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Nadya Shusharina, PhD: Harnessing DW-MRI to Identify Direction of the Microscopic Tumor Spread

 

 

 

 


Nadya Shusharina, PhD

Instructor in Radiation Oncology
Massachusetts General Hospital

Abstract

I will present our ongoing work to develop a microscopic tumor spread model for soft tissue sarcoma (STS) and glioma that is based on pre-treatment imaging data and thus patient specific. Recent microscopic studies have confirmed that cancer cells invade soft tissue by adopting the encountered tissue structure, which acts as a barrier or determines the direction of spread. Diffusion-weighted MR imaging (DW-MRI) provides information on tissue microstructure based on the anisotropic diffusion of water molecules and can thus be used to define the directionality of muscle fibers. In the musculoskeletal system, clinical applications of DW-MRI are limited to the identification of muscle pathology. We aim to unlock the potential of DW-MRI for the management of soft tissue sarcomas. Our method is based on solving the anisotropic Eikonal equation with diffusion tensor data as geometry encoding input.
We conducted a pilot imaging study by recruiting 10 healthy volunteers and acquiring DW-MR images of the left and right thigh. The aim of the study was to explore acquisition protocol parameters to ensure the best balance between image quality and acquisition time. We quantified the variability of tissue anisotropy in the human femur derived from diffusion tensor and investigated the impact of this variability on the modeled extent of microscopic tumor spread.
For brain tumors, although there is evidence that glioma spreads preferentially along white matter tracts, current clinical practice defines the microscopic tumor spread limit under the assumption of isotropic spread in brain tissue. In my presentation, I will show the tumor spread boundary calculated from retrospectively acquired DW-MRI data of glioma patients.

Short Bio

I am a researcher in medical image processing and analysis with an interest in data management. I have a broad background in computational physics, including data analysis, molecular-scale simulations, and analytical calculations. I started my scientific career in theoretical polymer physics and later moved to the biomedical field where I applied my quantitative analysis skills. At the Department of Radiation Oncology, MGH, I developed software for medical applications and gained extensive experience in algorithms for deformable image registration, which allowed me to gain a deep understanding of the analysis challenges specific to medical imaging. My recent research focuses on diffusion-weighted MRI for skeletal muscle characterization in application to soft tissue tumor spread modeling.

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