Brain metastases in newly diagnosed lung cancer: epidemiology and conditional survival

Abstract

Background: The brain serves as the primary site for metastasis in patients with both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The presence of lung cancer with brain metastasis (LCBM) is a debilitating condition associated with considerable morbidity and mortality. The objective of this study was to assess the incidence and survival rates of LCBM in the United States population.

Methods: We analyzed a total of 9,212 patients diagnosed with LCBM between 2010 and 2015, extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Our analysis assessed the incidence, relative survival, and conditional survival (CS) of LCBM. We utilized the Kaplan-Meier method to estimate overall survival and determine CS at year y+x after x years of survival, following the formula CS(y|x) = CS(y+x)/CS(x). Prognostic factor selection was performed using the least absolute shrinkage and selection operator (LASSO) regression approach, and multivariate Cox regression was employed to demonstrate the impact of these predictors on outcomes and construct a CS-based nomogram.

Results: The overall age-adjusted incidence rate of LCBM was 5.82 cases per 100,000, with a slight decline observed during our study period. Patient relative survival showed a continuous decline with increasing age. CS analysis revealed that the 5-year CS rate for patients initially diagnosed with LCBM adjusted from 3% to 13%, 28%, 52%, and 73% over successive years of survival (1-4 years). Identified predictors included age at diagnosis, sex, race, tumor size, tumor grade, surgery, radiotherapy, and chemotherapy. These predictors, along with the CS formula, were employed to develop a CS-based nomogram for real-time prognosis prediction. Calibration curve, area under the time-dependent receiver operating characteristic (ROC) curve, concordance index (c-index), and decision curve analysis (DCA) demonstrated the model's strong predictive capabilities.

Conclusions: This study deepened our understanding of LCBM patients, summarizing their epidemiological characteristics and CS patterns. We successfully developed a novel CS-based nomogram model for dynamic survival estimation, offering real-time and personalized prognostic information that is clinically valuable.

Keywords: Lung cancer with brain metastasis (LCBM); Surveillance, Epidemiology, and End Results (SEER); incidence; nomogram; survival.

Figure 1

Epidemiology and survival of lung cancer with brain metastasis. (A) Incidence of lung cancer with brain metastasis; (B) incidence of lung cancer with brain metastasis stratified by age groups; (C) relative survival of lung cancer with or without brain metastasis; (D) relative survival of lung cancer with brain metastasis stratified by age groups.

Figure 2

Kaplan-Meier method for estimating conditional survival at 5 years after surviving 0–4 years in LCBM patients. Conditional survival curves and their updated survival data adjusted for survived time. LCBM, lung cancer with brain metastasis.

Figure 3

Predictor screening. The LASSO regression model (A) and 10-fold cross-validation technique for predictor selection (B). LASSO, least absolute shrinkage and selection operator.

Figure 4

Multivariate Cox regression forest plot confirmed the prognostic value of selected variables. HR, hazard ratio; CI, confidence interval; SCC, squamous cell carcinoma; SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; RT, radiotherapy; CT, chemotherapy.

Figure 5

Conditional survival nomogram (CS-based nomogram) for predicting 1-, 3- and 5-year OS and 5-year CS for LCBMs. OS, overall survival; CS, conditional survival; LCBM, lung cancer with brain metastasis.

Figure 6

Conditional survival nomogram model evaluation and validation. Calibration plots of the CS-based nomogram for predicting the probability of survival at 1, 3, and 5 years in both training (A) and validation (B) cohorts. Time-dependent ROC curves for assessing the discrimination of the model in both training (C) and validation (D) cohorts. AUC, area under the curve; CS, conditional survival; ROC, receiver operating characteristic.

Figure 7

Decision curve analysis of the CS-based nomogram in both training (A) and validation (B) cohorts. The x-axis represents the percentage of threshold probability, whereas the y-axis represents the net benefit, calculated by adding the true positives and subtracting the false positives. CS, conditional survival.