Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer

J Magn Reson Imaging. 2018 Dec;48(6):1637-1647. doi: 10.1002/jmri.26184. Epub 2018 Aug 13.


Background: Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer.

Purpose: To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high-risk histological subtype) and to outcome in endometrial cancer patients.

Study type: Prospective cohort study.

Population/subjects: In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017.

Field strength/sequences: Preoperative pelvic MRI including contrast-enhanced T1 -weighted (T1 c), T2 -weighted, and diffusion-weighted imaging at 1.5T.

Assessment: Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross-sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration-histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated.

Statistical tests: Associations between texture parameters and histological features were assessed by uni- and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis.

Results: High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio [OR] 3.2, P lt 0.001), and high MPP in T1 c images independently predicted high-risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T1 c images predicted reduced recurrence- and progression-free survival (hazard ratio [HR] 1.5, P lt 0.001) after adjusting for MRI-measured tumor volume and histological risk at biopsy.

Data conclusion: MRI-derived tumor texture parameters independently predicted deep myometrial invasion, high-risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer.

Level of evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1637-1647.

Keywords: computer-assisted; endometrial neoplasms; entropy; image analysis; magnetic resonance imaging; risk assessment.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biopsy
  • Cervix Uteri / diagnostic imaging
  • Contrast Media
  • Diffusion Magnetic Resonance Imaging*
  • Disease-Free Survival
  • Endometrial Neoplasms / diagnostic imaging*
  • Endometrial Neoplasms / mortality*
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Lymphatic Metastasis
  • Middle Aged
  • Multivariate Analysis
  • Myometrium / diagnostic imaging
  • Neoplasm Invasiveness
  • Neoplasm Recurrence, Local
  • Preoperative Period
  • Prognosis
  • Proportional Hazards Models
  • Prospective Studies
  • ROC Curve
  • Risk
  • Treatment Outcome
  • Tumor Burden


  • Contrast Media