Joseph O. Deasy, PhD

This work presents a public project on the Cancer Genomics Cloud (CGC) platform for reproducible, out-of-the-box application of analysis workflows for pre-trained radiological and radiotherapy AI models; the first and only such project supporting analysis of...

Tumor hypoxia drives radio-resistance in Head & Neck cancer (HNC) and can guide radiation dose de-escalation. Tumor hypoxia can be imaged using 18F-fluoromisonidazole (FMISO-PET; non-standard-of-care). Because hypoxia drives glycolysis, we assessed correlatio...

Early assessment of radiotherapy response is essential for adaptive treatment planning and digital-twin development. Longitudinal quantification of tumor volume and mass changes depends on reliable deformable image registration (DIR), which remains challengin...

Dynamic Contrast-Enhanced (DCE) MRI provides functional information on tumor vascularity and perfusion, useful in characterizing tumors and gauging treatment response. We introduce an open-source extension to pyCERR for extracting non-parametric features of c...

To identify blood-bearing organs whose mean blood dose is significantly associated with OS in non-small cell lung cancer (NSCLC), and to evaluate the feasibility of sparing these organs during treatment planning.

Despite extensive research on automated treatment planning, manual trial-and-error optimization remains common in clinical practice. Knowledge-based and AI-driven approaches show promise but often lack robustness to evolving clinical protocols due to the need...

Differentiating radiation necrosis (RN) from tumor recurrence (TR) after radiotherapy remains challenging using conventional MRI. We investigated dynamic contrast-enhanced MRI (DCE-MRI) analysis using unbalanced regularized optimal mass transport (urOMT) to q...

To quantify and visualize tumor microenvironment transport behavior in longitudinal breast DCE-MRI acquired during neoadjuvant chemotherapy (NACT), and to develop image-based biomarkers for predicting therapeutic response