Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model
First author：Cheng Sen,Wu Jiaqi
Corresponding author：Ying Xiaomin,Zhang Yazhuo
essential_information
- Person in charge：Cheng Sen,Wu Jiaqi
- Data Sources： OTHERS
- Number of papers：OTHERS20190800
- Published time：2019-05-20
2019-05-20
Published articles
Journal：Journal of Translational Medicine
Publication type: Research Article
IF(IF): 5.531
PMID: 31109334 Link To Pubmed Full text Download
Background And Purpose
Method
Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set.
Conclusion
The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions.
Main results
Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-β), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively.