Skip to main content
SpringerLink
Log in

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma

  • Pelvis
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC).

Methods

Clinical features, solid component ADC (ADCSC) and whole-tumor ADC histogram-derived parameters (volume, the ADCmean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal–Wallis test, and receiver operating characteristic curves.

Results

A significantly higher ADCSC and smaller volume were associated with SMBT than with CCC/EC. The ADCmean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADCSC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADCSC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADCmean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively.

Conclusion

Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Kurata Y, Kido A, Moribata Y, et al. Diagnostic performance of MR imaging findings and quantitative values in the differentiation of seromucinous borderline tumour from endometriosis-related malignant ovarian tumour. Eur Radiol. 2017, 27: 1695-1703.

    Article  Google Scholar 

  2. Maeda D, Shih I. Pathogenesis and the role of ARID1A mutation in endometriosis-related ovarian neoplasms. Adv Anat Pathol. 2013, 20: 45-52.

    Article  CAS  Google Scholar 

  3. Mary A W, Ie-Ming S, James H S, et al. ancer Implications for Patients with Endometriosis. Semin Reprod Med. 2017, 35: 110-116.

    Article  Google Scholar 

  4. Yoshikawa H, Jimbo H, Okada S et al. Prevalence of endometriosis in ovarian cancer. Gynecol Obstet Invest. 2000, 50 Suppl 1:11-7.

    Article  Google Scholar 

  5. Nagamine M, Mikami Y. Ovarian seromucinous tumors: pathogenesis, morphologic spectrum, and clinical issues. Diagnostics (Basel). 2020, 10: 77.

    Article  Google Scholar 

  6. Karpathiou G, Chauleur C, Corsini T, et al. Seromucinous ovarian tumor a comparison with the rest of ovarian epithelial tumors. Annals of Diagnostic Pathology. 2017, 27: 28-33.

    Article  Google Scholar 

  7. NCCN Clinical practice guidelines in oncology: ovarian cancer including fallopian tube cancer and primary peritoneal cancer. Version 1.2021.

  8. Li HM, Feng F, Qiang JW, et al. Quantitative dynamic contrast-enhanced MR imaging for differentiating benign, borderline, and malignant ovarian tumors. Abdom Radiol (NY). 2018, 43: 3132-3141.

    Article  Google Scholar 

  9. Guiu MX, Stewart CJR. Endometriosis-associated ovarian neoplasia. Pathology. 2018, 50: 190-204.

    Article  Google Scholar 

  10. Li M, Tan J, Zhang Y, et al. Assessing CT imaging features combined with CEA and CA125 levels to identify endometriosis-associated ovarian cancer. Abdom Radiol (NY). 2021, 46: 2367-2375..

    Article  Google Scholar 

  11. Han J W, Kim K A, Chang H Y, et al. Newly categorized seromucinous tumor of the ovary: magnetic resonance imaging findings. J Comput Assist Tomogr. 2019, 43: 119-127.

    Article  Google Scholar 

  12. Kurata Y, Kido A, Moribata Y, et al. Differentiation of seromucinous borderline tumor from serous borderline tumor on MR imaging. Magn Reson Med Sci. 2018, 17: 211-217.

    Article  Google Scholar 

  13. Liu T, Sumida D, Wada T, et al. A diagnostic challenge of seromucinous borderline tumor: a case report. Medicine (Baltimore). 2019, 98: e15707.

    Article  Google Scholar 

  14. Morioka S, Kawaguchi R, Yamada Y, et al. Magnetic resonance imaging findings for discriminating clear cell carcinoma and endometrioid carcinoma of the ovary. J Ovarian Res. 2019, 12: 20.

    Article  Google Scholar 

  15. Yumiko O T, Satoshi O, Takako Y, et al. MRI of endometriotic cysts in association with ovarian carcinoma. AJR Am J Roentgenol. 2010, 194: 355-361.

    Article  Google Scholar 

  16. Lu J, Li H M, Cai S Q, et al. Prediction of platinum-based chemotherapy response in advanced high-grade serous ovarian cancer: ADC histogram analysis of primary tumors. Acad Radiol. 2020: S1076-6332(20)30052-0.

    Google Scholar 

  17. He M, Song Y, Li H, et al. Histogram analysis comparison of monoexponential, advanced diffusion-weighted imaging, and dynamic contrast-enhanced MRI for differentiating borderline from malignant epithelial ovarian tumors. J Magn Reson Imaging. 2020, 52: 257-268..

    Article  Google Scholar 

  18. Li H M, Zhang R, Gu W Y, et al. Whole solid tumour volume histogram analysis of the apparent diffusion coefficient for differentiating high-grade from low-grade serous ovarian carcinoma: correlation with Ki-67 proliferation status. Clin Radiol. 2019, 74: 918-925.

