Computed tomography‐based radiomics and clinical‐genetic features for brain metastasis prediction in patients with stage III/IV epidermal growth factor receptor‐mutant non‐small‐cell lung cancer

Author:

Zheng Mei1,Sun Xiaorong2,Qi Haoran1,Zhang Mingzhu3,Xing Ligang1ORCID

Affiliation:

1. Department of Radiation Oncology Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences Jinan China

2. Department of Nuclear Medicine Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences Jinan China

3. Cheeloo College of Medicine Shandong University Jinan China

Abstract

AbstractPurposeTo evaluate the value of computed tomography (CT)‐based radiomics combined with clinical‐genetic features in predicting brain metastasis in patients with stage III/IV epidermal growth factor receptor (EGFR)‐mutant non‐small‐cell lung cancer (NSCLC).MethodsThe study included 147 eligible patients treated at our institution between January 2018 and May 2021. Patients were randomly divided into two cohorts for model training (n = 102) and validation (n = 45). Radiomics features were extracted from the chest CT images before treatment, and a radiomics signature was constructed using the Least Absolute Shrinkage and Selection Operator regression. Kaplan–Meier survival analysis was used to describe the differences in brain metastasis‐free survival (BM‐FS) risk. A clinical‐genetic model was developed using Cox regression analysis. Radiomics, genetic, and combined prediction models were constructed, and their predictive performances were evaluated by the concordance index (C‐index).ResultsPatients with a low radiomics score had significantly longer BM‐FS than those with a high radiomics score in both the training (p < 0.0001) and the validation (p = 0.0016) cohorts. The C‐indices of the nomogram, which combined the radiomics signature and N stage, overall stage, third‐generation tyrosine kinase inhibitor treatment, and EGFR mutation status, were 0.886 (95% confidence interval [CI] 0.823–0.949) and 0.811 (95% CI 0.719–0.903) in the training and validation cohorts, respectively. The combined model achieved a higher discrimination and clinical utility than the single prediction models.ConclusionsThe combined radiomics‐genetic model could be used to predict BM‐FS in stage III/IV NSCLC patients with EGFR mutations.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Department of Science and Technology of Shandong Province

Publisher

Wiley

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