CT-based radiomics for predicting brain metastases as the first failure in patients with curatively resected locally advanced non-small cell lung cancer

Purpose: Brain metastasis (BM) is the primary first failure pattern in patients with curatively resected locally advanced non-small cell lung cancer (LA-NSCLC). It is not yet possible to accurately predict the occurrence of BM. The purpose of the research is to develop and validate a prediction model of BM-free survival based on radiomics characterising the primary lesions combined with clinical characteristics in patients with curatively resected LA-NSCLC. Methods: This study consisted of 124 patients with curatively resected stage IIB-IIIB NSCLC in our institution between January 2014 and June 2018. Patients were randomly divided into training and validation cohorts using a 4:1 ratio. Radiomics features were selected from the chest CT images before surgery. A radiomics signature was constructed using the LASSO algorithm based on the training cohort. Clinical model was developed using the Cox proportional hazards model. The clinical, radiomics, and integrated nomograms were constructed. The prediction performance of the models was assessed based on its discrimination, calibration, and clinical utility. Results: The radiomics signature is significantly associated with BM-free survival in the overall cohort. The discrimination performance of the integrated nomogram, with the C-indexes 0.889 (0.872-0.906, 95 % CI) and 0.853 (0.788-0.918, 95 % CI) in the training and validation cohorts, respectively, is significantly better than the clinical nomogram (p < 0.0001 for the training cohort, p = 0.0008 for the validation cohort). Compared with the radiomics nomogram, the integrated nomogram is also improved to varying degrees, but not apparent in the validation cohort (p = 0.0007 for the training cohort, p = 0.0554 for the validation cohort). The calibration curve and decision curve analysis demonstrated that the integrated nomogram exceeded the clinical or radiomics nomograms in predicting BM-free survival. Conclusions: Compared with the clinical or radiomics nomograms, the predictive performance of the integrated nomogram is significantly improved. The integrated nomogram is most suitable for predicting BM-free survival in patients with curatively resected LA-NSCLC.

Automated summary

A prediction model based on radiomics features and clinical characteristics was developed to accurately predict brain metastasis-free survival in patients with curatively resected locally advanced non-small cell lung cancer. The integrated nomogram showed significantly improved predictive performance compared to clinical or radiomics nomograms, making it most suitable for predicting brain metastasis-free survival in this patient population.