The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma

Xiaoyu Han; Jun Fan; Yuting Zheng; Chengyu Ding; Xiaohui Zhang; Kailu Zhang; Na Wang; Xi Jia; Yumin Li; Jia Liu; Jinlong Zheng; Heshui Shi

doi:10.3389/fonc.2022.757389

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma

Front Oncol. 2022 Jul 8:12:757389. doi: 10.3389/fonc.2022.757389. eCollection 2022.

Authors

Xiaoyu Han^{1

2}, Jun Fan³, Yuting Zheng^{1

2}, Chengyu Ding⁴, Xiaohui Zhang⁴, Kailu Zhang^{1

2}, Na Wang³, Xi Jia^{1

2}, Yumin Li^{1

2}, Jia Liu^{1

2}, Jinlong Zheng^{1

2}, Heshui Shi^{1

2}

Affiliations

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

Abstract

Objectives: Spread through air spaces (STAS), a new invasive pattern in lung adenocarcinoma (LUAD), is a risk factor for poor outcome in early-stage LUAD. This study aimed to develop and validate a CT-based radiomics model for predicting STAS in stage IA LUAD.

Methods: A total of 395 patients (169 STAS positive and 226 STAS negative cases, including 316 and 79 patients in the training and test sets, respectively) with stage IA LUAD before surgery were retrospectively included. On all CT images, tumor size, types of nodules (solid, mix ground-glass opacities [mGGO] and pure GGO [pGGO]), and GGO percentage were recorded. Region of interest (ROI) segmentation was performed semi-automatically, and 1,037 radiomics features were extracted from every segmented lesion. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were used to filter unstable (ICC < 0.75) and redundant features (r > 0.8). A temporary model was established by multivariable logistic regression (LR) analysis based on selected radiomics features. Then, seven radiomics features contributing the most were selected for establishing the radiomics model. We then built two predictive models (clinical-CT model and MixModel) based on clinical and CT features only, and the combination of clinical-CT and Rad-score, respectively. The performances of these three models were assessed.

Results: The radiomics model achieved good performance with an area under of curve (AUC) of 0.812 in the training set, versus 0.850 in the test set. Furthermore, compared with the clinical-CT model, both radiomics model and MixModel showed higher AUC and better net benefit to patients in the training and test cohorts.

Conclusion: The CT-based radiomics model showed satisfying diagnostic performance in early-stage LUAD for preoperatively predicting STAS, with superiority over the clinical-CT model.

Keywords: adenocarcinoma; lung cancer; radiomics; spread through air spaces; stage IA.