Bernardo Cacho-Díaz1, Laura Denisse Cuapaténcatl2, Jose Antonio Rodríguez2, Ytel Jazmin Garcilazo-Reyes2, Nancy Reynoso-Noverón3, Oscar Arrieta4. 1. Neuro Oncology Unit, National Cancer Institute, San Fernando 22, Tlalpan, 14080, Mexico City, ZC, Mexico. bernardocacho@doctor.com. 2. Neuro Oncology Unit, National Cancer Institute, San Fernando 22, Tlalpan, 14080, Mexico City, ZC, Mexico. 3. Research Department, National Cancer Institute, Mexico City, Mexico. 4. Thoracic Oncology Unit, National Cancer Institute, Mexico City, Mexico. ogarrieta@gmail.com.
Abstract
PURPOSE: Identification of a high-risk group of brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) could lead to early interventions and probably better prognosis. The objective of the study was to identify this group by generating a multivariable model with recognized and accessible risk factors. METHODS: A retrospective cohort from patients seen at a single center during 2010-2020, was divided into a training (TD) and validation (VD) datasets, associations with BM were measured in the TD with logit, variables significantly associated were used to generate a multivariate model. Model´s performance was measured with the AUC/C-statistic, Akaike information criterion, and Brier score. RESULTS: From 570 patients with NSCLC who met the strict eligibility criteria a TD and VD were randomly assembled, no significant differences were found amid both datasets. Variables associated with BM in the multivariate logit analyses were age [P 0.001, OR 0.96 (95% CI 0.93-0.98)]; mutational status positive [P 0.027, OR 1.96 (95% CI 1.07-3.56); and carcinoembryonic antigen levels [P 0.016, OR 1.001 (95% CI 1.000-1.003). BM were diagnosed in 24% of the whole cohort. Stratification into a high-risk group after simplification of the model, displayed a frequency of BM of 63% (P < 0.001). CONCLUSION: A multivariate model comprising age, carcinoembryonic antigen levels, and mutation status allowed the identification of a truly high-risk group of BM in NSCLC patients.
PURPOSE: Identification of a high-risk group of brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) could lead to early interventions and probably better prognosis. The objective of the study was to identify this group by generating a multivariable model with recognized and accessible risk factors. METHODS: A retrospective cohort from patients seen at a single center during 2010-2020, was divided into a training (TD) and validation (VD) datasets, associations with BM were measured in the TD with logit, variables significantly associated were used to generate a multivariate model. Model´s performance was measured with the AUC/C-statistic, Akaike information criterion, and Brier score. RESULTS: From 570 patients with NSCLC who met the strict eligibility criteria a TD and VD were randomly assembled, no significant differences were found amid both datasets. Variables associated with BM in the multivariate logit analyses were age [P 0.001, OR 0.96 (95% CI 0.93-0.98)]; mutational status positive [P 0.027, OR 1.96 (95% CI 1.07-3.56); and carcinoembryonic antigen levels [P 0.016, OR 1.001 (95% CI 1.000-1.003). BM were diagnosed in 24% of the whole cohort. Stratification into a high-risk group after simplification of the model, displayed a frequency of BM of 63% (P < 0.001). CONCLUSION: A multivariate model comprising age, carcinoembryonic antigen levels, and mutation status allowed the identification of a truly high-risk group of BM in NSCLC patients.