Development and Validation of a Comprehensive Model for Predicting Distant Metastasis of Solid Lung Adenocarcinoma: 3D Radiomics, 2D Radiomics and Clinical Features

Zhiwei Peng; Ze Lin; Anjing He; Li Yi; Mengni Jin; Zhiyong Chen; Yahong Tao; Yuhua Yang; Can Cui; Ying Liu; Minjing Zuo

doi:10.2147/CMAR.S393058

Development and Validation of a Comprehensive Model for Predicting Distant Metastasis of Solid Lung Adenocarcinoma: 3D Radiomics, 2D Radiomics and Clinical Features

Cancer Manag Res. 2022 Dec 13:14:3437-3448. doi: 10.2147/CMAR.S393058. eCollection 2022.

Authors

Zhiwei Peng^#¹, Ze Lin^#¹, Anjing He^#¹, Li Yi¹, Mengni Jin¹, Zhiyong Chen¹, Yahong Tao¹, Yuhua Yang¹, Can Cui¹, Ying Liu¹, Minjing Zuo¹

Affiliation

¹ Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.

^# Contributed equally.

Abstract

Objective: To develop and validate models for predicting distant metastases in patients with solid lung adenocarcinomas using 3D radiomic features, 2D radiomic features, clinical features, and their combinations.

Methods: This retrospective study included 253 eligible patients with solid adenocarcinoma of the lung diagnosed at our hospital between August 2018 and August 2021. 3D and 2D regions of interest were segmented from computed tomography-enhanced thin-slice images of the venous phase, and 851 radiomic features were extracted in each region. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select radiomic features and calculate radiomic scores, and logistic regression was used to develop the model. Development of a 3D radiomics model (model 1), a 2D radiomics model (model 2), a combined 3D radiomics and 2D radiomics model (model 3), a clinical model (model 4), and a comprehensive model (model 5) for the prediction of distant metastases in patients with solid lung adenocarcinomas. Nomograms were drawn to illustrate model 5, and receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used for model evaluation.

Results: The AUC (area under the curve) of model 1, model 2, model 3, model 4, and model 5 in the test set was 0.711, 0.769, 0.775, 0.829, and 0.892, respectively. The Delong test showed that AUC values were statistically different between model 5 and model 1 (p=0.001), and there was no statistical difference in AUC between the other models. Based on a comprehensive review of DCA, ROC curve, and Akaike information criterion (AIC), Model 5 is demonstrated to have better clinical utility, goodness of fit, and parsimony.

Conclusion: A comprehensive model based on 3D radiomic features, 2D radiomic features, and clinical features has the potential to predict distant metastasis in patients with solid lung adenocarcinomas.

Keywords: adenocarcinoma of lung; computed tomography; distant metastasis; lung neoplasms; radiomics.

Grants and funding

This study was funded by the Key Project of Science and Technology Program of Jiangxi Provincial Department of Education (GJJ200106) and the Applied Research Cultivation Program of Jiangxi Provincial Department of Science and Technology (20212BAG70048).