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Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors
OBJECTIVE: To evaluate the diagnostic utility of conventional magnetic resonance imaging (MRI)-based characteristics and a texture analysis (TA) for discriminating between ovarian thecoma-fibroma groups (OTFGs) and ovarian granulosa cell tumors (OGCTs). METHODS: This retrospective multicenter study...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131674/ https://www.ncbi.nlm.nih.gov/pubmed/35610706 http://dx.doi.org/10.1186/s13048-022-00989-z |
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author | Nagawa, Keita Kishigami, Tomoki Yokoyama, Fumitaka Murakami, Sho Yasugi, Toshiharu Takaki, Yasunobu Inoue, Kaiji Tsuchihashi, Saki Seki, Satoshi Okada, Yoshitaka Baba, Yasutaka Hasegawa, Kosei Yasuda, Masanori Kozawa, Eito |
author_facet | Nagawa, Keita Kishigami, Tomoki Yokoyama, Fumitaka Murakami, Sho Yasugi, Toshiharu Takaki, Yasunobu Inoue, Kaiji Tsuchihashi, Saki Seki, Satoshi Okada, Yoshitaka Baba, Yasutaka Hasegawa, Kosei Yasuda, Masanori Kozawa, Eito |
author_sort | Nagawa, Keita |
collection | PubMed |
description | OBJECTIVE: To evaluate the diagnostic utility of conventional magnetic resonance imaging (MRI)-based characteristics and a texture analysis (TA) for discriminating between ovarian thecoma-fibroma groups (OTFGs) and ovarian granulosa cell tumors (OGCTs). METHODS: This retrospective multicenter study enrolled 52 patients with 32 OGCTs and 21 OTFGs, which were dissected and pathologically diagnosed between January 2008 and December 2019. MRI-based features (MBFs) and texture features (TFs) were evaluated and compared between OTFGs and OGCTs. A least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select features and construct the discriminating model. ROC analyses were conducted on MBFs, TFs, and their combination to discriminate between the two diseases. RESULTS: We selected 3 features with the highest absolute value of the LASSO regression coefficient for each model: the apparent diffusion coefficient (ADC), peripheral cystic area, and contrast enhancement in the venous phase (VCE) for the MRI-based model; the 10th percentile, difference variance, and maximal correlation coefficient for the TA-based model; and ADC, VCE, and the difference variance for the combination model. The areas under the curves of the constructed models were 0.938, 0.817, and 0.941, respectively. The diagnostic performance of the MRI-based and combination models was similar (p = 0.38), but significantly better than that of the TA-based model (p < 0.05). CONCLUSIONS: The conventional MRI-based analysis has potential as a method to differentiate OTFGs from OGCTs. TA did not appear to be of any additional benefit. Further studies are needed on the use of these methods for a preoperative differential diagnosis of these two diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-022-00989-z. |
format | Online Article Text |
id | pubmed-9131674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91316742022-05-26 Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors Nagawa, Keita Kishigami, Tomoki Yokoyama, Fumitaka Murakami, Sho Yasugi, Toshiharu Takaki, Yasunobu Inoue, Kaiji Tsuchihashi, Saki Seki, Satoshi Okada, Yoshitaka Baba, Yasutaka Hasegawa, Kosei Yasuda, Masanori Kozawa, Eito J Ovarian Res Research OBJECTIVE: To evaluate the diagnostic utility of conventional magnetic resonance imaging (MRI)-based characteristics and a texture analysis (TA) for discriminating between ovarian thecoma-fibroma groups (OTFGs) and ovarian granulosa cell tumors (OGCTs). METHODS: This retrospective multicenter study enrolled 52 patients with 32 OGCTs and 21 OTFGs, which were dissected and pathologically diagnosed between January 2008 and December 2019. MRI-based features (MBFs) and texture features (TFs) were evaluated and compared between OTFGs and OGCTs. A least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select features and construct the discriminating model. ROC analyses were conducted on MBFs, TFs, and their combination to discriminate between the two diseases. RESULTS: We selected 3 features with the highest absolute value of the LASSO regression coefficient for each model: the apparent diffusion coefficient (ADC), peripheral cystic area, and contrast enhancement in the venous phase (VCE) for the MRI-based model; the 10th percentile, difference variance, and maximal correlation coefficient for the TA-based model; and ADC, VCE, and the difference variance for the combination model. The areas under the curves of the constructed models were 0.938, 0.817, and 0.941, respectively. The diagnostic performance of the MRI-based and combination models was similar (p = 0.38), but significantly better than that of the TA-based model (p < 0.05). CONCLUSIONS: The conventional MRI-based analysis has potential as a method to differentiate OTFGs from OGCTs. TA did not appear to be of any additional benefit. Further studies are needed on the use of these methods for a preoperative differential diagnosis of these two diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-022-00989-z. BioMed Central 2022-05-25 /pmc/articles/PMC9131674/ /pubmed/35610706 http://dx.doi.org/10.1186/s13048-022-00989-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Nagawa, Keita Kishigami, Tomoki Yokoyama, Fumitaka Murakami, Sho Yasugi, Toshiharu Takaki, Yasunobu Inoue, Kaiji Tsuchihashi, Saki Seki, Satoshi Okada, Yoshitaka Baba, Yasutaka Hasegawa, Kosei Yasuda, Masanori Kozawa, Eito Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors |
title | Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors |
title_full | Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors |
title_fullStr | Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors |
title_full_unstemmed | Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors |
title_short | Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors |
title_sort | diagnostic utility of a conventional mri-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131674/ https://www.ncbi.nlm.nih.gov/pubmed/35610706 http://dx.doi.org/10.1186/s13048-022-00989-z |
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