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Clinical nomogram for predicting the survival of patients with cerebral anaplastic gliomas

The present study aimed to develop an effective nomogram for predicting the overall survival (OS) of patients with cerebral anaplastic glioma (AG). This study included 1939 patients diagnosed with AG between 1973 and 2013 who were identified using the Surveillance, Epidemiology, and End Results data...

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Detalles Bibliográficos
Autores principales: Zhao, Ye-Yu, Wan, Qin-Si, Hao, Zheng, Zhu, Hua-Xin, Xing, Ze-Long, Li, Mei-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7478695/
https://www.ncbi.nlm.nih.gov/pubmed/32150092
http://dx.doi.org/10.1097/MD.0000000000019416
Descripción
Sumario:The present study aimed to develop an effective nomogram for predicting the overall survival (OS) of patients with cerebral anaplastic glioma (AG). This study included 1939 patients diagnosed with AG between 1973 and 2013 who were identified using the Surveillance, Epidemiology, and End Results database. A multivariate Cox regression analysis revealed that age, histology, tumor site, marital status, radiotherapy, and surgery were independent prognostic factors and, thus, these factors were selected to build a clinical nomogram. Harrell's concordance index (C-index) and a calibration curve were formulated to evaluate the discrimination and calibration of the nomogram using bootstrapping. A nomogram was developed to predict 5- and 9-year OS rates based on 6 independent prognostic factors identified in the training set: age, tumor site, marital status, histology, radiotherapy, and surgery (P < .05). The Harrell's concordance index values of the training and validation sets were 0.776 (0.759–0.793) and 0.766 (0.739–0.792), respectively. The calibration curve exhibited good consistency with the actual observation curve in both sets. Although the prognostic value of the World Health Organization (WHO) classification has been validated, we developed a novel nomogram based on readily available clinical variables in terms of demographic data, therapeutic modalities, and tumor characteristics to predict the survival of AG patients. When used in combination with the WHO classification system, this clinical nomogram can aid clinicians in making individualized predictions of AG patient survival and improving treatment strategies.