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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Objective

We aimed to differentiate granulosa cell tumors (GCT) from other ovarian sex-cord tumors (OSCs) based on feature analysis of the tumor body on MR imaging.

Methods

We retrospectively enrolled 27 patients with pathologically proven sex-cord tumours (14 GSTs, 8 fibromas, 4 fibrothecomas, and 1 sclerosing stromal tumour) from our institution. All MRI examinations were performed at least one month prior to surgery. MR image features were recorded by two radiologists with consensus readings. Histogram analysis was performed using FeAture Explorer software. The differences in histogram parameters between GCT (38.1 ± 14.6 years) and OSC (43.7 ± 18.0 years) groups were compared. Fourteen randomly selected cellular-type myomas who also underwent MRI in our hospital were considered as the control group. The intra-operator consistency of ADC value was evaluated across measurements twice.

Results

The repeatability of conventional ADC measurements on the tumor body was good. The values of ADC-mean, ADC-min, and ADC-max significantly differed across three groups (p < 0.001). The histogram variance on DWI, histogram percentage on T2WI, and ADC min showed the best discriminative performance in determining GCTs from other OSCs with an area under the receiver operator curve (AUC) of 0.997, 0.882, and 0.795, respectively. The histogram variance on DWI yielded a sensitivity of 92.3%, a specificity of 100%, and an accuracy of 96.6% in discriminating GSTs from other OSCs.

Conclusion

In the present study, feature analysis of tumor body MR imaging has helped to differentiate GST from OSC with better performance than conventional ADC measurements.

