Development and internal validation of a clinicopathological and CT radiomics based prediction model for extracranial distant metastasis after radical resection in early-stage non-small cell lung cancer.

Background: Wide use of low-dose chest CT detects more early NSCLC, though recurrence (mostly extracranial distant metastasis) still occurs. This study aimed to establish and validate computed tomography (CT) radiomics models that can be used for predicting the risk of extracranial distant metastasis (EDM) after radical resection in early-stage non-small cell lung cancer (e-NSCLC). Methods: This retrospective study included 312 patients with e-NSCLC who underwent radical surgery at Shandong Cancer Hospital from 2014 to 2019. The cases were randomly divided into a training set (n=200) and a validation set (n=112). The study excluded patients who received neoadjuvant therapy, had interrupted follow-up, or died of non-tumor-related causes. The patients were followed up for an average of 30.6 months after the first postoperative CT examination. This study defined EDM as distant metastasis in other organs other than brain metastasis after surgery, and accounted for 78.6% of all metastatic cases. The region of interest (ROI) was delineated, and the radiomics features were selected by using least absolute shrinkage and selection operator (LASSO) logistic regression models to construct radiomics signatures. The association between the radiomics signature and the occurrence of EDM was also evaluated. A combined nomogram incorporating the selected radiomics and clinical-pathological predictors was constructed using multivariable regression. The concordance index (C-index) was used to evaluate the performance of the model. To determine the clinical effectiveness of nomogram in radiomics, the decision curve analysis (DCA) was performed. Results: A total of 75 patients (24.0%) experienced metastasis, with EDM accounting for 78.6%. In the overall cohort, radiomics signature were significantly associated with the occurrence of the EDM. The discrimination performance of the radiomics nomogram is significantly better than the clinical-pathologic nomogram (P<0.001), with the C-indexes 0.818 [95% confidence interval (CI): 0.780-0.856] and 0.823 (95% CI: 0.769-0.877) in the training and validation cohorts, respectively. Analysis of the calibration and decision curves indicated that the comprehensive nomogram was superior to the clinical-pathological and radiomics nomogram in predicting EDM survival. Conclusions: CT radiomics features have the potential to predict postoperative EDM and may assist in preliminarily identifying patients at high metastatic risk, which could provide a reference for individualized postoperative adjuvant decision-making in e-NSCLC.