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.”
“This study is a wonderful example of how artificial intelligence can be used to develop new personalized treatments for the benefit of society.”
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.
