A predictive diagnostic model using multiparametric MRI for differentiating uterine carcinosarcoma from carcinoma of the uterine corpus View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-08

AUTHORS

Yuki Kamishima, Mitsuru Takeuchi, Tatsuya Kawai, Takatsune Kawaguchi, Ken Yamaguchi, Naoki Takahashi, Masato Ito, Toshinao Arakawa, Akiko Yamamoto, Kazushi Suzuki, Masaki Ogawa, Moe Takeuchi, Yuta Shibamoto

ABSTRACT

PURPOSE: To construct a diagnostic model for differentiating carcinosarcoma from carcinoma of the uterus. MATERIALS AND METHODS: Twenty-six patients with carcinosarcomas and 26 with uterine corpus carcinomas constituted a derivation cohort. The following nine MRI features of the tumors were evaluated: inhomogeneity, predominant signal intensity, presence of hyper- and hypointense areas, conspicuity of tumor margin, cervical canal extension on T2WI, presence of hyperintense areas on T1WI, contrast defect area volume percentage, and degree of enhancement. Two predictive models-with and without contrast-were constructed using multivariate logistic regression analysis. Fifteen other patients with carcinosarcomas and 30 patients with carcinomas constituted a validation cohort. The sensitivity and specificity of each model for the validation cohort were calculated. RESULTS: Inhomogeneity, predominant signal intensity on T2WI, and presence of hyperintense areas on T1WI were significant predictors in the unenhanced-MRI-based model. Presence of hyperintensity on T1WI, contrast defect area volume percentage, and degree of enhancement were significant predictors in the enhanced-MRI-based model. The sensitivity/specificity of unenhanced MRI were 87/73 and 87/70% according to reviewer 1 and 2, respectively. The sensitivity/specificity of the enhanced-MRI-based model were 87/70% according to both reviewers. CONCLUSIONS: Our diagnostic models can differentiate carcinosarcoma from carcinoma of the uterus with high sensitivity and moderate specificity. More... »

PAGES

472-483

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11604-017-0655-6

DOI

http://dx.doi.org/10.1007/s11604-017-0655-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085863531

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28584958


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39 schema:description PURPOSE: To construct a diagnostic model for differentiating carcinosarcoma from carcinoma of the uterus. MATERIALS AND METHODS: Twenty-six patients with carcinosarcomas and 26 with uterine corpus carcinomas constituted a derivation cohort. The following nine MRI features of the tumors were evaluated: inhomogeneity, predominant signal intensity, presence of hyper- and hypointense areas, conspicuity of tumor margin, cervical canal extension on T2WI, presence of hyperintense areas on T1WI, contrast defect area volume percentage, and degree of enhancement. Two predictive models-with and without contrast-were constructed using multivariate logistic regression analysis. Fifteen other patients with carcinosarcomas and 30 patients with carcinomas constituted a validation cohort. The sensitivity and specificity of each model for the validation cohort were calculated. RESULTS: Inhomogeneity, predominant signal intensity on T2WI, and presence of hyperintense areas on T1WI were significant predictors in the unenhanced-MRI-based model. Presence of hyperintensity on T1WI, contrast defect area volume percentage, and degree of enhancement were significant predictors in the enhanced-MRI-based model. The sensitivity/specificity of unenhanced MRI were 87/73 and 87/70% according to reviewer 1 and 2, respectively. The sensitivity/specificity of the enhanced-MRI-based model were 87/70% according to both reviewers. CONCLUSIONS: Our diagnostic models can differentiate carcinosarcoma from carcinoma of the uterus with high sensitivity and moderate specificity.
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