© 2024 Wu et al.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-09-09

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References

  1. ChenV.W. RuizB. KilleenJ.L. CotéT.R. WuX.C. CorreaC.N. Pathology and classification of ovarian tumors.Cancer200397102631264210.1002/cncr.1134512733128
     [Google Scholar]
  2. LevinG. ZigronR. Haj-YahyaR. MatanL.S. RottenstreichA. Granulosa cell tumor of ovary: A systematic review of recent evidence.Eur. J. Obstet. Gynecol. Reprod. Biol.2018225576110.1016/j.ejogrb.2018.04.00229665458
     [Google Scholar]
  3. LauszusF.F. PetersenA.C. GreisenJ. JakobsenA. Granulosa cell tumor of the ovary: A population-based study of 37 women with stage I disease.Gynecol. Oncol.200181345646010.1006/gyno.2001.618311371138
     [Google Scholar]
  4. ShinagareA.B. MeylaertsL.J. LauryA.R. MorteleK.J. MRI features of ovarian fibroma and fibrothecoma with histopathologic correlation.AJR Am. J. Roentgenol.20121983W296W30310.2214/AJR.11.722122358029
     [Google Scholar]
  5. RajkotiaK. VeeramaniM. MacuraK.J. Magnetic resonance imaging of adnexal masses.Top. Magn. Reson. Imaging200617637939710.1097/RMR.0b013e3180417d8e17417086
     [Google Scholar]
  6. Thomassin-NaggaraI. PonceletE. Jalaguier-CoudrayA. GuerraA. FournierL.S. StojanovicS. MilletI. BharwaniN. JuhanV. CunhaT.M. MasselliG. BalleyguierC. MalhaireC. PerrotN.F. SadowskiE.A. BazotM. TaourelP. PorcherR. DaraiE. ReinholdC. RockallA.G. Ovarian-Adnexal Reporting Data System Magnetic Resonance Imaging (O-RADS MRI) score for risk stratification of sonographically indeterminate adnexal masses.JAMA Netw. Open202031e1919896e191989610.1001/jamanetworkopen.2019.1989631977064
     [Google Scholar]
  7. ZhaoS.H. QiangJ.W. ZhangG.F. MaF.H. CaiS.Q. LiH.M. WangL. Diffusion-weighted MR imaging for differentiating borderline from malignant epithelial tumours of the ovary: Pathological correlation.Eur. Radiol.20142492292229910.1007/s00330‑014‑3236‑424871335
     [Google Scholar]
  8. LiH.M. ZhaoS.H. QiangJ.W. ZhangG.F. FengF. MaF.H. LiY.A. GuW.Y. Diffusion kurtosis imaging for differentiating borderline from malignant epithelial ovarian tumors: A correlation with Ki‐67 expression.J. Magn. Reson. Imaging20174651499150610.1002/jmri.2569628295854
     [Google Scholar]
  9. ZhangH. ZhangG.F. HeZ.Y. LiZ.Y. ZhangG.X. Prospective evaluation of 3T MRI findings for primary adnexal lesions and comparison with the final histological diagnosis.Arch. Gynecol. Obstet.2014289235736410.1007/s00404‑013‑2990‑x23934242
     [Google Scholar]
  10. JavadiS. GaneshanD.M. QayyumA. IyerR.B. BhosaleP. Ovarian cancer, the revised FIGO staging system, and the role of imaging.AJR Am. J. Roentgenol.201620661351136010.2214/AJR.15.1519927042752
     [Google Scholar]
  11. JavadiS. GaneshanD.M. JensenC.T. IyerR.B. BhosaleP.R. Comprehensive review of imaging features of sex cord-stromal tumors of the ovary.Abdom. Radiol.20214641519152910.1007/s00261‑021‑02998‑w33725145
     [Google Scholar]
  12. WeiC. ChenY. LiX. LiN. WuY. LinT. WangC. ZhangP. DongJ. YuY. Diagnostic performance of MR imaging-based features and texture analysis in the differential diagnosis of ovarian thecomas/fibrothecomas and uterine fibroids in the adnexal area.Acad. Radiol.202027101406141510.1016/j.acra.2019.12.02532035760
     [Google Scholar]
  13. ZhangH. ZhangH. GuS. ZhangY. LiuX. ZhangG. MR findings of primary ovarian granulosa cell tumor with focus on the differentiation with other ovarian sex cord-stromal tumors.J. Ovarian Res.20181114610.1186/s13048‑018‑0416‑x29871662
     [Google Scholar]
  14. TaylorE.C. IrshaidL. MathurM. Multimodality imaging approach to ovarian neoplasms with pathologic correlation.Radiographics202141128931510.1148/rg.202120008633186060
     [Google Scholar]
  15. GergesL. PopiolekD. RosenkrantzA.B. Explorative investigation of whole-lesion histogram MRI metrics for differentiating uterine leiomyomas and leiomyosarcomas.AJR Am. J. Roentgenol.201821051172117710.2214/AJR.17.1860529547053
     [Google Scholar]
  16. NougaretS. TardieuM. VargasH.A. Ovarian cancer: An update on imaging in the era of radiomics.Diagn. Interv. Imaging20181001064765530555018
     [Google Scholar]
  17. van GriethuysenJ.J.M. FedorovA. ParmarC. HosnyA. AucoinN. NarayanV. Beets-TanR.G.H. Fillion-RobinJ.C. PieperS. AertsH.J.W.L. Computational radiomics system to decode the radiographic phenotype.Cancer Res.20177721e104e10710.1158/0008‑5472.CAN‑17‑033929092951
     [Google Scholar]
  18. JungS.E. RhaS.E. LeeJ.M. ParkS.Y. OhS.N. ChoK.S. LeeE.J. ByunJ.Y. HahnS.T. CT and MRI findings of sex cord-stromal tumor of the ovary.AJR Am. J. Roentgenol.2005185120721510.2214/ajr.185.1.0185020715972425
     [Google Scholar]
  19. LakhmanY. VeeraraghavanH. ChaimJ. FeierD. GoldmanD.A. MoskowitzC.S. NougaretS. SosaR.E. VargasH.A. SoslowR.A. Abu-RustumN.R. HricakH. SalaE. Differentiation of uterine leiomyosarcoma from atypical leiomyoma: Diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis.Eur. Radiol.20172772903291510.1007/s00330‑016‑4623‑927921159
     [Google Scholar]
  20. ThapaD. WangP. WuG. A histogram analysis of diffusion and perfusion features of cervical cancer based on intravoxel incoherent motion magnetic resonance imaging.Magn. Reson. Imaging20185510311129953932
     [Google Scholar]
  21. YeR. WengS. LiY. YanC. ChenJ. ZhuY. WenL. Texture analysis of three-dimensional MRI images may differentiate borderline and malignant epithelial ovarian tumors.Korean J. Radiol.202122110611710.3348/kjr.2020.012132932563
     [Google Scholar]
  22. JhaA.K. MithunS. JaiswarV. SherkhaneU.B. PurandareN.C. PrabhashK. RangarajanV. DekkerA. WeeL. TraversoA. Repeatability and reproducibility study of radiomic features on a phantom and human cohort.Sci. Rep.2021111205510.1038/s41598‑021‑81526‑833479392
     [Google Scholar]
  23. ThomasJ.V. ElkassemA.A.M. GaneshanB. SmithA.D. MR imaging texture analysis in the abdomen and pelvis.Magn. Reson. Imaging Clin. N. Am.202028344745610.1016/j.mric.2020.03.00932624161
     [Google Scholar]
  24. ZhangH. LiuX. WangT. WangY. WangJ. JinJ. ZhangG. Histogram analysis of apparent diffusion coefficient on diffusion weighted magnetic resonance imaging in differentiation between low and high grade serous ovarian cancer.Curr. Med. Imaging Rev.202319216717410.2174/157340561866622051710101235585829
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056269130240117071800
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Histogram Feature Analysis of Tumor Body on Diffusion-weighted MR Imaging in Differentiation between Granulosa Cell Tumors and Other Sex-cord Tumors in Ovary: Comparison with Histological Results
Curr. Med. Imaging 20, e15734056269130 (2024); https://doi.org/10.2174/0115734056269130240117071800
/content/journals/cmir/10.2174/0115734056269130240117071800
/content/journals/cmir/10.2174/0115734056269130240117071800
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  • Article Type:     Research Article
Keyword(s): Diffusion-weighted imaging; Granulosa cell tumor; Magnetic resonance imaging; Ovarian neoplasm; Sex cord tumors; Tumor body

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