Abstract #1717

Evaluation of breast cancer using IVIM-Kurtosis and compartmental tracer kinetic models through semi-automated segmentation

Natalia Noemí Massaccesi Bove^1,2, Trinidad González Padin^1,2, Daniel Fino^1,2,3, Nicolás Moyano Brandi^1,2, Clara Lisazo^1,2, Leandro Enrique Salcedo^1,2,4, Federico Julián González Nicolini^2,3,5, Pedro Pablo Ariza^1,2, Roberto Andres Isoardi^2,3,5, María Cecilia Lorca^1,2, Cristian Allard⁶, and Verónica R. Saucedo^1,2

¹MRI Department, Fundación Argentina para el Desarrollo en Salud, Mendoza, Argentina, ²MRI Department, Fundación Escuela de Medicina Nuclear, Mendoza, Argentina, ³Instituto Balseiro, Universidad Nacional de Cuyo, San Carlos de Bariloche, Argentina, ⁴Universidad de Mendoza, Mendoza, Argentina, ⁵GQNYCS, Comisión Nacional de Energía Atómica, CABA, Argentina, ⁶GE Healthcare, Santiago de Chile, Chile

Synopsis

This study aims to evaluate the correlation between the IVIM-DKI models (which describe pure and pseudo diffusion) and the quantitative and semiquantitative parameters from dynamic contrast-enhanced (DCE) MRI (that analyze vascular permeability and pharmacokinetics). A Python algorithm was implemented to adjust the intensities of 12 b-values DWI to a biexponential function modeling the IVIM and DKI. Then, the statistical association between these parameters and the DCE MRI was evaluated with a Pearson’s correlation coefficient. The f and D* factors are both good biomarkers for the evaluation of perfusion properties, according to the examined correlation between all the parameters.

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