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| An Analysis of Diagnosing Pulmonary Ground Glass Nodules of Different Pathological Types Through High Quantitative Parameter of Resolution CT |
| WEI Fang, KONG Xiangchen, TIAN Xiaolei |
| Dandong First Hospital, Liaoning Dandong 118000, China |
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Abstract Objective: To explore the value of high resolution CT (HRCT) in the differential diagnosis of different pathological types of lung ground glass nodules (GGN). Methods: 210 patients who were admitted to our hospital from Dec. 2017 to Dec. 2019 with lung GGN were selected as the research object, and all patients underwent imageological examination and pathological examination. Comparison was conduct among patients with different pathological types of lung GGN on differences of lesion size, 3 d and 2 d ratios, solid component size, shape, edge, burr, boundry points and lobulation, cavitation. Results: Pathological examination showed that among the 210 patients, pulmonary inflammation, AAH, AIS, MIA and IAC were 24, 30, 50, 62 and 44, respectively. Imaging data showed that the lung GGN presented burr, lobulation, cavitation sign, pleural depression sign, irregular lesion and fuzzy boundary, and the inflammatory group <AAH group <AIS group <MIA group <IAC group, with statistically significant differences (P<0.05).In addition, as to the size of lesions and the size of solid components, data showed the inflammatory group <AAH group <AIS group <MIA group <IAC group; the difference was statistically significant (P<0.05).There was no significant difference between the five groups (P>0.05).In addition, the ROC curve showed that the AUC of solid component size in diagnosing MIA and IAC lung GGN was 1.000 and 0.937, respectively, with high accuracy, sensitivity and specificity: MIA was 100% and 100%, IAC was 88.6% and 90.3%, with good diagnostic value. Conclusion: High resolution CT has important value in distinguishing pulmonary ground-glass nodules of different pathological types, and can be comprehensively evaluated according to qualitative and quantitative data of HRCT imaging characteristics.
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2020, Vol. 26 
