Application of a Comprehensive Model Based on CT Radiomics and Clinical Features for Postoperative Recurrence Risk Prediction in Non-small Cell Lung Cancer

Peiwen Wang; Zhilin Luo; Chengwen Luo; Tianhu Wang

doi:10.1016/j.acra.2023.11.028

Application of a Comprehensive Model Based on CT Radiomics and Clinical Features for Postoperative Recurrence Risk Prediction in Non-small Cell Lung Cancer

Acad Radiol. 2024 Jan 2:S1076-6332(23)00662-1. doi: 10.1016/j.acra.2023.11.028. Online ahead of print.

Authors

Peiwen Wang¹, Zhilin Luo¹, Chengwen Luo¹, Tianhu Wang²

Affiliations

¹ Department of Thoracic Surgery, Third Affiliated Hospital, Chongqing Medical University, Chongqing 400016, P.R. China.
² Department of Thoracic Surgery, Third Affiliated Hospital, Chongqing Medical University, Chongqing 400016, P.R. China. Electronic address: 650221@hospital.cqmu.edu.cn.

PMID: 38172022
DOI: 10.1016/j.acra.2023.11.028

Abstract

Rationale and objectives: We constructed a comprehensive model by combining the radiomics and clinical features of tumors to predict the recurrence risk of patients with operable stage IA-IIIA non-small cell lung cancer (NSCLC). Our aim was to improve the accuracy of prognostic prediction and provide personalized treatment plans to enhance patient outcomes.

Materials and methods: We retrospectively analyzed 152 surgically treated patients with pathologically confirmed stage IA-IIIA NSCLC. These patients were randomly divided into a training cohort and a test cohort in an 8:2 ratio. Using the 3D Slicer image computing platform, we manually delineated the regions of interest (ROI) for all lesions and extracted radiomics features using Python. We used the Least Absolute Shrinkage and Selection Operator (LASSO) to select the radiomics features, while the COX multivariate regression model was employed to identify independent clinical risk factors for recurrence. Finally, we utilized logistic regression (LR) to build the model and validated it using the receiver operating characteristic curve (ROC). The predictive performance of the model was evaluated using the concordance index (C-index), and the clinical value of the model was compared through decision curve analysis (DCA).

Results: We extracted a total of 1562 radiomics features. After feature selection, we retained 29 features. The COX multivariate regression model demonstrated that the N stage was an independent risk factor for postoperative recurrence. In the training and test cohorts, the area under the curve (AUC) values of the radiomics-clinical comprehensive model were 0.972 and 0.937, respectively, while the C-index values were 0.815 and 0.847. These values surpassed those of the standalone clinical model or radiomics model.

Conclusion: Our study demonstrates that a comprehensive model based on CT radiomics and clinical features can effectively stratify the risk of postoperative recurrence in patients with operable NSCLC. It provides a powerful tool for accurately stratifying the risk of high-risk patients after surgery.

Keywords: Machine learning; Non-small cell lung cancer; Prognosis; Radiomics; Recurrence.