Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics - Tan, Mingyu; Ma, Weiling; Sun, Yingli; Gao, Pan; Huang, Xuemei; Lu, Jinjuan; Chen, Wufei; Wu, Yue; Jin, Liang; Tang, Lin; Kuang, Kaiming; Li, Ming (Frontiers in Oncology, 2021)
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.