A nomogram for predicting brain metastases of EGFR-mutated lung adenocarcinoma patients and estimating the efficacy of therapeutic strategies

Background To establish a nomogram for predicting the outcome of EGFR-mutated lung adenocarcinoma patients with brain metastases (BMs) and to estimate the efficacy of different therapeutic strategies. Methods The data of 129 cases with BM from the period between January 1st 2011 and December 31st 2014 were collected, and all of the cases were pathologically confirmed to be lung adenocarcinoma, stages I-IV and with 19 and/or 21 exon mutations of EGFR. Cox regression analysis and log-rank test were used for data analysis. The nomogram was used to establish the progression models. Results In the univariate analysis, the stage, ECOG score, interval between the diagnosis of lung cancer and BM, the number of brain metastatic lesions, and the diameter of the maximal brain metastatic lesion correlated well with overall survival (OS). In multivariate Cox proportional hazard analysis, the ECOG score, interval between the diagnosis of lung cancer and BM, and the number of brain metastatic lesions correlated well with the OS. Patients were divided into the poor prognostic group and the good prognostic group based on the nomogram prognostic model score. Subgroup analysis showed that in the poor prognostic group, the OS of patients who received radiotherapy was better than that of the patients who did not receive radiotherapy as the first-line treatment (30 vs. 19 months, P 0.05). Patients in the good prognostic group who received radiotherapy had a better 3-y OS rate than the patients who received no radiotherapy as the first-line treatment (91.2% vs. 58.1%, P<0.05). The 3-y OS rate was 87.6% in the TKI subgroup and 67.8% in the no TKI group (P<0.05). Conclusions We established an effective nomogram model to predict the progression of EGFR-mutated lung adenocarcinoma patients with BM and the therapeutic effect of the individual treatments. Radiotherapy was beneficial for the patients of both the poor and good prognostic groups, but TKI may be better suited for treating the patients with good prognosis.