MRI-based radiomics model for distinguishing Stage I endometrial carcinoma from endometrial polyp: a multicenter study

Acta Radiol. 2023 Sep;64(9):2651-2658. doi: 10.1177/02841851231175249. Epub 2023 Jun 8.

Abstract

Background: Patients with early endometrial carcinoma (EC) have a good prognosis, but it is difficult to distinguish from endometrial polyps (EPs).

Purpose: To develop and assess magnetic resonance imaging (MRI)-based radiomics models for discriminating Stage I EC from EP in a multicenter setting.

Material and methods: Patients with Stage I EC (n = 202) and EP (n = 99) who underwent preoperative MRI scans were collected in three centers (seven devices). The images from devices 1-3 were utilized for training and validation, and the images from devices 4-7 were utilized for testing, leading to three models. They were evaluated by the area under the receiver operating characteristic curve (AUC) and metrics including accuracy, sensitivity, and specificity. Two radiologists evaluated the endometrial lesions and compared them with the three models.

Results: The AUCs of device 1, 2_ada, device 1, 3_ada, and device 2, 3_ada for discriminating Stage I EC from EP were 0.951, 0.912, and 0.896 for the training set, 0.755, 0.928, and 1.000 for the validation set, and 0.883, 0.956, and 0.878 for the external validation set, respectively. The specificity of the three models was higher, but the accuracy and sensitivity were lower than those of radiologists.

Conclusion: Our MRI-based models showed good potential in differentiating Stage I EC from EP and had been validated in multiple centers. Their specificity was higher than that of radiologists and may be used for computer-aided diagnosis in the future to assist clinical diagnosis.

Keywords: Magnetic resonance imaging; Stage I endometrial carcinoma; endometrial polyp; multicenter study; radiomics.

Publication types

  • Multicenter Study

MeSH terms

  • Endometrial Neoplasms* / diagnostic imaging
  • Endometrial Neoplasms* / pathology
  • Female
  • Humans
  • Magnetic Resonance Imaging / methods
  • ROC Curve
  • Retrospective Studies
  • Uterine Neoplasms*