Use of a Radiomics Model to Predict Tumor Invasiveness of Pulmonary Adenocarcinomas Appearing as Pulmonary Ground-Glass Nodules.

Xing Xue,Linpeng Yao,Xin Li,Feng Chen,Feng Cui,Siying Zhang,Peipei Pang,Jianhua Yan,Wei Peng,Yong Yang,Qiang Huang,Yuqing Lian

doi:10.1155/2018/6803971

Abstract

Background It is important to distinguish the classification of lung adenocarcinoma. A radiomics model was developed to predict tumor invasiveness using quantitative and qualitative features of pulmonary ground-glass nodules (GGNs) on chest CT. Materials and Methods A total of 599 GGNs [including 202 preinvasive lesions and 397 minimally invasive and invasive pulmonary adenocarcinomas (IPAs)] were evaluated using univariate, multivariate, and logistic regression analyses to construct a radiomics model that predicted invasiveness of GGNs. In primary cohort (comprised of patients scanned from August 2012 to July 2016), preinvasive lesions were distinguished from IPAs based on pure or mixed density (PM), lesion shape, lobulated border, and pleural retraction and 35 other quantitative parameters (P <0.05) using univariate analysis. Multivariate analysis showed that PM, lobulated border, pleural retraction, age, and fractal dimension (FD) were significantly different between preinvasive lesions and IPAs. After logistic regression analysis, PM and FD were used to develop a prediction nomogram. The validation cohort was comprised of patients scanned after Jan 2016. ResultsThe model showed good discrimination between preinvasive lesions and IPAs with an area under curve (AUC) of 0.76 [95% CI: 0.71 to 0.80] in ROC curve for the primary cohort. The nomogram also demonstrated good discrimination in the validation cohort with an AUC of 0.79 [95% CI: 0.71 to 0.88]. Conclusions For GGNs, PM, lobulated border, pleural retraction, age, and FD were features discriminating preinvasive lesions from IPAs. The radiomics model based upon PM and FD may predict the invasiveness of pulmonary adenocarcinomas appearing as GGNs.

Highlights

The detection rate of pulmonary nodules has increased due to advances in diagnostic imaging and the widespread use of low-dose chest CT screening
The inclusion criteria for the study were as follows: (1) patients were scanned with routine chest CT using a slice thickness of 5.0 mm; (2) diagnosis without distant metastasis was confirmed by surgery and pathology; (3) only the last CT scan before surgery was chosen for the study; (4) nodules with pure ground-glass nodules (GGNs) or a partially solid component were selected on CT scans; and (5) the diameter of each nodule was between 5.0 and 30.0 mm
To the best our knowledge, the present study is the first to use a quantitative radiomics model, incorporating clinical and imaging features, to differentiate preinvasive lesions from invasive pulmonary adenocarcinomas (IPAs) appearing as GGNs

Summary

Introduction

The detection rate of pulmonary nodules has increased due to advances in diagnostic imaging and the widespread use of low-dose chest CT screening. In 2011, the International Association for the Study of Lung Cancer (IASLC), the American Thoracic Society (ATS), and the European Respiratory Society (ERS) set a new standard for the classification of lung adenocarcinoma. A total of 599 GGNs [including 202 preinvasive lesions and 397 minimally invasive and invasive pulmonary adenocarcinomas (IPAs)] were evaluated using univariate, multivariate, and logistic regression analyses to construct a radiomics model that predicted invasiveness of GGNs. In primary cohort (comprised of patients scanned from August 2012 to July 2016), preinvasive lesions were distinguished from IPAs based on pure or mixed density (PM), lesion shape, lobulated border, and pleural retraction and 35 other quantitative parameters (P

Methods

Results

Conclusion