Three New Cancer Projects Receive Funding in Joint Collaboration Between Oden Institute, MD Anderson and TACC

HOUSTON and AUSTIN, TX – The University of Texas MD Anderson Cancer Center, the Oden Institute for Computational Engineering and Sciences, and the Texas Advanced Computing Center (TACC) at The University of Texas at Austin have announced funding for three cancer research projects as part of the Joint Center for Computational Oncology (JCCO).

This collaborative initiative continues to accelerate breakthroughs in oncology by combining the Oden Institute’s expertise in computational science, MD Anderson’s Institute for Data Science in Oncology’s leadership in clinical oncology and data science, and TACC’s advanced high-performance computing capabilities.

This is the sixth round of JCCO funding since the program began in 2020. So far, 25 projects have been funded and this year each project received $60,000 in seed funding, split evenly between UT Austin and MD Anderson researchers, along with 12,500 core hours on TACC systems to support large-scale computation.

Tom Yankeelov, director of the Oden Institute’s Center for Computational Oncology, and John Hazle, chair of Imaging Physics at MD Anderson, co-lead the effort.

“Our pilot project program has two primary goals: connecting clinicians with computational scientists to tackle pressing problems in cancer, and translating those efforts into clinical application,” Yankeelov said. “The three projects selected this year reflect both aims, targeting new disease settings with practical solutions that are well suited for the clinical setting.”

“Collaborations like this between UT Austin, MD Anderson, and TACC are essential to advancing cancer research,” Hazle said. “Each project exemplifies the interdisciplinary approach needed to transform innovations in computation into tangible benefits for patients. These seed funds help form the teams that scale and go on to secure additional funding to drive the innovations to the clinic.”

2025–2026 Funded Research Projects

Computational Imaging-Based Forecasting of Growth, Dissemination, and Treatment Response in High-Risk Metastatic Prostate Cancer
Led by Thomas J.R. Hughes, professor of aerospace engineering & engineering mechanics and lead of the Computational Mechanics Group at the Oden Institute, and Aradhana Venkatesan, M.D., professor of Abdominal Imaging at MD Anderson, this project will develop a computational pipeline to forecast the progression of high-risk metastatic prostate cancer. By integrating imaging with advanced modeling, the goal is to predict tumor growth and treatment response to guide clinical decision-making.

Biophysical Modeling of Head & Neck Cancer for Dose Adaptive Radiotherapy
Co-PIs Clifton Fuller, M.D., professor of Radiation Oncology at MD Anderson and David Hormuth, research scientist in the Center for Computational Oncology at the Oden Institute, will work to generate a validated, perfusion and cellularity informed dynamic digital twin of radiation response. This project will help accelerate translation of quantitative imaging and digital twin modeling into patient-specific radiation planning and has the potential to directly impact the standard of care for treatment of patients with head and neck cancers.

Lesion Image Forecasting for Early Detection in Liver Cancer
Building on previous JCCO-funded work, George Biros, professor of mechanical engineering and lead of the Parallel Algorithms for Data Analysis and Simulation Group at the Oden Institute, and Suprateek Kundu, associate professor of Biostatistics at MD Anderson, are developing computational models that predict the growth and spread of liver tumors.  This project will help generate valuable preliminary results that can subsequently be used to inform physics-based modeling incorporating mechanistic biological insights. The proposed modeling framework is flexible, interpretable, and broadly applicable across cancer types—including liver, prostate, and brain cancers. Moreover, the proposal can be adapted to predict treatment response and clinical outcomes for future timepoints, which will be considered for future funding applications involving digital twin studies.

Additional information on the Joint Center for Computational Oncology initiative and past projects can be found here.