Abstract #2143

Radiomics model with reduced field-of-view DWI for predicting LVSI and high-risk histological type in endometrial cancer

Akiyo Takada^1,2, Daniel A. Di Giovanni³, Takuro Horikoshi⁴, Takahiro Tsuboyama⁵, Hajime Yokota², Sakurako Harada Kagitani⁶, Evan McNabb⁷, Takashi Uno², and Caroline Reinhold⁸

¹Radiology, McGill University, Montreal, QC, Canada, ²Department of Diagnostic Radiology and Radiation Oncology, Chiba University Graduate School of Medicine, Chiba, Japan, ³Department of Diagnostic Radiology, McGill University, Montreal, QC, Canada, ⁴Department of Radiology, Chiba University Hospital, Chiba, Japan, ⁵Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan, ⁶Department of pathology, Chiba University Hospital, Chiba, Japan, ⁷Department of Medical Imaging, McGill University Health Centre, Montreal, QC, Canada, ⁸Department of Radiology, McGill University, Montreal, QC, Canada

Synopsis

Keywords: Uterus, Uterus

Motivation: Although reduced field of view (rFOV) DWI is essential for preoperative diagnosis and staging of endometrial cancer (EC), few studies have investigated its potential for predicting EC prognostic factors using radiomics analysis.

Goal(s): Our study aims to predict substantial/extensive LVSI and high-risk histological type in EC using radiomics analysis with rFOV DWI.

Approach: We analyzed 206 patients from 2 hospitals, extracting radiomic features from multiple sequences, including rFOV DWI. We utilized a Bayesian model incorporating radiomic and clinical data, including CA125 and hospital differences.

Results: The Bayesian model showed high accuracy for LVSI (93.5%) and moderate for high-risk histology (80.6%).

Impact: This study impacts endometrial cancer care by radiomics analysis using Bayesian modeling with MRI-derived radiomic features to predict lymphovascular space invasion and high-risk histology, supporting personalized treatment. Findings suggest the potential of rFOV DWI.

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