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

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

doi:10.3389/fonc.2021.658138

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

Front Oncol. 2021 Apr 15:11:658138. doi: 10.3389/fonc.2021.658138. eCollection 2021.

Authors

Mingyu Tan¹, Weiling Ma¹, Yingli Sun¹, Pan Gao¹, Xuemei Huang¹, Jinjuan Lu¹, Wufei Chen¹, Yue Wu², Liang Jin¹, Lin Tang³, Kaiming Kuang⁴, Ming Li¹

Affiliations

¹ Department of Radiology, Huadong Hospital Affiliated With Fudan University, Shanghai, China.
² Department of Thoracic Surgery, Huadong Hospital Affiliated With Fudan University, Shanghai, China.
³ Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
⁴ Dianei Technology, Shanghai, China.

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.