Funding resource: NIH/NCI R01 CA233487
The long-term goal of this project is to overcome barriers related to prediction uncertainties and human-computer interactions, which are currently limiting the ability to make personalized clinical decisions for real-time response-based adaptation in radiotherapy from available data.
Funding resource: NIH/NCI R37 CA222215
To develop an evaluated an integrated tomographic feedback system that uses X-ray acoustics (XACT) and advanced ultrasound (US) images to monitor a patient’s present status during radiotherapy delivery.
Funding resource: NIH/NCI R41 CA243722
Sponsor: Endectra LLC
In this STTR proposal, Endectra will work with oncology researchers at Moffitt to develop and evaluate a novel Cerenkov Multi-Spectral Imaging (CMSI) technique using new solid state on-body probes to conduct routine optical measurements of radiation dose and molecular imaging during cancer radiotherapy delivery. This approach is expected to provide more accurate tumor physiological representation and dose adaptation during treatment, reduce overall patient exposure to radiation, and allow for ongoing assessment of tumor physiological parameters. If successful, Endectra will develop CMSI as an alternative cost saving and effective molecular imaging/targeting modality for routine radiotherapy applications, greatly improving radiotherapy outcomes and yielding a major impact on public health.
Funding Resource: University of Chicago (Prime: NIH/NBIB 75N92020D00018/75N92020F0001)
To develop machine learning tools for image-based modeling and development of a clinical decision support tool for Covid19 patient management.