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

Bo Yan; Yuanyuan Chang; Yifeng Jiang; Yuan Liu; Junyi Yuan; Rong Li

doi:10.3389/fsurg.2022.973523

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

Front Surg. 2022 Aug 26:9:973523. doi: 10.3389/fsurg.2022.973523. eCollection 2022.

Authors

Bo Yan^{1

2}, Yuanyuan Chang^{1

2}, Yifeng Jiang³, Yuan Liu⁴, Junyi Yuan⁵, Rong Li^{1

2}

Affiliations

¹ Clinical Research Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
³ Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ Department of Statistics Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁵ Department of Information Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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.

Keywords: ground-glass nodule features; high-resolution computed tomography; invasion of lung adenocarcinoma by GGN features predictive model; invasiveness; lung adenocarcinoma.

Publication types

Retracted Publication