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Sci Rep ; 8(1): 10924, 2018 Jul 19.
Artigo em Inglês | MEDLINE | ID: mdl-30026516

RESUMO

The aim of this study was to discriminate the children malignant peripheral neuroblastic tumors (PNTs) from those with benign histotype ganglioneuroma (GN) based on clinical and biological characteristics in all PNTs. Four hundred and seventy-six patients were included in this study, containing 345 patients for model development and 131 patients for external validation. Multivariate logistic regression analysis was conducted to select potentially useful characteristics for discrimination of histopathology. External validation was performed for model evaluation. Compared with the main characteristics of GN (85/345, 24.6%), those of malignant PNTs (260/345, 75.4%) showed significant differences. Multivariate analysis was performed to further find the characteristics linked to histopathology. The results indicated that for the patients younger than 49 months, the primary site of adrenal and thoracic, the level of serum neuron-specific enolase (NSE) > 33 ng/mL, and tumor encasing blood vessels were the extremely important discrimination factors of malignant PNTs. The area under the receiver-operating-characteristic of the discrimination model was 0.96. The accuracy rate, sensitivity and specificity were 93.4%, 96.3% and 83.8%, respectively. Meanwhile, the accuracy rate of the external validation from the 131 patients was 97.0%. Overall, histopathologic type of childhood malignant PNTs can be discriminated based on age, primary site, NSE level and the relationship between primary tumor and blood vessels.


Assuntos
Ganglioneuroblastoma/patologia , Ganglioneuroma/patologia , Neuroblastoma/patologia , Fosfopiruvato Hidratase/sangue , Criança , Pré-Escolar , Feminino , Ganglioneuroblastoma/irrigação sanguínea , Ganglioneuroblastoma/metabolismo , Ganglioneuroma/irrigação sanguínea , Ganglioneuroma/metabolismo , Humanos , Lactente , Modelos Logísticos , Masculino , Análise Multivariada , Neuroblastoma/irrigação sanguínea , Neuroblastoma/metabolismo , Curva ROC , Sensibilidade e Especificidade
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