Heng Shen1,2, Gang Deng2, Qianxue Chen3, Jin Qian4. 1. Department of neurosurgery, Suizhou Hospital, Hubei University of Medicine, 60 Longmen Street, Suizhou, 441399, Hubei, China. 2. Department of Neurosurgery, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, 430060, Hubei, China. 3. Department of Neurosurgery, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, 430060, Hubei, China. chenqx666@whu.edu.cn. 4. Department of neurosurgery, Suizhou Hospital, Hubei University of Medicine, 60 Longmen Street, Suizhou, 441399, Hubei, China. Qianjin7601@163.com.
Abstract
BACKGROUND: The prognosis of lung cancer with synchronous brain metastasis (LCBM) is very poor, and patients often die within a short time. However, little is known about the early mortality and related factors in patients with LCBM. METHODS: Patients diagnosed with LCBM between 2010 and 2016 were enrolled from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate logistic regression analysis were used to identify significant independent prognostic factors, which were used to construct nomograms of overall and cancer-specific early death. Then, the prediction ability of the model was verified by receiver operating characteristic (ROC) curve. At last, the clinical application value of the model was tested through decision curve analysis (DCA). RESULTS: A total of 29,902 patients with LCBM were enrolled in this study. Among them, 13,275 (44.4%) patients had early death, and 11,425 (38.2%) cases died of lung cancer. The significant independent risk factors for overall and cancer-specific early death included age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis and marital status, which were used to construct the nomogram. The ROC curve demonstrated good predictive ability and clinical application value. The areas under the curve (AUC) of the training group was 0.793 (95% CI: 0.788-0.799) and 0.794 (95% CI: 0.788-0.799), in the model of overall and cancer-specific early death respectively. And the AUC of the validation group were 0.803 (95% CI: 0.788-0.818) and 0.806 (95% CI: 0.791-0.821), respectively. The calibration plots of the model showed that the predicted early death is consistent with the actual value. The DCA analysis indicated a good clinical application value of this model. CONCLUSIONS: We established a comprehensive nomogram to predict early death in lung cancer patients with synchronous brain metastases. Nomograms may help oncologists develop better treatment strategies, such as clinical trials and hospice care.
BACKGROUND: The prognosis of lung cancer with synchronous brain metastasis (LCBM) is very poor, and patients often die within a short time. However, little is known about the early mortality and related factors in patients with LCBM. METHODS:Patients diagnosed with LCBM between 2010 and 2016 were enrolled from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate logistic regression analysis were used to identify significant independent prognostic factors, which were used to construct nomograms of overall and cancer-specific early death. Then, the prediction ability of the model was verified by receiver operating characteristic (ROC) curve. At last, the clinical application value of the model was tested through decision curve analysis (DCA). RESULTS: A total of 29,902 patients with LCBM were enrolled in this study. Among them, 13,275 (44.4%) patients had early death, and 11,425 (38.2%) cases died of lung cancer. The significant independent risk factors for overall and cancer-specific early death included age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis and marital status, which were used to construct the nomogram. The ROC curve demonstrated good predictive ability and clinical application value. The areas under the curve (AUC) of the training group was 0.793 (95% CI: 0.788-0.799) and 0.794 (95% CI: 0.788-0.799), in the model of overall and cancer-specific early death respectively. And the AUC of the validation group were 0.803 (95% CI: 0.788-0.818) and 0.806 (95% CI: 0.791-0.821), respectively. The calibration plots of the model showed that the predicted early death is consistent with the actual value. The DCA analysis indicated a good clinical application value of this model. CONCLUSIONS: We established a comprehensive nomogram to predict early death in lung cancerpatients with synchronous brain metastases. Nomograms may help oncologists develop better treatment strategies, such as clinical trials and hospice care.
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