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Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution



To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)–based endometrial cancer (EC) MR imaging (ECM).


We retrospectively enrolled 530 patients with pathologically proven EC at our institution between January 1, 2013, and December 31, 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both sagittal and coronal T2WI-based MR images were used for lesion area determination. All MR images were divided into two groups: deep (more than 50%) and shallow (less than 50%) MI based on their pathological diagnosis. We trained a detection model based on YOLOv3 algorithm to locate the lesion area on ECM. Then, the detected regions were fed into a classification model based on DL network to identify MI depth automatically.


In the testing dataset, the trained model detected lesion regions with an average precision rate of 77.14% and 86.67% in both sagittal and coronal images, respectively. The classification model yielded an accuracy of 84.78%, a sensitivity of 66.67%, a specificity of 87.50%, a positive predictive value of 44.44%, and a negative predictive value of 94.59% in determining deep MI. The radiologists and trained network model together yielded an accuracy of 86.2%, a sensitivity of 77.8%, a specificity of 87.5%, a positive predictive value of 48.3%, and a negative predictive value of 96.3%.


In this study, the DL network model derived from MR imaging provided a competitive, time-efficient diagnostic performance in MI depth identification.

Key Points

• The models established with the deep learning method could help improve the diagnostic confidence and performance of MI identification based on endometrial cancer MR imaging.

• The models enabled the classification of endometrial cancer MR images to the two categories with a sensitivity of 0.67, a specificity of 0.88, and an accuracy of 0.85.

• Using the detected lesion region to evaluate myometrial invasion depth could remove redundant information in the image and provide more effective features.

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Area under the ROC curve


Confidence interval


Convolutional neural network


Deep learning


Diffusion-weighted imaging


Endometrial cancer


Endometrial cancer MR imaging


Echo-planar imaging two-dimensional


Fat-saturated T2-weighted imaging


Lymph node metastases


Maximum diameter


Myometrial invasion


Machine learning


Magnetic resonance imaging


Negative predictive value


Picture archiving and communication systems


Positive predictive value




Receiver operating characteristic curve






T1-weighted imaging


T2-weighted imaging


Turbo spin-echo


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This study has received funding by National Natural Science Foundation of China (No.61731009, 81,771,816).

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Corresponding authors

Correspondence to Guang Yang or He Zhang.

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The scientific guarantor of this publication is Guofu Zhang.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.


• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Supplementary Table 1

Details of parameters for 1.5 Tesla MRI imaging protocols (DOCX 18 kb)

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Chen, X., Wang, Y., Shen, M. et al. Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution. Eur Radiol 30, 4985–4994 (2020).

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  • Endometrial cancer
  • Magnetic resonance imaging
  • Deep learning
  • Neoplasm staging