Zhangheng Huang1, Yuexin Tong1, Huifei Tian2, Chengliang Zhao3. 1. Department of Spine Surgery, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China. 2. School of Stomatology, Wenzhou Medical University, Wenzhou, Zhejiang, China. 3. Department of Spine Surgery, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China. Electronic address: 38221965@qq.com.
Abstract
BACKGROUND: The brain is one of the common metastatic sites of lung adenocarcinoma, and the prognosis associated with brain metastasis is not good. We performed a large data analyses to determine the prognostic factors of lung adenocarcinoma with brain metastases (LABM) and to develop a nomogram to predict its prognosis. METHODS: We conducted a retrospective study of 2879 patients with LABM from the Surveillance, Epidemiology, and End Results database. An X-tile analysis provided the optimal age cutoff point. We used univariate and multivariate Cox regression analyses to determine the independent prognostic factors of LABM. Finally, we established and validated a nomogram to predict the prognosis of LABM. RESULTS: A total of 2879 patients with brain metastases were included in this study. Multivariate Cox regression analysis showed that age, race, sex, T stage, N stage, surgery, chemotherapy, bone metastasis, liver metastasis, and marital status were independent prognostic factors. We constructed a nomogram to predict the prognosis of LABM with the RMS package. Through calibration curves, receiver operating characteristic curves, and decision curve analyses, we found that the nomogram, which predicted the prognosis of LABM, performed well internally. CONCLUSIONS: The nomogram is expected to be a precise and personalized tool for predicting the prognosis of patients with LABM. This nomogram will help clinicians develop more rational and effective treatment strategies.
BACKGROUND: The brain is one of the common metastatic sites of lung adenocarcinoma, and the prognosis associated with brain metastasis is not good. We performed a large data analyses to determine the prognostic factors of lung adenocarcinoma with brain metastases (LABM) and to develop a nomogram to predict its prognosis. METHODS: We conducted a retrospective study of 2879 patients with LABM from the Surveillance, Epidemiology, and End Results database. An X-tile analysis provided the optimal age cutoff point. We used univariate and multivariate Cox regression analyses to determine the independent prognostic factors of LABM. Finally, we established and validated a nomogram to predict the prognosis of LABM. RESULTS: A total of 2879 patients with brain metastases were included in this study. Multivariate Cox regression analysis showed that age, race, sex, T stage, N stage, surgery, chemotherapy, bone metastasis, liver metastasis, and marital status were independent prognostic factors. We constructed a nomogram to predict the prognosis of LABM with the RMS package. Through calibration curves, receiver operating characteristic curves, and decision curve analyses, we found that the nomogram, which predicted the prognosis of LABM, performed well internally. CONCLUSIONS: The nomogram is expected to be a precise and personalized tool for predicting the prognosis of patients with LABM. This nomogram will help clinicians develop more rational and effective treatment strategies.