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

Abstract

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.