Construction of pathological classification prediction model for malignant pulmonary pure ground-glass nodule patients based on CT imaging
10.3760/cma.j.cn371439-20230704-00124
- VernacularTitle:基于CT影像学特征的恶性肺纯磨玻璃结节患者病理分型预测模型构建
- Author:
Yu CHEN
1
;
Hua XU
;
Hai LIU
;
Shixin CHEN
Author Information
1. 三二〇一医院医学影像科，汉中 723000
- Keywords:
Lung neoplasms;
Tomography technology, X-ray computer;
Pathology;
Prediction
- From:
Journal of International Oncology
2023;50(11):655-660
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct the pathological classification prediction model for malignant pulmonary pure ground-glass nodule (pGGN) patients based on CT imaging and to analyze the predictive efficacy.Methods:A total of 193 pulmonary pGGN patients with histopathological findings who underwent surgical treatment in 3201 Hospital from January 2018 to December 2022 were retrospectively included, with 217 lesions. All patiens were divided into invasive adenocarcinoma group (68 patients, 73 lesions) and non-invasive adenocarcinoma group (125 patients, 144 lesions) based on whether they were invasive adenocarcinoma; The clinical feature data and CT imaging parameters were compared between the two groups; Multivariate logistic regression analysis was used to analyze the risk factors of malignant lung pGGN diagnosed as invasive adenocarcinoma; A logistic prediction model for pathological classification of malignant lung pGGN was constructed to analyze its predictive efficacy using receiver operator characteristic (ROC) curves.Results:The percentages of burr signs in invasive adenocarcinoma group and non-invasive adenocarcinoma group were 34.25% (25/73) and 5.56% (8/144), respectively; The proportion of internal vascular signs was 93.15% (68/73) and 18.75% (27/144), respectively; The air bronchial signs were 67.12% (49/73) and 12.50% (18/144), respectively, with statistically significant differences ( χ2=30.93, P<0.001; χ2=108.95, P<0.001; χ2=67.72, P<0.001). The maximum CT value of nodular plain scan in invasive adenocarcinoma group (-527.82±72.95) HU, was significantly higher than that in non-invasive adenocarcinoma group (-592.79±86.47) HU, with a statistically significant difference ( t=-5.50, P<0.001). The results of multivariate analysis showed that spicule sign ( OR=8.93, 95% CI: 1.99-39.97, P=0.004), air bronchial sign ( OR=8.16, 95% CI: 2.91-22.86, P<0.001), internal vascular sign ( OR=48.39, 95% CI: 14.81-158.07, P<0.001) and the maximum CT value of plain scan ( OR=1.01, 95% CI: 1.00-1.02, P=0.001) were independent factors for the diagnosis of malignant pulmonary pGGN as invasive adenocarcinoma. Using burr sign, air bronchogram sign, internal vascular sign, maximum CT value of plain scan, and logistic regression model P-value to predict the pathological classification of malignant lung pGGN, the optimal cutoff values were 0.50, 0.50, 0.50, -547.23 HU, 0.46, and the area under the curve was 0.64, 0.77, 0.87, 0.69 and 0.96, respectively. The sensitivity was 34.25%, 67.12%, 93.15%, 82.19% and 89.04%, and the specificity was 94.44%, 87.50%, 81.25%, 46.53% and 92.36%, respectively, with the Jordan index being 28.69%, 54.62%, 74.40%, 28.72% and 81.40%. Conclusion:Patients with malignant pulmonary pGGN who have concomitant spicule sign, air bronchial sign, internal vascular sign, and maximum CT value on plain scan have a higher risk of being diagnosed with invasive adenocarcinoma; The predictive model constructed based on spicule sign, air bronchial sign, internal vascular sign, and maximum CT value on plain scan has shown good predictive performance in assisting the differential diagnosis of malignant pulmonary pGGN pathological classification.
