RT Journal Article
SR Electronic
T1 The Role of Predictive Model Based on Quantitative Basic Magnetic Resonance Imaging in Differentiating Medulloblastoma from Ependymoma
JF Anticancer Research
JO Anticancer Res
FD International Institute of Anticancer Research
SP 2975
OP 2980
DO 10.21873/anticanres.14277
VO 40
IS 5
A1 NGUYEN MINH DUC
A1 HUYNH QUANG HUY
A1 CHANDRAN NADARAJAN
A1 BILGIN KESERCI
YR 2020
UL http://ar.iiarjournals.org/content/40/5/2975.abstract
AB Background/Aim: Even though advanced magnetic resonance imaging (MRI) can effectively differentiate between medulloblastoma and ependymoma, it is not readily available throughout the world. This study aimed to investigate the role of simple quantified basic MRI sequences in the differentiation between medulloblastoma and ependymoma in children. Patients and Methods: The institutional review board approved this prospective study. The brain MRI protocol, including sagittal T1-weighted, axial T2-weighted, coronal fluid-attenuated inversion recovery, and axial T1-weighted with contrast enhancement (T1WCE) sequences, was assessed in 26 patients divided into two groups: Medulloblastoma (n=22) and ependymoma (n=4). The quantified region of interest (ROI) values of tumors and their ratios to parenchyma were compared between the two groups. Multivariate logistic regression analysis was utilized to find significant factors influencing the differential diagnosis between the two groups. A generalized estimating equation (GEE) was used to create the predictive model for the discrimination of medulloblastoma from ependymoma. Results: Multivariate logistic regression analysis showed that the T2- and T1WCE-ROI values of tumors and the ratios of T1WCE-ROI values to parenchyma were the most significant factors influencing the diagnosis between these two groups. GEE produced the model: y=exn/(1+exn) with predictor xn=−8.773+0.012x1 − 0.032x2 − 13.228x3, where x1 was the T2-weighted signal intensity (SI) of tumor, x2 the T1WCE SI of tumor, and x3 the T1WCE SI ratio of tumor to parenchyma. The sensitivity, specificity, and area under the curve of the GEE model were 77.3%, 100%, and 92%, respectively. Conclusion: The GEE predictive model can discriminate between medulloblastoma and ependymoma clinically. Further research should be performed to validate these findings.