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

• Main Authors: Mingyu Tan, Weiling Ma, Yingli Sun, Pan Gao, Xuemei Huang, Jinjuan Lu, Wufei Chen, Yue Wu, Liang Jin, Lin Tang, Kaiming Kuang, Ming Li
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-04-01
• Series: Frontiers in Oncology
• Subjects: pulmonary nodules; tomography; X-ray computer; radiomics; volume doubling time; machine learning
• Online Access: https://www.frontiersin.org/articles/10.3389/fonc.2021.658138/full

ObjectivesTo investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.MethodsFrom 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 volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); 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.ResultsSixty-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.ConclusionsThe model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.