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Updated: Aug 5, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
CT imaging indications correlate with the degree of lung adenocarcinoma infiltration
Wenchen He1,2, Gang Guo1,2, Xiaoxiang Du1,2
1Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China.
Background:
Ground glass nodules (GGN) of the lung may be a precursor of lung cancer and have received increasing attention in recent years with the popularity of low-dose high-resolution computed tomography (CT). Many studies have discussed imaging features that suggest the benignity or malignancy of GGN, but the extent of its postoperative pathological infiltration is poorly understood. In this study, we identified CT imaging features that indicate the extent of GGN pathological infiltration.
Methods:
A retrospective analysis of 189 patients with pulmonary GGN from January 2020 to December 2021 at Shanxi Cancer Hospital was performed. Patients were classified according to their pathological type into non-invasive adenocarcinoma [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) in a total of 34 cases], micro-invasive adenocarcinoma (MIA) in 80 cases, and invasive adenocarcinoma (IAC) in a total of 75 cases. The general demographic data, nodule size, nodule area, solid component, CT indications and pathological findings of the three groups of patients were analyzed to predict the correlation between GGN and the degree of lung adenocarcinoma infiltration.
Results:
No statistically significant differences were found among the three groups in general information, vascular signs, and vacuolar signs (P > 0.05). Statistically significant differences among the three groups were found in nodule size, nodule area, lobar signs, pleural traction, burr signs, bronchial signs, and solid components (P < 0.05). Logistic regression equation tests based on the statistically significant indicators showed that nodal area, lobar sign, pleural pull, burr sign, bronchial sign, and solid component were independent predictors of lung adenocarcinoma infiltration. The subject operating characteristic (ROC) curve analysis showed that nodal area is valuable in predicting GGN infiltration.
Conclusion:
CT-based imaging indications are useful predictors of infiltrative adenocarcinoma manifested as pulmonary ground glass nodules.
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