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

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

Author information

1
Department of Radiology, Haukeland University Hospital, Bergen, Norway.
2
Section for Radiology, Department of Clinical Medicine, University of Bergen, Norway.
3
Department of Biomedicine, University of Bergen, Norway.
4
Unit for Applied Clinical Research, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
5
Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway.
6
Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Norway.
7
Institute of Nuclear Medicine, University College London, London, UK.

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

PMID:
30102441
DOI:
10.1002/jmri.26184

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