Preoperative tumor texture analysis on MRI predicts high‐risk disease and reduced survival in endometrial cancer. Issue 6 (13th August 2018)

• Record Type: Journal Article
• Authors: Ytre‐Hauge, Sigmund; Dybvik, Julie A.; Lundervold, Arvid; Salvesen, Øyvind O.; Krakstad, Camilla; Fasmer, Kristine E.; Werner, Henrica M.; Ganeshan, Balaji; Høivik, Erling; Bjørge, Line; Trovik, Jone; Haldorsen, Ingfrid S.
• Is Part Of: Journal of magnetic resonance imaging. Volume 48:Issue 6(2018)
• Page Start: 1637
• Page End: 1647
• Publication Date: 2018-08-13
• Subjects: endometrial neoplasms -- magnetic resonance imaging -- image analysis -- computer‐assisted -- entropy -- risk assessment; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26184
• Languages: English
• ISSNs: 1053-1807

Abstract

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