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Construction of a Prognostic Nomogram Model for Patients with Mucinous Breast Cancer

OBJECTIVE: The objective of the study is to develop a nomogram for estimating three- and five-year survival rates in mucinous breast cancer patients. METHODS: Between 2010 and 2016, the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) were searched as a data source...

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Detalles Bibliográficos
Autores principales: Zhu, Xulong, Li, Ying, Liu, Fende, Zhang, Feifei, Li, Jianhui, Cheng, Chong, Shen, Yanwei, Jiang, Nan, Du, Jia, Zhou, Yajing, Huo, Binliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967531/
https://www.ncbi.nlm.nih.gov/pubmed/35368964
http://dx.doi.org/10.1155/2022/1230812
Descripción
Sumario:OBJECTIVE: The objective of the study is to develop a nomogram for estimating three- and five-year survival rates in mucinous breast cancer patients. METHODS: Between 2010 and 2016, the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) were searched as a data source for patients associated with mucinous breast cancer (MBC). A total of 3964 patients were recruited after screening. The multivariate Cox model and the univariate Kaplan-Meier (KM) approach were employed to evaluate the independent prognostic markers, followed by developing a nomogram for estimating three- and five-year survival rates in MBC patients. Consequently, the consistency index (C-index) was employed to assess the predictive accuracy of the generated nomogram. RESULTS: Age, race, T stage, M stage, surgery, and radiotherapy were all independent predictive biomarkers for the MBC patients (P < 0.05). The nomogram was finally developed based on the underlined factors. Furthermore, the C-index of 0.803 and reliable calibration curves were obtained in the nomogram's assessment. CONCLUSIONS: In patients with mucinous breast cancer, the proposed nomogram provides a viable tool for accurate prognostic prediction. In clinical practice, it could serve as a personalized diagnosis tool, estimate prognosis, and help in suggesting treatment plans for patients with MBC.