AUTHOR=Yan Bo , Chang Yuanyuan , Jiang Yifeng , Liu Yuan , Yuan Junyi , Li Rong 

TITLE=RETRACTED: A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma

JOURNAL=Frontiers in Surgery

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2022.973523

DOI=10.3389/fsurg.2022.973523

ISSN=2296-875X

ABSTRACT=Objective: The morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a systematic model for predicting invasive LUAD. This study aimed to construct a predictive model based on GGN features under HRCT for LUAD.
Methods: A total of 301 surgical LUAD patients with HRCT-confirmed GGN were enrolled, and their GGN-related features were assessed by 2 individual radiologists. The pathological diagnosis of the invasive LUAD was established by pathologic examination following surgery (including 171 invasive and 130 non-invasive LUAD patients). 
Results: GGN features including shorter distance from pleura, larger diameter, area and mean CT attenuation, more heterogeneous uniformity of density, irregular shape, coarse margin, not defined nodule-lung interface, spiculation, pleural indentation, air bronchogram, vacuole sign, vessel changes, lobulation were observed in invasive LUAD patients compared with non-invasive LUAD patients. After adjustment by multivariate logistic regression model, GGN diameter (OR=1.490, 95%CI:1.326-1.674), mean CT attenuation (OR=1.007, 95%CI:1.004-1.011) and heterogeneous uniformity of density (OR=3.009, 95%CI:1.485-6.094) were independent risk factors for invasive LUAD. In addition, a predictive model integrating these three independent GGN features was established (named as invasion of lung adenocarcinoma by GGN features (ILAG)), and receiver-operating characteristic curve illustrated that the ILAG model presented good predictive value for invasive LUAD (AUC: 0.919, 95%CI: 0.889-0.949). 
Conclusions: ILAG predictive model integrating GGN diameter, mean CT attenuation and heterogeneous uniformity of density via HRCT shows great potential for early estimation of LUAD invasiveness.