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| Analysis of CT Signs and Pathological Features in Patients With Ground-Glass Nodules and Lung Adenocarcinoma |
| Cui Zhiyong |
| Department of Medical Imaging,Xuchang Central Hospital Affiliated to Henan University of Science and Technology,Xuchang 461000,China |
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Abstract Objective To investigate the CT signs and pathological features in patients with ground-glass nodules and lung adenocarcinoma. Methods Using a retrospective research method,clinical data were collected among patients who underwent CT examinationand were diagnosed as ground-glass nodules and lung adenocarcinoma by pathological examination,including imaging data and pathological data.A total of 72 cases of data that met the criteria were included.According to the case classification,the patients were divided into three groups：preinvasive lesions（group A）,minimally invasive adenocarcinoma（group B）and invasive adenocarcinoma（group C）.The CT features were analyzed and compared among patients with different pathological types,including quantitative indicators（average CT value,lesion volume,maximum diameter）and qualitative indicators（image manifestations）. Results The CT signs of group C were mainly less even and uneven,with unclear borders and burr-like edges.The above signs were significantly different from those of sign distribution of groups A and B（P<0.05）.CT signs of patients in group C were 1.232 times,3.774 times and 17.857 times of the non-C type（A type and B type）in terms of uniform density,clear tumor lung boundary and burr edge risk respectively.The average CT value of group C was significantly higher than that of group A and group B [（-318.45±150.62）HU vs.（-553.06±128.37）HU vs.（-568.94±135.41）HU]（P<0.05）.The lesion volume and maximum diameter in group C were higher than those in group A and group B（P<0.05）. Conclusion CT signs of patients with ground-glass nodules and lung cancer have a certain relationship with the pathological types of lung adenocarcinoma.The clear tumor lung boundary,burr shape and CT quantitative parameters can be used as predictors.
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Received: 09 October 2020
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2021, Vol. 41 
