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Abstract #1521

Predicted tumor stroma segmentation from high-field MR texture maps and machine learning: an ex vivo study on ovarian tumors

Marion Tardieu1, Lakhdar Khellaf2, Maida Cardoso3, Olivia Sgarbura4, Pierre-Emmanuel Colombo4, Christophe Goze-Bac3, and Stephanie Nougaret1
1Montpellier Cancer Research Institute (IRCM), INSERM U1194, University of Montpellier, Montpellier, France, 2Department of pathology, Montpellier Cancer Institute (ICM), Montpellier, France, 3BNIF facility, L2C, UMR 5221, CNRS, University of Montpellier, Montpellier, France, 4Department of Surgery, Montpellier Cancer Institute (ICM), Montpellier, France


The objective was to probe the associations of high-field MR-images and their derived texture maps (TM) with histopathology in ovarian cancer (OC). Four ovarian tumors were imaged ex-vivo using a 9.4T-MR scanner. Automated MR-derived stroma-tumor segmentation maps were constructed using machine learning and validated against histology. Through TM, we found that areas of tumor cells appeared uniform on MR-images, while areas of stroma appeared heterogeneous. Using the automated model, MRI predicted stromal proportion with an accuracy from 61.4% to 71.9%. In this hypothesis-generating study, we showed that it is feasible to resolve histologic structures in OC using ex-vivo MR radiomics.

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