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Abstract #0100

A One-Step Biomarker Quantification Methodology for DCE-MRI of Complex Ovarian Masses: Capturing Kinetic Pattern from Early to Late Enhancement

Anahita Fathi Kazerooni1,2, Mahnaz Nabil3, Hamidreza Haghighat Khah4, and Hamidreza Saligheh Rad1,2

1Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran, 2Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran, 3Department of Mathematics, Islamic Azad University, Qazvin Branch, Qazvin, Iran, 4Department of Diagnostic Imaging, Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

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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