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

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 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.

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.

Keywords: X-ray computer; machine learning; pulmonary nodules; radiomics; tomography; volume doubling time.

Figure 1

The workflow of the study.

Figure 2

AUC value of the training set. (A) The AUC value of the radiographic model with the training set is 0.717. (B) The AUC value of the radiomics model with the training set is 0.876. (C) The AUC value of the combined radiographic-radiomics model with the training set is 0.903.

Figure 3

AUC value of the validation set. (A–C) The AUC value of the radiographic model with the validation set is 0.727. (B) The AUC value of the radiomics model with the validation set is 0.710. (C) The AUC value of the combined radiographic-radiomics model with the validation set is 0.778.