Accurate characterization of sonographically-indeterminate ovarian masses before surgery is crucial for proper disease management. While DCE-MRI has emerged as a problem-solving technique, accurate parameter estimations from semi-quantitative or PK analysis are dependent on multiple steps, including proper protocol design, motion reduction, selection of physiology-based PK model and AIF, which discourages development and reliability of computer-aided diagnostic procedures. Here, we aimed to develop a one-step pre-processing and quantification classification scheme based on a five-parameter Sigmoid model, capturing early- to late-enhancement kinetics, including washout as a previously overlooked parameter for ovarian masses, to generate accurate differentiation of complex ovarian masses.
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