Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules

Yuting Zheng; Xiaoyu Han; Xi Jia; Chengyu Ding; Kailu Zhang; Hanting Li; Xuexiang Cao; Xiaohui Zhang; Xin Zhang; Heshui Shi

doi:10.3389/fonc.2023.1208758

Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules

Front Oncol. 2023 Aug 10:13:1208758. doi: 10.3389/fonc.2023.1208758. eCollection 2023.

Authors

Yuting Zheng^{1

2}, Xiaoyu Han^{1

2}, Xi Jia^{1

2}, Chengyu Ding³, Kailu Zhang^{1

2}, Hanting Li^{1

2}, Xuexiang Cao⁴, Xiaohui Zhang⁴, Xin Zhang^{1

2}, Heshui Shi^{1

2}

Affiliations

¹ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
² Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
³ ShuKun (BeiJing) Technology Co., Ltd., Beijing, China.
⁴ Clinical Solution, Philips Healthcare, Shanghai, China.

Abstract

Objectives: To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery.

Methods: The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA).

Results: The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model.

Conclusion: DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.

Keywords: dual-energy spectral computed tomography; ground-glass nodules; invasion; lung adenocarcinoma; prediction; radiomics.