Multicenter Development of a Clinical-Molecular Nomogram for Predicting Survival in Lung Cancer Brain Metastasis Patients.

BACKGROUND

Lung cancer brain metastasis (LCBM) accounts for 40-50% of intracranial malignancies, with emerging evidence of alternative metastatic pathways circumventing the blood-brain barrier. Existing prognostic models lack validation in Asian populations and molecular stratification. This multicenter study aimed to develop a clinical nomogram integrating clinicopathological and molecular determinants for personalized LCBM management.

METHODS

Retrospective analysis of 522 surgically treated LCBM patients (2015-2021) from four Chinese institutions was conducted. Patients were randomized 7:3 into training (n=365) and validation (n=157) cohorts. Multivariate Cox regression identified independent prognostic factors, which were incorporated into a nomogram predicting 6-/12-/18-month overall survival (OS). Model performance was assessed via time-dependent ROC curves (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS

The median OS after neurosurgery was 9 months (range: 4-18 months), with 6-, 12-, and 18-month survival rates of 86.2%, 46.7%, and 17.2%, respectively. Independent predictive factors included brain metastasis size ≥5 cm, Leptomeningeal metastasis(LM), EGFR mutation with TKI treatment, and extracranial metastases. The nomogram demonstrated robust discriminative ability and calibration. EGFR-mutant patients receiving postoperative TKIs showed significantly prolonged survival attributable to enhanced blood-brain barrier permeability. Finally, the authors developed a web-based dynamic nomogram for LCBM patients to facilitate clinical implementation.

CONCLUSION

This study establishes a validated prognostic model integrating tumor burden, EGFR mutation status, and metastatic patterns. It demonstrates that EGFR-guided TKI therapy and bone metastasis surveillance critically influence LCBM outcomes. The nomogram provides a quantifiable framework for risk-adapted therapeutic decisions, advancing precision oncology in neuro-oncology practice.