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. 2022 Jan 28;42(1):BSR20212416.
doi: 10.1042/BSR20212416.

Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs

Weiyuan Fang  1 , Guorui Zhang  1 , Yali Yu  1 , Hongjie Chen  1 , Hong Liu  1
Affiliations

Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs

Weiyuan Fang et al. Biosci Rep. .

Abstract

Objective: To explore the value of quantitative parameters of artificial intelligence (AI) and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs).

Methods: CT images of 224 GGNs from 210 individuals were collected retrospectively and classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. AI was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was built and evaluated.

Results: Of the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, and IAC, respectively. In terms of AI parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P<0.0001). Except for the CT signs of the location, and the tumor-lung interface, there were significant differences among the three groups in the density, shape, vacuolar signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P<0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P<0.05).

Conclusion: AI parameters are valuable for identifying subtypes of early lung adenocarcinoma and have improved diagnostic efficacy when combined with CT signs.

Keywords: Artificial intelligence; Computed tomography; Ground-glass nodules; Lung cancer.

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Conflict of interest statement

The authors declare that there are no competing interests associated with the manuscript.

Figures

Figure 1
Figure 1. ROC curve graph of predictive model 1 and quantitative parameters for distinguishing AAH/AIS and MIA
Figure 2
Figure 2. ROC curve graph of predictive model 2 and quantitative parameters for distinguishing MIA and IAC
See this image and copyright information in PMC

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