Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter.

Lili Shi,Jinli Zhao,Yunpeng Wang,Linxiao Zhou,Fei Shan,Lei Liu,Xueqing Peng,Mingxiang Feng,Yi Zhan,Weiya Shi

doi:10.3389/fonc.2021.618677

Abstract

PurposeTo develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter.Materials and MethodsThis retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts.ResultsFive radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful.ConclusionThis study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.

Highlights

Lung cancer is one of the most commonly diagnosed human malignancy and the leading cause of cancer-related death worldwide [1]
The purpose of this study was to investigate the ability of computed tomography (CT) radiomics features combined with CT radiological features to differentiate invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA), and develop a nomogram incorporating CT radiomics signatures and radiological features to provide an individual, preoperative assessment of the risk of IAC in patients with ground-glass nodules (GGNs) measuring 5-10 mm
The exclusion criteria were as follows: 1) no routine CT examination had been performed in the month before surgery; 2) a series of consecutive CT images with a thickness of more than 1 mm; 3) CT images with severe respiratory motion artifacts; 4) the average diameter of nodule was smaller than 5mm or larger than 10mm; 5) the nodule presenting as a solid nodule

Summary

Introduction

Lung cancer is one of the most commonly diagnosed human malignancy and the leading cause of cancer-related death worldwide [1]. Adenocarcinoma is the most common histologic type of lung cancer and its incidence has increased over the past few decades, accounting for more than 40% of the total nowadays [2]. It was classified into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) in the 2015 World Health Organization (WHO) classification of lung tumors [3]. Discriminating IAC from AIS/MIA before surgery could help clinicians to assess prognosis in order to improve clinical decision making and avoid over- or undertreatment, without the need for invasive procedures

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