DCE-MRI and its subsequently derived pharmacokinetic parameters have been adopted to explore tumor angiogenesis and vascular permeability changes inside tumors and improve the diagnostic accuracy of ovarian tumors. Radiomics can convert medical images to mineable high-dimensional quantitative imaging features based on automatic feature extraction algorithms. In this study, we present a radiomics model based on a DCE-MRI PK protocol and establish an effective and noninvasive 3-class classification prediction model for the discrimination among benign, borderline and malignant ovarian tumors.
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