国家神经系统疾病临床医学研究中心数据管理与共享系统
Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
A Logistic Regression Model for Detecting the Presence of Malignant Progression in Atypical Meningiomas
第一作者：Zhang Qing
通讯作者：Zhang Jun-Ting
基本信息
- 负责人：Zhang Qing
- 数据来源： OTHERS
- 论文编号：OTHERS20190866
- 发表时间：2019-02-26
2019-02-26
已发表文章
主要研究背景及目的
方法
The clinical, radiologic, and pathologic data of 33 previously Simpson grade I resected (if any) as well as no radiotherapy treated intracranial AMs between January 2008 and December 2015 were reviewed. Immunohistochemical staining for connexin 43 (Cx43) and Ki-67 was performed. Descriptive analysis and univariate and multivariate logistic regression analyses were used to explore independent predictors of MP. A multivariable logistic model was developed to estimate the risk of MP, and its diagnostic value was determined from a receiver operating characteristic curve.
结论
Low Cx43 expression and irregular tumor shape were independent predictors of the presence of MP. The relevant logistic regression model was found to be effective in distinguishing MP-AMs from primary AMs.
主要结果
There were 11 AMs (33.3%) with histopathologically confirmed MP from benign meningiomas. The other 22 (66.7%) were initially diagnosed AMs with no histopathologically confirmed MP during a median 60.5 months (range, 42-126 months) of follow-up. Univariate and multivariate logistic analyses showed that irregular tumor shape (P = 0.010) and low Cx43 expression (P = 0.010) were independent predictors of the presence of MP, and the predicted probability was calculated by the following formula: P = 1/[1+exp.{1.218-(3.202×Shape)+(3.814×Cx43)}]. P > 0.5 for an irregularly shaped (score 1) AM with low Cx43 expression (score 0) indicated a high probability of MP. The sensitivity, specificity, positive predictive value, negative predictive value, and overall predictive accuracy were 63.6, 95.6, 87.5, 84.0, and 84.8%, respectively.