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

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

doi:10.1042/bsr20212416

Weiyuan Fang, Hong Liu + Show 3 more

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https://doi.org/10.1042/bsr20212416

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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.

Highlights

Lung cancer is the second most common cancer and remains the leading cause of cancer deaths for both men and women, with an estimated 1.8 million deaths (18%) [1]
We retrospectively reviewed the data of patients with 224 ground-glass nodule (GGN) to explore the value of artificial intelligence (AI) quantitative parameters combined with computed tomography (CT) signs in the differential diagnosis of pathological subtypes of lung adenocarcinoma
The results showed that the 3D mean diameter, mean CT value, and irregular shape were independent predictive factors identifying adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) groups, and the areas under the curve (AUC) of predictive model 1 was 0.779, which was higher than the independent diagnosis of each quantitative parameter

Summary

Introduction

Lung cancer is the second most common cancer and remains the leading cause of cancer deaths for both men and women, with an estimated 1.8 million deaths (18%) [1]. Ground-glass nodules (GGNs), called subsolid nodules, are a typical imaging manifestation and can be found at any stage of lung adenocarcinoma [3, 4]. There are two types of GGNs: mixed ground-glass nodules (mGGNs) and pure ground-glass nodules (pGGNs). The detection rate of GGNs has grown dramatically as a result of the widespread use of high-resolution computed tomography (HRCT) in early lung cancer screening [5]. The management principles, surgical approach, and prognosis of GGNs differ depending on the pathological subtype, and accurate preoperative prediction of pathological subtypes is a critical step in optimizing patient management [6,7,8]. The 5-year survival rate can be as high as License 4.0 (CC BY)

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