18F-FDG PET/CT Analysis of Early-Stage Lung Cancer Presenting as Pure Ground-Glass Opacity lung nodules for distinction of invasiveness.

Abstract

Abstract Objective: In the past decade, as the increasing application of high-resolution computed tomography (HRCT) screening, pure ground-glass opacity nodules (pGGNs) are encountered more frequently. However, the clinical strategies for invasive and noninvasive pGGNs are different. Thus, in this study, we aimed to analyze the value and efficacy of the 18F-FDG PET/CT combined with HRCT for identifying the atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma. Methods: The 18F-FDG PET/CT images and pathologic specimens of 90 patients with resected pGGNs at 2nd Xiangya Hospital in China between August 2013 and November 2019 were reviewed. The nodule size, density, metabolic parameters, and radiologic characteristic were assessed from 18F-FDG PET/CT and HRCT datasets. To investigate the invasiveness of the pGGNs lesions, we grouped AAH, AIS and MIA into the non-IAC group and IA into the IAC group. Then a mathematical model for predicting the invasiveness of pGGNs was established and assessed based on multivariate logistics regression. Results: Of 90 pGGNs, 57 were non-IAC (63.3%, 29 were AAH and AIS, 28 were MIA), and 33 were IAC (36.7%). There is no significant difference between non-IAC and IAC groups in terms of age, sex, smoking history, periphery, bubble, or lobulation (p＞0.05). Multivariate logistic regression analysis identified the maximum of CT value (CTmax), average standard uptake value (SUVmean), vessel pass, and speculation as independent predictors of invasiveness. The mathematical model we established as y=exp(x)/[1+exp(x)],x=1.445+1.184×length+0.009×mean attenuation+1.582×SUVmax, where e is the natural logarithm. When the cut-off value was set at 0.82, the sensitivity, specificity, and accuracy of our model was 68.9%, 96.6%, and 83.3%, respectively. The area under the receiver operating characteristic (ROC) curve of the model was 0.881 (95% confidence interval (CI): 0.807 to 0.955), which was higher than the model without 18F-FDG PET/CT parameters (AUC value of the model without 18F-FDG 0.848). Conclusion: Our study demonstrated a nomogram to accurately discriminate the invasive status of the pGGNs by visual assessment and 18F-FDG PET/CT parameters. The predicting model could assist surgeons to make decisions for the treatment of patients with pGGN.