Generation of a Potential Prognostic Matrix for Papillary Thyroid Cancer that Assesses Age, Tumor Size, Transforming Growth Factor-β, and BRAFV600E Mutation

Ping Wang; Yu Lun; Yudong Fu; Fei Wang; Shihua Zhao; Yangyang Wang; Xu Hou
October 2017
Oncology Research & Treatment;Oct2017, Vol. 40 Issue 10, p586
Academic Journal
Background: Papillary thyroid cancer (PTC) is the most common differentiated thyroid cancer and is responsible for 80-90% of thyroid cancer cases. Despite typically excellent prognoses, these subclinical low-risk cancers are often treated aggressively by surgical thyroidectomy. Consequently, the objective of this study was to generate a prognostic matrix to be used prior to PTC intervention. Methods: In this study, 80 PTC patients were assessed. Following adjustment for sex, logistic regression analysis showed that BRAFV600E mutation, transforming growth factor beta (TGF-β) expression, age, and tumor size are risk factors that can affect tumor clinical stage (p < 0.05). Based on the results of this analysis, we generated a matrix that incorporated 4 variables: patient age, tumor size, BRAFV600E mutation, and TGF-β expression. Results: We observed that the corresponding area under curve was as high as 0.91. The sensitivity and specificity of the model were 94.74 and 83.61%, respectively. These values are significantly higher than those generated from single indexes. Conclusion: As a result of this analysis, it is hoped that the resultant matrix can be utilized during clinical diagnosis and treatment prior to thyroid nodule surgery.


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