Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT

Ningxin Chen; Ruikun Li; Mengmeng Jiang; Yixian Guo; Jiejun Chen; Dazhen Sun; Lisheng Wang; Xiuzhong Yao

doi:10.3389/fmed.2022.833283

Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT

Front Med (Lausanne). 2022 Feb 24:9:833283. doi: 10.3389/fmed.2022.833283. eCollection 2022.

Authors

Ningxin Chen¹, Ruikun Li², Mengmeng Jiang³, Yixian Guo³, Jiejun Chen³, Dazhen Sun², Lisheng Wang², Xiuzhong Yao³

Affiliations

¹ Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
² Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China.

Abstract

Purposes and objectives: The aim of this study was to predict the progression-free survival (PFS) in patients with small cell lung cancer (SCLC) by radiomic signature from the contrast-enhanced computed tomography (CT).

Methods: A total of 186 cases with pathological confirmed small cell lung cancer were retrospectively assembled. First, 1,218 radiomic features were automatically extracted from tumor region of interests (ROIs) on the lung window and mediastinal window, respectively. Then, the prognostic and robust features were selected by machine learning methods, such as (1) univariate analysis based on a Cox proportional hazard (CPH) model, (2) redundancy removing using the variance inflation factor (VIF), and (3) multivariate importance analysis based on random survival forests (RSF). Finally, PFS predictive models were established based on RSF, and their performances were evaluated using the concordance index (C-index) and the cumulative/dynamic area under the curve (C/D AUC).

Results: In total, 11 radiomic features (6 for mediastinal window and 5 for lung window) were finally selected, and the predictive model constructed from them achieved a C-index of 0.7531 and a mean C/D AUC of 0.8487 on the independent test set, better than the predictions by single clinical features (C-index = 0.6026, mean C/D AUC = 0.6312), and single radiomic features computed in lung window (C-index = 0.6951, mean C/D AUC = 0.7836) or mediastinal window (C-index = 0.7192, mean C/D AUC = 0.7964).

Conclusion: The radiomic features computed from tumor ROIs on both lung window and mediastinal window can predict the PFS for patients with SCLC by a high accuracy, which could be used as a useful tool to support the personalized clinical decision for the diagnosis and patient management of patients with SCLC.

Keywords: contrast-enhanced CT; mediastinal window; progression-free survival (PFS); radiomics analysis; small cell lung cancer (SCLC).