University of Texas at Austin


Prostate, Mammography Analysis & Imaging Methods to be Tackled in Latest Texas Computational Oncology Collaboration

By Aira Balasubramanian

Published Oct. 24, 2023

The past two hundred years have borne witness to critical advancements in cancer diagnosis and treatment by blending medical expertise with the cutting edge of scientific research. Computational science has become a key tool in continuing this progress as scientists and clinicians continue to work to reach patients with unmet needs. The Joint Center for Computational Oncology (JCCO), a collaboration launched in 2020 between The University of Texas MD Anderson Cancer Research Center, The Texas Advanced Computing Center (TACC), and The Oden Institute of Computational Sciences and Engineering, aims to blend oncological data with computational research in order to fund and support projects aiming to use computational technology to accelerate the  fight against cancer. 

This year marks the fourth round of seed funding. The JCCO has funded 3 new projects which bring collaborators from research units across MD Anderson and UT Austin together.

Introducing the 2023-2024 JCCO Oncological Data and Computational Sciences Grant Recipients:

Imaging-Based Forecasting of Prostate Cancer Histopathology and Progression during Active Surveillance

This project is spearheaded by Thomas J. Hughes, Lead of the Computational Mechanics Group at the Oden Institute, and MD Anderson’s Aradhana Venkatesan, who serves as Director of Translational Research for the Department of Abdominal Imaging. Their work seeks to minimize under and over treatment of prostate cancer using mathematical modelling of patient specific MRI data that analyzes tissues as cancers grow. 

Development of a processing pipeline for automated longitudinal mammography analysis in a large prospective breast cancer screening cohort

This project aims to develop a comprehensive, computerized system for rapid mammography analysis. The technology hopes to improve the accuracy and specificity with which breast cancers can be diagnosed by using longitudinal mammography and clinical reports analyzed by deep learning and natural language processing.  This project is led by Olena Weaver, Breast Imaging Director of Contrast Enhanced Mammography at MD Anderson, and Edward Castillo, Associate Professor of Biomedical Engineering and Affiliated Faculty Member at the Oden Institute. 

Safe, Accurate Assessment of Treatment Response via Dynamic Contrast Enhanced Multispectral Optoacoustic Tomography Imaging of Tumor Perfusion

Tumor perfusion, which refers to the passage of blood and fluid through a tumor, can be of critical importance in determining the efficacy of specific cancer therapies. Mark David Pagel, Director of MD Anderson’s Contrast Agent Molecular Engineering Laboratory (CAMEL) and Umberto Villa, research scientist at the Oden Institute’s Center for Predictive Engineering and Computational Sciences, aim to develop robust optical imaging techniques to measure blood flow. This serves as a potential treatment tool  across a wide range of cancers. By blending multispectral and optoacoustic imaging methods, their project aims to reduce imaging risk while enhancing the predictive accuracy of treatment analysis. 

Thomas Yankeelov, Director of the Oden Institute’s Center for Computational Oncology, serves as the scientific lead on this collaboration along with John Hazle, Chairman of MD Anderson’s Department of Imaging Physics. Yankeelov expressed enthusiasm for this year’s cohort of funded projects, and their “excellent chance of yielding clinically actionable predictions, and represent a nice combination of translational and clinical research that we are excited to support.” 

These projects excellent chance of yielding clinically actionable predictions, and represent a nice combination of translational and clinical research that we are excited to support.

— Thomas Yankeelov

The JCCO has also continued support for 2 previous seed projects over the ‘23-’24 cycle.

Single-cell network-based transfer learning model for designing precision medicine in colorectal cancer

By using pre-trained  machine learning models, Stephen Yi, Director of the Bioinformatics and Developmental Therapeutics Lab at Dell Medical School and Oden Institute Affiliated Faculty member, and Scott Kopetz, MD Anderson’s Chair for Translational Medicine in GI Medical Oncology, are working to design personalized medicine solutions for colorectal cancers. 

Rapid, motion-robust MRI for fast and affordable prostate cancer screening and surveillance,

In an effort to make prostate MRI’s faster and more accurate, reducing the rate of unnecessary biopsies, Ken-Pin Hwang of MD Anderson’s Department of Imaging Physics and Jon Tamir of UT Austin’s Department of Electrical and Computer Engineering and the Oden Institute will blend mathematical modeling and massively parallel distributed computing.

Each new seed project receives grant funding of $50,000, split between both MD Anderson and UT Austin. Computational elements of each project will be performed on TACC’s high performance computing platforms. Ernesto Lima, a research associate at the Oden Institute’s Center for Computational Oncology, will support researchers with the implementation of these project elements.