AUTHOR=Tan Mingyu , Ma Weiling , Sun Yingli , Gao Pan , Huang Xuemei , Lu Jinjuan , Chen Wufei , Wu Yue , Jin Liang , Tang Lin , Kuang Kaiming , Li Ming 

TITLE=Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.658138

DOI=10.3389/fonc.2021.658138

ISSN=2234-943X

ABSTRACT=Abstract

Objectives: To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.
Methods: From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each region of interest (ROI). Then, radiomics features were selected by repeatability analysis and AVONA; Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis. 
Results: Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set.
Conclusions: The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.