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European Radiology

, Volume 29, Issue 2, pp 838–848 | Cite as

Preoperative prediction of deep myometrial invasion and tumor grade for stage I endometrioid adenocarcinoma: a simple method of measurement on DWI

  • Bin YanEmail author
  • Xiufen Liang
  • Tingting Zhao
  • Chen Niu
  • Caixia Ding
  • Wenjun Liu
Magnetic Resonance
  • 123 Downloads

Abstract

Objectives

To explore the utility of the tumor area ratio (TAR) for predicting deep myometrial invasion and tumor grade in stage I endometrioid adenocarcinoma (EEA).

Methods

We retrospectively evaluated 86 patients with International Federation of Gynecology and Obstetrics (FIGO) stage I EEA. All patients underwent unenhanced contrast MRI and diffusion-weighted imaging (DWI) procedures. The volume and maximum area of the tumor and uterus were obtained, and the tumor volume ratio (TVR) and TAR were calculated. The Kruskal-Wallis test and Mann-Whitney U test were used to compare the differences in indexes (TVR and TAR) between the different tumor grades and between superficial and deep myometrial invasion.

Results

The TVR and TAR values for deep myometrial invasion and high-grade EEA tumors were significantly higher than the values for superficial myometrial invasion and low-grade tumors (all p = 0.000). According to the receiver-operating characteristic (ROC) curve, the area under the curve (AUC) was significantly higher for TAR than for TVR for tumors with deep myometrial invasion (0.936 vs. 0.844, p = 0.045). However, no significant differences in the AUCs for TVR and TAR were observed between high- and low-grade tumors (0.865 vs. 0.863, p = 0.956). A TAR ≥ 34.6% predicted deep myometrial invasion in EEA with a sensitivity, specificity, and accuracy of 85.0%, 84.8%, and 86.0%, respectively. A TAR ≥ 38.9% predicted high-grade tumors with a sensitivity, specificity, and accuracy of 83.3%, 81.1%, and 82.6%, respectively.

Conclusion

TAR is useful for predicting deep myometrial invasion and high-grade stage I EEA

Key Points

TAR is useful for predicting risk factors for EEA.

TAR is easy to obtain and has high accuracy.

TAR has excellent interobserver repeatability agreement (ICC range 95.1–99.6%).

Keywords

Endometrioid adenocarcinoma Cell differentiation Neoplasm staging Diffusion Magnetic resonance imaging 

Abbreviations

AUC

Area under the curve

CE-MRI

Contrast-enhanced MRI

CI

Confidence interval

DWI

Diffusion-weighted imaging

EC

Endometrial cancer

EEA

Endometrioid adenocarcinoma

FIGO

International Federation of Gynecology and Obstetrics

ICC

Interobserver correlation coefficient

MRI

Magnetic resonance imaging

NPV

Negative predictive value

PPV

Positive predictive value

ROC

Receiver-operating characteristic curve

TAR

Tumor area ratio

TV

Tumor volume

TVR

Tumor volume ratio

Notes

Funding

This study received funding from the Fundamental Research Funds for the Central Universities of China (1191320118).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Bin Yan, Department of Radiology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an Shaanxi, P.R China.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (Wenjun Liu) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in: Yan B et al Can the apparent diffusion coefficient differentiate the grade of endometrioid adenocarcinoma and the histological subtype of endometrial cancer? Acta Radiol, 2018, 59:363-370.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

