The Role of Predictive Model Based on Quantitative Basic Magnetic Resonance Imaging in Differentiating Medulloblastoma from Ependymoma

Nguyen Minh Duc N. M. D., Huynh Quang Huy H. Q. H., Nadarajan C., Keserci B.

ANTICANCER RESEARCH, vol.40, no.5, pp.2975-2980, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 5
  • Publication Date: 2020
  • Doi Number: 10.21873/anticanres.14277
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, EMBASE, Gender Studies Database, MEDLINE, Veterinary Science Database
  • Page Numbers: pp.2975-2980
  • Yıldız Technical University Affiliated: No


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=e(xn)/(1+e(xn)) with predictor x(n) =-8.773+0.012x(1) - 0.032x(2) - 13.228x(3), where x(1) was the T2-weighted signal intensity (SI) of tumor, x(2) the T1WCE SI of tumor, and x(3) 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.