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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