References

  1. 1.
    Teng F, Zhang YF, Wang YM et al (2015) Contrast-enhanced MRI in preoperative assessment of myometrial and cervical invasion, and lymph node metastasis: diagnostic value and error analysis in endometrial carcinoma. Acta Obstet Gynecol Scand 94:266–273CrossRefGoogle Scholar
  2. 2.
    Yan B, Zhao TT, Liang XF, Niu C, Ding CX (2018) Can the apparent diffusion coefficient differentiate the grade of endometrioid adenocarcinoma and the histological subtype of endometrial cancer? Acta Radiol 59:363–370CrossRefGoogle Scholar
  3. 3.
    Benedetti PP, Basile S, Maneschi F et al (2008) Systematic pelvic lymphadenectomy vs. no lymphadenectomy in early-stage endometrial carcinoma: randomized clinical trial. J Natl Cancer Inst 100:1707–1716CrossRefGoogle Scholar
  4. 4.
    Luomaranta A, Butzow R, Pauna AR, Leminen A, Loukovaara M (2015) Combined use of endometrial sample and magnetic resonance imaging in the preoperative risk-stratification of endometrial carcinomas. Acta Obstet Gynecol Scand 94:95–101CrossRefGoogle Scholar
  5. 5.
    Kinkel K, Forstner R, Danza FM et al (2009) Staging of endometrial cancer with MRI: guidelines of the European Society of Urogenital Imaging. Eur Radiol 19:1565–1574CrossRefGoogle Scholar
  6. 6.
    Zhang L, Liu A, Zhang T, Song Q, Wei Q, Wang H (2015) Use of diffusion tensor imaging in assessing superficial myometrial invasion by endometrial carcinoma: a preliminary study. Acta Radiol 56:1273–1280CrossRefGoogle Scholar
  7. 7.
    Meissnitzer M, Forstner R (2016) MRI of endometrium cancer—how we do it. Cancer Imaging 16:11CrossRefGoogle Scholar
  8. 8.
    Bakir B, Sanli S, Bakir VL et al (2017) Role of diffusion weighted MRI in the differential diagnosis of endometrial cancer, polyp, hyperplasia, and physiological thickening. Clin Imaging 41:86–94CrossRefGoogle Scholar
  9. 9.
    Park SB (2016) Functional MR imaging in gynecologic malignancies: current status and future perspectives. Abdom Radiol (NY) 41:2509–2523CrossRefGoogle Scholar
  10. 10.
    Das SK, Niu XK, Wang JL et al (2014) Usefulness of DWI in preoperative assessment of deep myometrial invasion in patients with endometrial carcinoma: a systematic review and meta-analysis. Cancer Imaging 14:32CrossRefGoogle Scholar
  11. 11.
    Lin G, Ng KK, Chang CJ, Wang JJ, Ho KC, Wu TI (2009) Myometrial invasion in endometrial cancer: diagnostic accuracy of diffusion-weighted 3.0-T MR imaging—initial experience. Radiology 250:784–792CrossRefGoogle Scholar
  12. 12.
    Andreano A, Rechichi G, Rebora P, Sironi S, Valsecchi MG, Galimberti S (2014) MR diffusion imaging for preoperative staging of myometrial invasion in patients with endometrial cancer: a systematic review and meta-analysis. Eur Radiol 24:1327–1338CrossRefGoogle Scholar
  13. 13.
    Woo S, Cho JY, Kim SY, Kim SH (2014) Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade. Acta Radiol 55:1270–1277CrossRefGoogle Scholar
  14. 14.
    Tamai K, Koyama T, Saga T et al (2007) Diffusion-weighted MR imaging of uterine endometrial cancer. J Magn Reson Imaging 26:682–687CrossRefGoogle Scholar
  15. 15.
    Nakamura K, Imafuku N, Nishida T et al (2012) Measurement of the minimum apparent diffusion coefficient (ADCmin) of the primary tumor and CA125 are predictive of disease recurrence for patients with endometrial cancer. Gynecol Oncol 124:335–339CrossRefGoogle Scholar
  16. 16.
    Seo JM, Kim CK, Choi D, Kwan PB (2013) Endometrial cancer: utility of diffusion-weighted magnetic resonance imaging with background body signal suppression at 3T. J Magn Reson Imaging 37:1151–1159CrossRefGoogle Scholar
  17. 17.
    Nougaret S, Reinhold C, Alsharif SS et al (2015) Endometrial cancer: combined MR volumetry and diffusion-weighted imaging for assessment of myometrial and lymphovascular invasion and tumor grade. Radiology 276:797–808CrossRefGoogle Scholar
  18. 18.
    Kishimoto K, Tajima S, Maeda I et al (2016) Endometrial cancer: correlation of apparent diffusion coefficient (ADC) with tumor cellularity and tumor grade. Acta Radiol 57:1021–1028CrossRefGoogle Scholar
  19. 19.
    Rechichi G, Galimberti S, Signorelli M et al (2011) Endometrial cancer: correlation of apparent diffusion coefficient with tumor grade, depth of myometrial invasion, and presence of lymph node metastases. AJR Am J Roentgenol 197:256–262CrossRefGoogle Scholar
  20. 20.
    Bharwani N, Miquel ME, Sahdev A, Naeayanan P (2011) Diffusion-weighted imaging in the assessment of tumour grade in endometrial cancer. Br J Radiol 84:997–1004CrossRefGoogle Scholar
  21. 21.
    Mainenti PP, Pizzuti LM, Segreto S et al (2016) Diffusion volume (DV) measurement in endometrial and cervical cancer: a new MRI parameter in the evaluation of the tumor grading and the risk classification. Eur J Radiol 85:113–124CrossRefGoogle Scholar
  22. 22.
    Husby JA, Salvesen ØO, Magnussen IJ et al (2015) Tumour apparent diffusion coefficient is associated with depth of myometrial invasion and is negatively correlated to tumour volume in endometrial carcinomas. Clin Radiol 70:487–494CrossRefGoogle Scholar
  23. 23.
    Todo Y, Choi HJ, Kang S et al (2013) Clinical significance of tumor volume in endometrial cancer: a Japan-Korea cooperative study. Gynecol Oncol 131:294–298CrossRefGoogle Scholar
  24. 24.
    Bourgioti C, Chatoupis K, Tzavara C, Antoniou A, Rodolakis A, Moulopoulos LA (2016) Predictive ability of maximal tumor diameter on MRI for high-risk endometrial cancer. Abdom Radiol (NY) 41:2484–2495CrossRefGoogle Scholar
  25. 25.
    Kurman RJ, Carcangiu ML, Herrington CS, Young RH (2014) WHO Classification of Tumours of Female Reproductive Organs, 4th edn. IARC Press, LyonGoogle Scholar
  26. 26.
    Husby JA, Reitan BC, Biermann M (2015) Metabolic tumor volume on 18F-FDG PET/CT improves preoperative identification of high-risk endometrial carcinoma patients. J Nucl Med 56:1191–1198CrossRefGoogle Scholar
  27. 27.
    Amant F, Moerman P, Neven P, Timmerman D, Van Limbergen E, Vergote I (2005) Endometrial cancer. Lancet 366:491–505CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology, Shaanxi Provincial Tumor HospitalXi’an Jiaotong UniversityXi’an ShaanxiPeople’s Republic of China
  2. 2.Department of RadiologyThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an ShaanxiPeople’s Republic of China
  3. 3.Department of Pathology, Shaanxi Provincial Tumor HospitalXi’an Jiaotong UniversityXi’an ShaanxiPeople’s Republic of China
  4. 4.Department of Epidemiology and Biotatistics, School of Public HealthXi’an Jiaotong UniversityXi’an ShaanxiPeople’s Republic of China

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