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Bo J, Sun M, Wei C, Wei L, Fu B, Shi B, Fang X, Dong J. MRI combined with clinical features to differentiate ovarian thecoma-fibroma with cystic degeneration from ovary adenofibroma. Br J Radiol 2024; 97:1057-1065. [PMID: 38402483 DOI: 10.1093/bjr/tqae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 01/11/2024] [Accepted: 02/21/2024] [Indexed: 02/26/2024] Open
Abstract
OBJECTIVE To explore the value of magnetic resonance imaging (MRI) and clinical features in identifying ovarian thecoma-fibroma (OTF) with cystic degeneration and ovary adenofibroma (OAF). METHODS A total of 40 patients with OTF (OTF group) and 28 patients with OAF (OAF group) were included in this retrospective study. Univariable and multivariable analyses were performed on clinical features and MRI between the two groups, and the receiver operating characteristic (ROC) curve was plotted to estimate the optimal threshold and predictive performance. RESULTS The OTF group had smaller cyst degeneration degree (P < .001), fewer black sponge sign (20% vs. 53.6%, P = .004), lower minimum apparent diffusion coefficient value (ADCmin) (0.986 (0.152) vs. 1.255 (0.370), P < .001), higher age (57.4 ± 14.2 vs. 44.1 ± 15.9, P = .001) and more postmenopausal women (72.5% vs. 28.6%, P < .001) than OAF. The area under the curve of MRI, clinical features and MRI combined with clinical features was 0.870, 0.841, and 0.954, respectively, and MRI combined with clinical features was significantly higher than the other two (P < .05). CONCLUSION The cyst degeneration degree, black sponge sign, ADCmin, age and menopause were independent factors in identifying OTF with cystic degeneration and OAF. The combination of MRI and clinical features has a good effect on the identification of the two. ADVANCES IN KNOWLEDGE This is the first time to distinguish OTF with cystic degeneration from OAF by combining MRI and clinical features. It shows the diagnostic performance of MRI, clinical features, and combination of the two. This will facilitate the discriminability and awareness of these two diseases among radiologists and gynaecologists.
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Affiliation(s)
- Juan Bo
- Department of Radiology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui 230001, China
| | - Mingjie Sun
- Faculty of Graduate Studies, Wannan Medical College, Wuhu, Anhui 241002, China
| | - Chao Wei
- Department of Radiology, Western District, First Affiliated Hospital of University of Science and Technology of China, No.107 Huanhu East Road, Shushan District, Hefei, Anhui, 230031, China
| | - Longyu Wei
- Faculty of Graduate Studies, Bengbu Medical College, Bengbu, Anhui 233030, China
| | - Baoyue Fu
- Faculty of Graduate Studies, Bengbu Medical College, Bengbu, Anhui 233030, China
| | - Bin Shi
- Department of Radiology, Western District, First Affiliated Hospital of University of Science and Technology of China, No.107 Huanhu East Road, Shushan District, Hefei, Anhui, 230031, China
| | - Xin Fang
- Department of Radiology, Western District, First Affiliated Hospital of University of Science and Technology of China, No.107 Huanhu East Road, Shushan District, Hefei, Anhui, 230031, China
| | - Jiangning Dong
- Department of Radiology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, Anhui 230001, China
- Department of Radiology, Western District, First Affiliated Hospital of University of Science and Technology of China, No.107 Huanhu East Road, Shushan District, Hefei, Anhui, 230031, China
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Cui L, Xu H, Zhang Y. Diagnostic Accuracies of the Ultrasound and Magnetic Resonance Imaging ADNEX Scoring Systems For Ovarian Adnexal Mass: Systematic Review and Meta-Analysis. Acad Radiol 2022; 29:897-908. [PMID: 34217614 DOI: 10.1016/j.acra.2021.05.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 11/01/2022]
Abstract
We conducted a meta-analysis of IOTA (international ovarian tumor analysis) ADNEX (Assessment of Different NEoplasias in the adneXa) as ultrasound system and MRI (magnetic resonance imaging) ADNEX scoring systems as MR system to assess their diagnostic test accuracy for differentiating benign from malignant adnexal masses of the ovary. We performed an electronic search for relevant publications in the English language up to February 2021 using PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science, and Google scholar databases and search engines. We computed the pooled sensitivity, pooled specificity, and summary receiver operating characteristics curve (SROC) using the statistical software STATA (Version 13, College Station, TX, StataCorp LP). Based on 11 studies using IOTA-ADNEX, we observed pooled sensitivity, specificity, area under curve, and diagnostic odds ratio were 96% (95% CI, 94% to 97%), 79% (95% CI, 70% to 86 %), 97% (95% CI, 95% to 98%), and 88 (95% CI, 43 to 180). Based on five studies using MR-ADNEX scoring system the pooled sensitivity, specificity, area under curve and diagnostic odds ratio were 91 % (95% CI, 87% to 94 %), 95% (95% CI, 92% to 97 %), 98% (95% CI, 96% to 99%), and 189 (95% CI, 90 to 396) respectively. Our meta-analysis results demonstrate that the MR-ADNEX scoring system had higher specificity however bit lower sensitivity compared to the IOTA-ADNEX scoring system for discriminating benign from malignant ovarian tumors.
