT images
Summary
Lung adenocarcinoma is the most commonly diagnosed histological subtype of non-small-cell lung cancer (NSCLC), which is the leading cause of cancer-related deaths worldwide [1]. In 2011, a new classification system for lung adenocarcinomas according to the International Association for the study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS) has been put forward, wherein the lung adenocarcinomas are mainly classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). AAH and AIS were pre-invasive lesions [2]. More and more treatment methods can be used for the treatment of lung cancer. Many patients, even patients with resectable lung cancer, still have poor prognoses [3]. Studies have found that, even for patients with complete surgical resection and in pathologic stage T1 (pathologic-T1, pT1), the treatment effects and prognoses may be significantly different [4]. There is an urgent need to determine reliable prognostic factors that can predict clinical outcomes and more precisely stratify the group of patients susceptible to poorer outcomes
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