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Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level
BACKGROUND: To determine whether magnetic resonance (MR) imaging features combined with apparent diffusion coefficient (ADC) values could be used as a tool for categorizing ovarian epithelial cancer (OEC) and predicting survival, as well as correlating with laboratory tests (serum cancer antigen 125...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6595619/ https://www.ncbi.nlm.nih.gov/pubmed/31242916 http://dx.doi.org/10.1186/s13048-019-0534-0 |
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author | Zhang, Guofu Yao, Weigen Sun, Taotao Liu, Xuefen Zhang, Peng Jin, Jun Bai, Yu Hua, Keqin Zhang, He |
author_facet | Zhang, Guofu Yao, Weigen Sun, Taotao Liu, Xuefen Zhang, Peng Jin, Jun Bai, Yu Hua, Keqin Zhang, He |
author_sort | Zhang, Guofu |
collection | PubMed |
description | BACKGROUND: To determine whether magnetic resonance (MR) imaging features combined with apparent diffusion coefficient (ADC) values could be used as a tool for categorizing ovarian epithelial cancer (OEC) and predicting survival, as well as correlating with laboratory tests (serum cancer antigen 125, serum CA-125) and tumor proliferative index (Ki-67 expression). METHODS AND MATERIALS: MRI examination was undertaken before invasive procedures. MRI features were interpreted and recorded on the picture archive communication system (PACS). ADC measurements were manually performed on post-process workstation. Clinical characteristics were individually retrieved and recorded through the hospital information system (HIS). Cox hazard model was used to estimate the effects of both clinical and MRI features on overall survival. RESULTS: Both clinical and MRI features differed significantly between Type I and Type II cancer groups (p < 0.05). The mean ADC value was inversely correlated with Ki-67 expression in Type I cancer (ρ = − 0.14, p < 0.05). A higher mean ADC value was more likely to suggest Type I ovarian cancer (Odds Ratio (OR) = 16.80, p < 0.01). Old age and an advanced International Federation of Gynecology and Obstetrics (FIGO) stage were significantly related to Type II ovarian cancer (OR = 0.22/0.02, p < 0.05). An advanced FIGO stage, solid components, and old age were significantly associated with poor survival (Hazard Ratio (HR) = 23.54/3.69/2.46, p < 0.05). Clear cell cancer type had a poorer survival than any other pathological subtypes of ovarian cancer (HR = 13.6, p < 0.01). CONCLUSIONS: MR imaging features combined with ADC value are helpful in categorizing OEC. ADC values can reflect tumor proliferative ability. A solid mass may predict poor prognosis for OEC patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13048-019-0534-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6595619 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65956192019-08-07 Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level Zhang, Guofu Yao, Weigen Sun, Taotao Liu, Xuefen Zhang, Peng Jin, Jun Bai, Yu Hua, Keqin Zhang, He J Ovarian Res Research BACKGROUND: To determine whether magnetic resonance (MR) imaging features combined with apparent diffusion coefficient (ADC) values could be used as a tool for categorizing ovarian epithelial cancer (OEC) and predicting survival, as well as correlating with laboratory tests (serum cancer antigen 125, serum CA-125) and tumor proliferative index (Ki-67 expression). METHODS AND MATERIALS: MRI examination was undertaken before invasive procedures. MRI features were interpreted and recorded on the picture archive communication system (PACS). ADC measurements were manually performed on post-process workstation. Clinical characteristics were individually retrieved and recorded through the hospital information system (HIS). Cox hazard model was used to estimate the effects of both clinical and MRI features on overall survival. RESULTS: Both clinical and MRI features differed significantly between Type I and Type II cancer groups (p < 0.05). The mean ADC value was inversely correlated with Ki-67 expression in Type I cancer (ρ = − 0.14, p < 0.05). A higher mean ADC value was more likely to suggest Type I ovarian cancer (Odds Ratio (OR) = 16.80, p < 0.01). Old age and an advanced International Federation of Gynecology and Obstetrics (FIGO) stage were significantly related to Type II ovarian cancer (OR = 0.22/0.02, p < 0.05). An advanced FIGO stage, solid components, and old age were significantly associated with poor survival (Hazard Ratio (HR) = 23.54/3.69/2.46, p < 0.05). Clear cell cancer type had a poorer survival than any other pathological subtypes of ovarian cancer (HR = 13.6, p < 0.01). CONCLUSIONS: MR imaging features combined with ADC value are helpful in categorizing OEC. ADC values can reflect tumor proliferative ability. A solid mass may predict poor prognosis for OEC patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13048-019-0534-0) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-26 /pmc/articles/PMC6595619/ /pubmed/31242916 http://dx.doi.org/10.1186/s13048-019-0534-0 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Zhang, Guofu Yao, Weigen Sun, Taotao Liu, Xuefen Zhang, Peng Jin, Jun Bai, Yu Hua, Keqin Zhang, He Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level |
title | Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level |
title_full | Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level |
title_fullStr | Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level |
title_full_unstemmed | Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level |
title_short | Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level |
title_sort | magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with ki-67 expression and serum cancer antigen-125 level |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6595619/ https://www.ncbi.nlm.nih.gov/pubmed/31242916 http://dx.doi.org/10.1186/s13048-019-0534-0 |
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