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.

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Rationale:Pulmonary hamartomas are the most common benign tumor of the lung. Two types of pathologically similar hamartomas exist based on their location. These tumors have a low incidence, are rarely reported and frequently misdiagnosed because of lack of familiarity and/or understanding concerning their imaging features.Patient concerns:Seventeen patients received treatment between June 2007 and May 2013 and had complete medical records. All of them had different degrees of cough and expectoration. Other symptoms include fever (5 cases), hemoptysis (4 cases), chest pain (3 cases), shortness of breath (2 cases), and dyspnea (1 case).Diagnoses:These patients all have pathologically confirmed, and informed the diagnosis of endobronchial hamartoma.Interventions:Unenhanced and enhanced CT scans were performed using Toshiba Aquilion 64-slice and GE Lightspeed 64-slice CT scanners. The scan was performed from the superior thoracic aperture to the lateral costophrenic angle. The transaxial CT data was inserted into a Volume Wizard workstation to reconstruct images using MPR technique.Outcomes:The relationship between the location of the tumor and bronchi was clearly displayed on the axial images in only 2 patients. In all 17 patients, reconstructed MPR images were able to display the tumor parallel to the long axis of bronchi, thus facilitating in tumor identification and positioning along the bronchial tree.Lessons:MPR images are valuable tools in the diagnosis of endobronchial hamartomas. Chiefly, these reconstructions aid in the detection of intratumoral fat/calcification and clearly demonstrate the tumors relationship and effect with the adjacent bronchi.