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



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


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.


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.


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%).


Endometrioid adenocarcinoma Cell differentiation Neoplasm staging Diffusion Magnetic resonance imaging 



Area under the curve


Contrast-enhanced MRI


Confidence interval


Diffusion-weighted imaging


Endometrial cancer


Endometrioid adenocarcinoma


International Federation of Gynecology and Obstetrics


Interobserver correlation coefficient


Magnetic resonance imaging


Negative predictive value


Positive predictive value


Receiver-operating characteristic curve


Tumor area ratio


Tumor volume


Tumor volume ratio



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

Compliance with ethical standards


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.


• retrospective

• diagnostic or prognostic study

• performed at one institution


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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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