Development and validation of a nomogram for predicting survival of advanced breast cancer patients in China. (October 2020)

• Record Type: Journal Article
• Authors: Lin, Shaoyan; Mo, Hongnan; Li, Yiqun; Guan, Xiuwen; Chen, Yimeng; Wang, Zijing; Yuan, Peng; Wang, Jiayu; Luo, Yang; Fan, Ying; Cai, Ruigang; Li, Qiao; Chen, Shanshan; Zhang, Pin; Li, Qing; Ma, Fei; Xu, Binghe
• Is Part Of: Breast. Volume 53(2020)
• Page Start: 172
• Page End: 180
• Publication Date: 2020-10
• Subjects: Advanced breast cancer -- Nomogram -- Overall survival -- Prognosis -- Prediction; Breast -- Diseases -- Periodicals; Breast -- Tumors -- Periodicals; Breast -- Periodicals; Electronic journals; Periodicals; 616
• DOI: 10.1016/j.breast.2020.08.004
• Languages: English
• ISSNs: 0960-9776

Abstract

Abstract: Background: There is a lack of prognostic models predicting the overall survival (OS) of advanced breast cancer (ABC) patients in China. Methods: Data from the China National Cancer Center database that recorded 4039 patients diagnosed with breast cancer between 1987 and 2019 were extracted and a total of 2263 ABC participants were enrolled in this study, which were further randomized 3:1 and divided into training (n = 1706) and validation (n = 557) groups. The nomogram was built based on independent predictors identified by univariate and multivariate cox regression analyses. The discriminatory and predictive capacities of the nomogram were assessed by Harrell's concordance index (C-index) and calibration plots. Results: Univariate and multivariate analyses found that age, Eastern Cooperative Oncology Group (ECOG) score, T-stage, N-stage, tumor subtype, the presence of distant lymph node (DLN)/liver/brain metastasis, local therapy, efficacy of first-line therapy and metastatic-free interval (MFI) were significantly related to OS (all P < 0.05). These variables were incorporated into a nomogram to predict the 2-year and 3-year OS of ABC patients. The C-indexes of the nomogram were 0.700 (95% confidence interval [CI]: 0.683–0.717) for the training set and 0.686 (95% CI: 0.652–0.719) for the validation set. The calibration curves revealed satisfactory consistency between actual survival and nomogram prediction in both the internal and external validations. The nomogram was capable of stratifying patients into different risk cohorts. Conclusions: We constructed and validated a nomogram that might serve as an efficient tool to provide prognostic prediction for ABC patients and guide the physicians to make personalized treatment decisions. Highlights: The nomogram might serve as an efficient tool to provide prognostic prediction for advanced breast cancer patients. This model could identify low-risk patients who might benefit from aggressive local curative treatment of liver metastasis. The nomogram suggested that the low-risk triple-negative breast cancer patients demanded more intensive treatments. External validation from other centers was needed.