CT Texture Analysis for Differentiating Bronchiolar Adenoma, Adenocarcinoma In Situ, and Minimally Invasive Adenocarcinoma of the Lung.

Jinju Sun,Jingqin Fang,Yun Yao,Xiaoguang Li,Yang Li,Rongbing Jin,Haipeng Tong,Xiao Chen,Kaijun Liu,Yi Luo,Huan Liu

doi:10.3389/fonc.2021.634564

Abstract

Purpose: This study aimed to investigate the potential of computed tomography (CT) imaging features and texture analysis to distinguish bronchiolar adenoma (BA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).Materials and Methods: Fifteen patients with BA, 38 patients with AIS, and 36 patients with MIA were included in this study. Clinical data and CT imaging features of the three lesions were evaluated. Texture features were extracted from the thin-section unenhanced CT images using Artificial Intelligence Kit software. Then, multivariate logistic regression analysis based on selected texture features was employed to distinguish BA from AIS/MIA. Receiver operating characteristics curves were performed to determine the diagnostic performance of the features.Results: By comparison with AIS/MIA, significantly different CT imaging features of BA included nodule type, tumor size, and pseudo-cavitation sign. Among them, pseudo-cavitation sign had a moderate diagnostic value for distinguishing BA and AIS/MIA (AUC: 0.741 and 0.708, respectively). Further, a total of 396 quantitative texture features were extracted. After comparation, the top six texture features showing the most significant difference between BA and AIS or MIA were chosen. The ROC results showed that these key texture features had a high diagnostic value for differentiating BA from AIS or MIA, among which the value of a comprehensive model with six selected texture features was the highest (AUC: 0.977 or 0.976, respectively) for BA and AIS or MIA. These results indicated that texture analyses can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA.Conclusion: CT texture analysis can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA, which has a potential clinical value and helps pathologist and clinicians to make diagnostic and therapeutic strategies.

Highlights

Bronchiolar adenoma (BA) is a recently recognized rare benign tumor with good prognosis that corresponds to the anatomic epithelial cellular component of bronchioles [1]
The inclusion criteria were as follows: (a) thinsection computed tomography (CT) scans were performed before surgery; (b) lesions presented as solitary pulmonary nodule on thin-section CT images; (c) biopsy, surgery, chemotherapy, and radiotherapy were not performed for lesions before CT examination; (d) there was surgical resection and histopathological confirmation as BA, adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA); (e) the interval between CT scanning and surgery was within 30 days
BA presented as pure groundglass nodule (GGN) (2/15, 13.33%), subsolid GGN (3/15, 20.00%), or solid nodule (10/15, 66.67%), among which a solid nodule was more common in this study, whereas subsolid GGN was the common nodule type in AIS and MIA

Summary

Introduction

Bronchiolar adenoma (BA) is a recently recognized rare benign tumor with good prognosis that corresponds to the anatomic epithelial cellular component of bronchioles [1]. On computed tomography (CT) images, BA often presents as a peripheral irregular-shaped small solid nodule, groundglass nodule (GGN), or subsolid GGN with a central cavity [2, 3], which could be misdiagnosed as adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) [4]. As subtypes of lung adenocarcinomas, AIS or MIA requires surgery and is not expected to recur if removed completely [5,6,7]. BA, a benign tumor, does not need surgery and just needs follow-up observation. Conventional CT characteristics of pulmonary nodules such as tumor size, density, shape, and margin are often insufficient for evaluation

Objectives

Methods

Results

Conclusion