Quantitative CT analysis of early-stage lung adenocarcinoma with pure ground-glass opacity

Abstract

Objective To investigate if quantitative analysis of early-stage lung adenocarcinoma manifesting as a pure ground-glass nodule (pGGN) on CT can predict its pathological grading. Methods One hundred and five patients who had undergone curative resection for lung adenocarcinoma, manifesting as a pure ground-glass nodule, were retrospectively enrolled from June 2012 to October 2015 in Changzheng Hospital. Among 110 lesions, there were 22 typical adenomatous hyperplasia (AAH), 28 adenocarcinoma in situ (AIS), 28 minimally invasive adenocarcinoma (MIA), and 32 invasive adenocarcinoma (IAC). We evaluated all CT images using United Imaging CT advanced post-processing workstation, and all pGGNs were analyzed as follows: long diameter and area of maximum section, volume, mean CT number, mass, minimum CT number, maximum CT number, and 2%, 5%, 25%, 50%, 75%, 95%, 98% percentile CT number. Variables between different pathological grades and between before and after invasion satisfying the law of normal distribution and homoscedasticity were compared using one-way AVOVA, other variables were compared using Kruskal-Wallis H test. Each individual variable were enrolled in ROC analysis, and Logistic regression analysis was performed by taking if pGGN was invasive lesion as the dependent variable, and long diameter and area of maximum section, volume and maximum CT number were taken as independent variables. Results The lesion size(including long diameter and area of maximum section, volume), mean CT number, mass, 5%, 25%, 50%, 75%, 95%, 98% percentile CT number and maximum CT number were statistically different among four pathological types of AAH, AIS, MIA and IAC(P<0.05). Between preinvasive lesion and invasive lesion, the lesion size, mean CT number, mass, 2%, 25%, 50%, 75%, 95%, 98% percentile CT number and maximum CT number were also statistically different(P<0.05). ROC analysis was taken for the individual variables, variables which area under the curve (AUC) of more than 0.7 were the long diameter of maximum section (AUC=0.754, P<0.001), area of maximum section volume(AUC=0.787, P<0.001), volume(AUC=0.788, P<0.001), mass(AUC=0.822, P<0.001) and 98% percentile CT number(AUC=0.714, P<0.001), maximum CT number (AUC= 0.759, P<0.001) . Logistic regression analysis showed that the long diameter of maximum section(OR=1.143, 95% CI 1.027-1.273, P=0.015)and the mean CT number (OR=1.005, 95% CI 1.002-1.009, P=0.001) were independent risk factors that predicting pGGN was invasive lesion, the ROC analysis was performed based on the predicted probability of Logistic regression model, and the AUC was 0.793(P<0.001). Conclusion Quantitative analysis of early-stage lung adenocarcinoma manifesting as a pure ground-glass nodule on CT to get its size, the maximum CT number and mass, can be useful for predicting pathological grading. Key words: Lung neoplasms; Tomography, X-ray computed; Pathology