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Nagawa K, Kishigami T, Yokoyama F, Murakami S, Yasugi T, Takaki Y, Inoue K, Tsuchihashi S, Seki S, Okada Y, Baba Y, Hasegawa K, Yasuda M, Kozawa E. Diagnostic utility of a conventional MRI-based analysis and texture analysis for discriminating between ovarian thecoma-fibroma groups and ovarian granulosa cell tumors. J Ovarian Res 2022; 15:65. [PMID: 35610706 PMCID: PMC9131674 DOI: 10.1186/s13048-022-00989-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
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.
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Affiliation(s)
- Keita Nagawa
- Department of Radiology, Saitama Medical University, 38 Morohongou, Moroyama-machi, Iruma-gun, Saitama, Japan.
| | - Tomoki Kishigami
- Department of Radiology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, Japan
| | - Fumitaka Yokoyama
- Department of Radiology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, Japan
| | - Sho Murakami
- Department of Radiology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, Japan
| | - Toshiharu Yasugi
- Department of Gynecology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, Japan
| | - Yasunobu Takaki
- Department of Radiology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, Japan
| | - Kaiji Inoue
- Department of Radiology, Saitama Medical University, 38 Morohongou, Moroyama-machi, Iruma-gun, Saitama, Japan
| | - Saki Tsuchihashi
- Department of Radiology, Saitama Medical University, 38 Morohongou, Moroyama-machi, Iruma-gun, Saitama, Japan
| | - Satoshi Seki
- Department of Radiology, Saitama Medical University, 38 Morohongou, Moroyama-machi, Iruma-gun, Saitama, Japan
| | - Yoshitaka Okada
- Department of Diagnostic Imaging, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka city, Saitama, Japan
| | - Yasutaka Baba
- Department of Diagnostic Imaging, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka city, Saitama, Japan
| | - Kosei Hasegawa
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka city, Saitama, Japan
| | - Masanori Yasuda
- Department of Diagnostic Pathology, Saitama Medical University International Medical Center, 1397-1 Yamane, Hidaka city, Saitama, Japan
| | - Eito Kozawa
- Department of Radiology, Saitama Medical University, 38 Morohongou, Moroyama-machi, Iruma-gun, Saitama, Japan
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Li NY, Shi B, Chen YL, Wang PP, Wang CB, Chen Y, Ge YQ, Dong JN, Wei C. The Value of MRI Findings Combined With Texture Analysis in the Differential Diagnosis of Primary Ovarian Granulosa Cell Tumors and Ovarian Thecoma-Fibrothecoma. Front Oncol 2021; 11:758036. [PMID: 34778075 PMCID: PMC8578857 DOI: 10.3389/fonc.2021.758036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Objective This study aims to explore the value of magnetic resonance imaging (MRI) and texture analysis (TA) in the differential diagnosis of ovarian granulosa cell tumors (OGCTs) and thecoma-fibrothecoma (OTCA–FTCA). Methods The preoperative MRI data of 32 patients with OTCA–FTCA and 14 patients with OGCTs, confirmed by pathological examination between June 2013 and August 2020, were retrospectively analyzed. The texture data of three-dimensional MRI scans based on T2-weighted imaging and clinical and conventional MRI features were analyzed and compared between tumor types. The Mann–Whitney U-test, χ2 test/Fisher exact test, and multivariate logistic regression analysis were used to identify differences between the OTCA–FTCA and OGCTs groups. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic curve analysis was carried out to evaluate diagnostic efficiency. Results A multivariate analysis of the imaging-based features combined with TA revealed that intratumoral hemorrhage (OR = 0.037), log-sigma-20mm-3D_glszm_SmallAreaEmphasis (OR = 4.40), and log-sigma-2-0mm-3D_glszm_SmallAreaHighGrayLevelEmphasis (OR = 1.034) were independent features for discriminating between OGCTs and OTCA–FTCA (P < 0.05). An imaging-based diagnosis model, TA-based model, and combination model were established. The areas under the curve of the three models in predicting OGCTs and OTCA–FTCA were 0.935, 0.944, and 0.969, respectively; the sensitivities were 93.75, 93.75, and 96.87%, respectively; and the specificities were 85.71, 92.86, and 92.86%, respectively. The DeLong test indicated that the combination model had the highest predictive efficiency (P < 0.05), with no significant difference among the three models in differentiating between OGCTs and OTCA–FTCA (P > 0.05). Conclusions Compared with OTCA–FTCA, intratumoral hemorrhage may be characteristic MR imaging features with OGCTs. Texture features can reflect the microheterogeneity of OGCTs and OTCA–FTCA. MRI signs and texture features can help differentiate between OGCTs and OTCA–FTCA and provide a more comprehensive and accurate basis for clinical treatment.
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Affiliation(s)
- Nai-Yu Li
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Bin Shi
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yu-Lan Chen
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Pei-Pei Wang
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Chuan-Bin Wang
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yao Chen
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ya-Qiong Ge
- Department of the Healthcare, GE of China, Shanghai, China
| | - Jiang-Ning Dong
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Chao Wei
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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