Sigmund Ytre-Hauge1,2, Julie A Dybvik1, Arvid Lundervold1,3, Øyvind O Salvesen4, Camilla Krakstad5,6, Kristine E Fasmer1,2, Henrica M Werner5,6, Balaji Ganeshan7, Erling Høivik5,6, Line Bjørge5,6, Jone Trovik5,6, Ingfrid S Haldorsen1,2. 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.
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 cancerpatients. 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.
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