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Grant Will Help Yale Team Develop Diagnostic Tool to Evaluate Tumors

December 01, 2022

The Yale Center for Clinical Investigation (YCCI) has awarded a research pilot award to an interdisciplinary team of Yale researchers for a project titled, “Utilizing Machine Learning to Model the Volumetric Growth of Vestibular Schwannomas.”

The two-year $50,000 grant, funded through the 2022 YCCI Translational and Interdisciplinary Research Pilot Program, was awarded to Daniel Wiznia, MD, assistant professor of orthopaedics and rehabilitation; Frank Buono, PhD, associate research scientist in psychiatry; and Steven Tommasini, PhD, research scientist in orthopaedics and rehabilitation. Other Yale collaborators include Asher Marks, MD; Annie Wang, MD; Justin Berry, MA; and Lisa Lattanza, MD.

“With this award we will create a novel AI 3D diagnostic tool for clinicians to evaluate vestibular schwannoma tumors,” Wiznia said. “This will be a game changer in brain tumor treatment.”

A defining characteristic of Neurofibromatosis type II (NF2) is the presentation of vestibular schwannoma (VS) tumors. While benign, VS tumors may impinge on vital structures such as cranial nerves causing morbidity or pressing on the brainstem, which can be life-threatening. It is clinically important to accurately measure and evaluate the growth of VS tumors, especially for neurologists, neurosurgeons, and radiation oncologists to better understand the regions and rates of growth and structures that are at risk.

Magnetic resonance imaging (MRI) is considered the gold-standard for assessing the size, location, and evolution of VS tumors. Traditionally, tumors are analyzed linearly in 2 dimensions on planar views, but the methodology has been demonstrated to be inaccurate. The 3-dimensional (3D) volumetric technique proposed in this research project is more sensitive in identifying tumor progression.

The 3D Medicine Collaborative integrates an interdisciplinary team between Yale School of Medicine (Wiznia, Buono, Marks, Wang, Lattanza) and the School of Engineering (Tommasini) to automate the labor-intensive process with machine learning and develop 3D diagnostics for medical providers of NF2 to visualize and quantify VS tumors in 3D. To accomplish this goal, the team will build a ground truth dataset of NF2 VS MRI based 3D models, develop an artificial intelligence algorithm to automate the rapid creation of 3D models of VS tumors, and create visualization tools to analyze growth and highlight at risk structures.

“This award is one example of how the Yale 3D Medicine Collaborative has brought together researchers across the university to tackle the treatment of complex medical challenges,” Wiznia said.

This innovative approach to personalized 3D modeling of brain tumors has the potential to significantly increase the understanding of spatial characteristics of brain tumors, and the researchers will translate these advanced tools into the clinical, academic, and surgical settings to facilitate care for these diseases. This initial project will lay the groundwork for expansion of a volumetric approach to other CNS tumors.

Submitted by Christopher Gardner on December 01, 2022