    Article  CAS  Google Scholar 

  19. Xu Y S, Liu H F, Xi D L, et al. Whole-lesion histogram analysis metrics of the apparent diffusion coefficient: a correlation study with histological grade of hepatocellular carcinoma. Abdom Radiol (NY). 2019, 44: 3089-3098.

    Article  Google Scholar 

  20. Xiao Z, Tang Z, Zhang J, et al. Whole-tumor histogram analysis of monoexponential and advanced diffusion-weighted imaging for sinonasal malignant tumors: Correlations with histopathologic features. J Magn Reson Imaging. 2020, 51: 273-285.

    Article  Google Scholar 

  21. Zhang Z, Song C, Zhang Y, et al. Apparent diffusion coefficient (ADC) histogram analysis: differentiation of benign from malignant parotid gland tumors using readout-segmented diffusion-weighted imaging. Dentomaxillofac Radiol. 2019, 48: 20190100.

    Article  Google Scholar 

  22. Pope W B, Kim H J, Huo J, et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology. 2009, 252: 182-189.

    Article  Google Scholar 

  23. Kyriazi S, Collins D J, Messiou C, et al. Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients. Radiology. 2011, 261: 182-192.

    Article  Google Scholar 

  24. Zhou L, Yao L, Dai L, et al. Ovarian endometrioid carcinoma and clear cell carcinoma: A 21-year retrospective study. J Ovarian Res. 2021, 14:63.

    Article  Google Scholar 

  25. Kim H S, Kim T H, Chung H H, et al. Risk and prognosis of ovarian cancer in women with endometriosis: a meta-analysis. Br J Cancer. 2014, 110:1878-1890.

    Article  CAS  Google Scholar 

  26. Zhang Y D, Wu C J, Wang Q, et al. Comparison of utility of histogram apparent diffusion coefficient and R2* for differentiation of low-grade from high-grade clear cell renal cell carcinoma. AJR Am J Roentgenol. 2015, 205: W193-W201.

    Article  Google Scholar 

  27. Ono T, Kishimoto K, Tajima S, et al. Apparent diffusion coefficient (ADC) values of serous, endometrioid, and clear cell carcinoma of the ovary: pathological correlation. Acta Radiol. 2020, 61: 992-1000.

    Article  Google Scholar 

  28. Schob S, Meyer H, Dieckow J, et al. Histogram analysis of diffusion weighted imaging at 3T is useful for prediction of lymphatic metastatic spread, proliferative activity, and cellularity in thyroid cancer. Int J Mol Sci. 2017, 18: 821.

    Article  Google Scholar 

  29. Wang F, Wang Y X, Zhou Y, et al. Apparent diffusion coefficient histogram analysis for assessing tumor staging and detection of lymph node metastasis in epithelial ovarian cancer: correlation with p53 and Ki-67 expression. Mol Imaging Biol. 2019, 21: 731-739.

    Article  CAS  Google Scholar 

  30. Moteki T, Horikoshi H, Endo K. Relationship between apparent diffusion coefficient and signal intensity in endometrial and other pelvic cysts. Magn Reson Imaging. 2002, 20: 463-470.

    Article  Google Scholar 

  31. Kato N, Sasou S, Motoyama T. Expression of hepatocyte nuclear factor-1beta (HNF-1beta) in clear cell tumors and endometriosis of the ovary. Mod Pathol. 2006, 19: 83-89.

    Article  CAS  Google Scholar 

  32. Xiao W B, Awadallah A, Xin W. Loss of ARID1A/BAF250a expression in ovarian endometriosis and clear cell carcinoma. Int J Clin Exp Pathol. 2012, 5: 642-650.

    CAS  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of P.R. China [No. 81971579], Shanghai Municipal Commission of Science and Technology [No. 19411972000], Shanghai Municipal Health Commission [No. ZK2019B01] and Shanghai Jinshan District Health Commission [No.JSYQ201901].

Author information

Authors and Affiliations

  1. Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People’s Republic of China

    Jing Lu, Shuhui Zhao, Fenghua Ma, Haiming Li, Yong’ai Li & Jinwei Qiang

  2. Department of Radiology, Xinhua Hospital, Medical College of Shanghai Jiao Tong University, Shanghai, 200092, People’s Republic of China

    Shuhui Zhao

  3. Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai, 200011, People’s Republic of China

    Fenghua Ma

  4. Department of Radiology, Shanghai Cancer Center, Fudan University, 270 Dongan Road, Shanghai, 200032, People’s Republic of China

    Haiming Li

Authors
  1. Jing Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuhui Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fenghua Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haiming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong’ai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinwei Qiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yong’ai Li or Jinwei Qiang.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, J., Zhao, S., Ma, F. et al. Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma. Abdom Radiol 48, 724–732 (2023). https://doi.org/10.1007/s00261-022-03742-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-022-03742-8

Keywords

Search

Navigation