APRIL 18, 2023 — Generative AI is giving researchers a clearer picture when it comes to targeting cancer treatments.
In a collaborative effort, researchers from UTSA, UT Health San Antonio and the University of Pittsburgh are studying the use of AI for adaptive radiotherapy, with the hope it can improve and replace the current practice that clinicians use to review images and treat a tumor.
“This is a multidisciplinary research project that includes multiple faculty members who have come together with a different skillset – AI, data analytics, and health care – to solve a challenge,” said Paul Rad, UTSA associate professor with joint appointment in the Department of Computer Science and the Alvarez College of Business. “Our study aimed to analyze treatment doses administered and develop a precise map of a patient's cancer progression while accounting for potential variability using uncertainty estimation."
Patients undergoing radiotherapy are currently given a computed tomography (CT) scan to help physicians see where the tumor is on an organ, for example a lung. A treatment plan to remove the cancer with targeted radiation doses is then made based on that CT image.
Rad says that cone-beam computed tomography (CBCT) is often integrated into the process after each dosage to see how much a tumor has shrunk, but CBCTs are low-quality images that are time-consuming to read and prone to misinterpretation.
UTSA researchers used domain adaptation techniques to integrate information from CBCT and initial CT scans for tumor evaluation accuracy. Their Generative AI approach visualizes the tumor region affected by radiotherapy, improving reliability in clinical settings.
This improved approach enables physicians to more accurately see how much a tumor has decreased week by week and to plan the following weeks’ radiation dose with greater precision. Ultimately, the approach could lead clinicians to better target tumors while sparing the surrounding critical organs and healthy tissue.
Nikos Papanikolaou, a professor in the Departments of Radiation Oncology and Radiology at UT Health San Antonio, provided the patient data that enabled the researchers to advance their study.
“UTSA and UT Health San Antonio have a shared commitment to deliver the best possible health care to members of our community,” Papanikolaou said. “This study is a wonderful example of how artificial intelligence can be used to develop new personalized treatments for the benefit of society.”
The American Society for Radiology Oncology stated in a 2020 report that between half or two-thirds of people diagnosed with cancer were expected to receive radiotherapy treatment. According to the American Cancer Society, the number of new cancer cases in the U.S. in 2023 is projected to be nearly two million.
Arkajyoti Roy, UTSA assistant professor of management science and statistics, says he and his collaborators have been interested in using AI and deep learning models to improve treatments over the last few years.
“Besides just building more advanced AI models for radiotherapy, we also are super interested in the limitations of these models,” he said. “All models make errors and for something like cancer treatment it’s very important not only to understand the errors but to try to figure out how we can limit their impact; that’s really the goal from my perspective of this project.”
The researchers’ study included 16 lung cancer patients whose pre-treatment CT and mid-treatment weekly CBCT images were captured over a six-week period. Results show that using the researchers’ new approach demonstrated improved tumor shrinkage predictions for weekly treatment plans with significant improvement in lung dose sparing. Their approach also demonstrated a reduction in radiation-induced pneumonitis or lung damage up to 35%.
“We’re excited about this direction of research that will focus on making sure that cancer radiation treatments are robust to AI model errors,” Roy said. “This work would not be possible without the interdisciplinary team of researchers from different departments.”
The joint research, titled “CBCT-guided Adaptive Radiotherapy using Self-Supervised Sequential Domain Adaptation with Uncertainty Estimation” will be published in the Medical Image Analysis journal, a peer-reviewed academic journal which focuses on medical and biological image analysis.
Collaborators included Nima Ebadi with the UTSA Department of Electrical and Computer Engineering; Ruiqi Li, a Ph.D. student in the UT Health San Antonio Radiological Science Program; and Arun Das with the University of Pittsburgh Department of Medicine. Both Rad and Roy are core faculty members at the new UTSA School of Data Science in Downtown San Antonio.
UTSA Today is produced by University Communications and Marketing, the official news source of The University of Texas at San Antonio. Send your feedback to news@utsa.edu. Keep up-to-date on UTSA news by visiting UTSA Today. Connect with UTSA online at Facebook, Twitter, Youtube and Instagram.
Covidence is a systematic & scoping review tool used to streamline the process of screening and reviewing articles. Using this software, research teams can easily import studies, perform automatic deduplication, and extract data using templates. This workshop will show attendees how to start a review in Covidence, add collaborators, and get started on screening.
Virtual (Zoom)In this workshop, attendees will be introduced to Pandas, a Python tool for working with data easily. It makes it simple to organize and analyze information when data is organized and categorized, like spreadsheets or tables.
Group Spot B, John Peace LibraryEach fall and spring semester, students convene at the Main Campus at UTSA with booths, ideas and prototypes. A crowd of judges, local organizations, students, faculty and sponsors walk around and talk to the students about their projects and ask questions. Students get the real-life experience of "pitching" their project with hopes of getting funding or support to move to the next level.
UTSA Convocation Center, Main CampusJoin the doctoral candidates for the Doctoral Conferreal Ceremony and celebrate their accomplishments.
Arts Building Recital Hall, Main CampusCelebrate the graduates from the Carlos Alvarez College of Business, College of Education and Human Development, Margie and Bill Klesse College of Engineering and Integrated Design and University College.
AlamodomeCelebrate the graduates from the College for Health, Community and Policy, College of Liberal and Fine Arts and College of Sciences.
AlamodomeThe University of Texas at San Antonio is dedicated to the advancement of knowledge through research and discovery, teaching and learning, community engagement and public service. As an institution of access and excellence, UTSA embraces multicultural traditions and serves as a center for intellectual and creative resources as well as a catalyst for socioeconomic development and the commercialization of intellectual property - for Texas, the nation and the world.
To be a premier public research university, providing access to educational excellence and preparing citizen leaders for the global environment.
We encourage an environment of dialogue and discovery, where integrity, excellence, respect, collaboration and innovation are fostered.