Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis

Fatmaelzahraa Abdelfattah Denewar, Mitsuru Takeuchi, Misugi Urano, Yuki Kamishima, Tatsuya Kawai, Naoki Takahashi, Moe Takeuchi, Susumu Kobayashi, Junichi Honda, Yuta Shibamoto

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Objective To assess the value of contrast-enhanced MRI, apparent diffusion coefficient (ADC) measurement, and CA-125 measurement for differentiating borderline ovarian tumors (BOTs) from stage I malignant epithelial ovarian tumors (MEOTs). Material and methods This retrospective study included 43 patients with BOTs and 43 patients with stage I MEOTs who underwent contrast-enhanced MRI with DWI and CA-125 analysis. Two radiologists evaluated the MRI findings in consensus. Univariate and multivariate analyses were performed to detect the best predictor variables for MEOTs. Results Mixed cystic/solid and predominantly solid appearances, as well as thickened irregular septa, were more frequent in MEOTs. A papillary architecture and internal branching (PA&IB) pattern was more frequent in BOTs. MEOTs had thicker walls and septa, larger solid components, and higher CA-125 values. The mean ADC value of solid components (ADCmean) and minimum ADC value of whole lesions (ADCmin) were lower in MEOTs. Multivariate analysis revealed that ADCmin and maximum diameter of the solid components were independent indicators of MEOTs with an AUC, sensitivity, and specificity of 0.86, 81%, and 84%, respectively. Conclusion ADCmin and maximum diameter of solid components were useful for differentiating BOTs from MEOTs.

Original languageEnglish (US)
Pages (from-to)116-123
Number of pages8
JournalEuropean Journal of Radiology
Volume91
DOIs
StatePublished - Jun 1 2017

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Logistic Models
Regression Analysis
Carcinoma
Neoplasms
Multivariate Analysis
Diffusion Magnetic Resonance Imaging
Area Under Curve
Retrospective Studies
Sensitivity and Specificity

Keywords

  • Apparent diffusion coefficient value
  • Borderline ovarian tumor
  • Differential diagnosis
  • Magnetic resonance imaging
  • Malignant epithelial ovarian tumor

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis. / Denewar, Fatmaelzahraa Abdelfattah; Takeuchi, Mitsuru; Urano, Misugi; Kamishima, Yuki; Kawai, Tatsuya; Takahashi, Naoki; Takeuchi, Moe; Kobayashi, Susumu; Honda, Junichi; Shibamoto, Yuta.

In: European Journal of Radiology, Vol. 91, 01.06.2017, p. 116-123.

Research output: Contribution to journalArticle

Denewar, FA, Takeuchi, M, Urano, M, Kamishima, Y, Kawai, T, Takahashi, N, Takeuchi, M, Kobayashi, S, Honda, J & Shibamoto, Y 2017, 'Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis', European Journal of Radiology, vol. 91, pp. 116-123. https://doi.org/10.1016/j.ejrad.2017.04.001
Denewar, Fatmaelzahraa Abdelfattah ; Takeuchi, Mitsuru ; Urano, Misugi ; Kamishima, Yuki ; Kawai, Tatsuya ; Takahashi, Naoki ; Takeuchi, Moe ; Kobayashi, Susumu ; Honda, Junichi ; Shibamoto, Yuta. / Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis. In: European Journal of Radiology. 2017 ; Vol. 91. pp. 116-123.
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