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

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 system...

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Detalles Bibliográficos
Autores principales: Yan, Bo, Chang, Yuanyuan, Jiang, Yifeng, Liu, Yuan, Yuan, Junyi, Li, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458920/
https://www.ncbi.nlm.nih.gov/pubmed/36090345
http://dx.doi.org/10.3389/fsurg.2022.973523
Descripción
Sumario: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.