Research

Pre-Clinical: Cognitive Decline & Radiation Therapy

ImagineQuant’s preclinical division is dedicated to generating a deeper understanding of the neural and psychological effects of radiation exposure during brain cancer treatment. Our studies of rodent models employ various in vivo and in vitro analytical techniques, such as SV2A PET and autoradiography, to quantify changes in synaptic density and further elucidate the pathomechanisms of cognitive decline.

Clinical: Imaging Based Quantitative Measurement of PD-L1

An important clinical problem today is selection of patients for treatment with immune checkpoint inhibitor (ICI) therapy, which is effective in only about 20% of patients with metastatic disease. Currently, one of the biomarkers that predicts patient response to ICI, is immunohistochemistry-based measurement of PD-L1 levels, which is limited due to invasive nature of biopsy, inability to biopsy multiple lesions in patients with widely metastatic disease, and sampling error. We propose to develop a protocol for imaging based quantitative measurement of PD-L1 using a novel PET tracer and to validate this method against immunohistochemistry in resected primary and metastatic lesions.

Yale Glioma and Brain METS Databases

The majority of the literature about AI studies in neuro-oncology uses databases that consist of several hundred of patient images. As a team, we have developed and annotated a database of gliomas and brain metastases from the Yale Tumor Registry with available outcomes of survival and molecular profile. We are strong believers of Open Science and are involved in multiple collaborations to advance research through responsible sharing.

Development of Informatics Tools

Our research focuses on the development and clinical translation of auto-segmentation and feature extraction tools for neuro-oncological MRI. As a team with Visage Imaging, we have developed a relational database repository that links images with FHIR data from electronic medical record (EMR), PACS integrated deep learning algorithms for segmentation of brain metastases and gliomas and made these algorithms available on clinical Visage PACS interface. This allows direct implementation of AI tools into clinical practice for ease of use, improvement of workflows, precision medicine, and building of annotated datasets for development and optimization of novel AI algorithms.