Lab Members
Assessment of tumor response remains a limiting factor in evaluation of novel treatment techniques, which is critical for evidence-based medicine. Quantitative assessment of therapeutic response through anatomical, functional, and metabolic imaging has been identified by the NIH has a critical component in the advancement of local therapies. The testing, validation, and translation of image-based assessments have been hampered by the inability to accurately link the serially acquired images indicating response over time with the therapy that was delivered. My research has shown that biomechanical model-based deformable registration can map anatomy between multi-modality images obtained over a relatively short time frame, accounting for anatomical motion. However, the complex, longer-term changes in shape and volume due to non-mechanical forces such as differential response of normal tissues to irradiation are not addressed by force-based mechanics applied to anatomical models to guide deformation. In addition, these complex changes challenge the ability to combine local therapy previously delivered with that intended to be delivered, such as in the chronic management of liver cancer. These uncertainties related to complex changes often result in conservative management of patient treatments, potentially reducing the effective management of the cancer, and increased uncertainties in the correlation of treatment with assessment of outcome. The development of biomechanical models, currently under investigation in my lab, to describe the volumetric and spatial response of an organ to a local therapy will enable the improvement of correlating novel imaging, currently being developed by John Hazle, Ph.D., Osama Mawlawi, Ph.D. and Jason Stafford, Ph.D. in Imaging Physics, with local therapy in radiation oncology, interventional radiology, and image-guided surgery.
Current Members
Rajarajeswari Muthusivarajan, Ph.D.
Postdoctoral Fellow
Raji was a T32 Cancer Nanotechnology Postdoctoral Fellow from 2018 to 2020. Her research interests are molecular dynamics simulations and medical image segmentation based on deep learning techniques.
Mohamed Eltaher, M.D.
Research Assistant II
Mohamed is a medical graduate who joined the Fuentes Lab in the fall of 2022. His areas of interest are image segmentation, manuscript writing and data collection.
Kareem Wahid, Ph.D.
Postdoctoral Fellow
Kareem is currently an Image Guided Cancer Therapy T32 Postdoctoral Fellow (2023-2025). He is an M.D.-Ph.D. trainee affiliated with The University of Texas Medical Scientist Training Program at Houston. His research focuses on human-machine interaction for AI-based medical imaging tools, predominantly in radiotherapy applications.
Mahesh Nandyala, Ph.D.
Postdoctoral Fellow
Mahesh has been a joint postdoctoral fellow at MD Anderson Cancer Center (Fuentes Lab) and UT Austin (Yankeelov Lab) since July 2023. His research interest is computational oncology, focusing on numerical modeling of cancer treatments such as neoadjuvant treatments (NAT) and nanoparticle-assisted thermal therapies. Currently, he is working on the computational prediction of magnetic hyperthermia treatment in rodent models with Dr. Fuentes and the optimization of breast cancer NAT using MRI-informed patient data with Dr. Yankeelov.
Awj Twam
Assistant Data Scientist
Awj received her B.S. in biomedical engineering from The University of Texas at San Antonio. She has gained some research experience in data analysis and her area of interests are in machine-learning methods in cancer care and translational scientific research.
Bilyana Tzolova
Graduate Student
Bilyana started as a Ph.D. student in the department of Computational and Applied Mathematics at Rice University in the fall of 2019. She is co-advised by Dr. David Fuentes and Dr. Beatrice Riviere. Her interests lie in image segmentation, deep learning and biomedical informatics. She is currently working to more effectively model the hepatic vascular system using computational and deep learning methods.
Alex "Cito" Balsells
Graduate Student
Cito is a Ph.D. student in the department of Computational Applied Mathematics and Operations Research at Rice University. Through his fellowship from the National Library of Medicine in Biomedical Informatics and Data Science, he is advised by Dr. Beatrice Riviere and Dr. David Fuentes. His interests lie at the intersection of clinical practice and machine learning based image segmentation.
Adrian Celaya
Graduate Student
Adrian is currently pursuing his Ph.D. in the Department of Computational and Applied Mathematics at Rice University, located in Houston, Texas. His advisors are Drs. Beatrice Riviere and David Fuentes. His research is centered around creating innovative methods for 3D medical image segmentation and scientific machine learning. Prior to this, he was the information System Security Manager on the U.S. Navy's aircraft carrier USS Carl Vinson. Adrian joined The University of Texas MD Anderson Cancer Center as a research assistant in the Department of Imaging Physics after leaving the Navy in August 2020.
Rohan Amare, Ph.D.
Postdoctoral Fellow
Rohan holds a Ph.D. in mechanical engineering, specializing in bioheat transfer and biophysics. With a keen interest in computational modeling, he uses medical imaging data to simulate blood flow and mass transport. His current research goal is to develop predictive frameworks for specific cancer treatments.
Zaina Boriek
Postbaccalaureate Trainee
Zaina is a postbaccalaureate trainee in the Fuentes Lab, specializing in machine learning applications in medical imaging and clinical oncology. Her research focuses on developing and validating predictive models for healthcare outcomes, such as predicting tumor volume and assessing disease risk. Zaina integrates demographic, clinical, and imaging data to enhance healthcare AI, with a strong emphasis on machine learning to ensure equitable predictions for diverse patient populations. Zaina earned a B.S. in Psychology from the University of Houston in 2023 and intends to pursue medical training.