Researchers at UC San Diego have developed advanced deep learning techniques that could revolutionize treatment planning for breast cancer radiotherapy – making it faster and improving its quality. The team sought to reduce inconsistencies in treatment plans and improve patient outcomes by leveraging artificial intelligence (AI) using the Expanse system at the San Diego Supercomputer Center (SDSC), which is a pillar of the School of Computing, Information and Data Sciences (SCIDS) at UC San Diego.

“Our study focused on two innovative methods for training AI models to predict radiation doses: a ‘glowing’ mask algorithm and a gradient-weighted loss function,” said Lance Moore, a computational data science research specialist at UC San Diego Health Department of Radiation Medicine & Applied Sciences. “The glowing mask encodes distance data into treatment images while the gradient-weighted function prioritizes accuracy at the borders of the high-dose regions.”

These techniques were tested via U.S. National Science Foundation ACCESS allocations on SDSC’s Expanse using three-dimensional U-Net deep learning models trained on data from more than 300 breast cancer treatment plans.

“The results were promising as the combination of glowing masks and the gradient-weighted loss function produced the most accurate predictions – achieving a high level of precision in dose distribution,” Moore said. “For example, the error in mean dose to critical organs like the heart and lung was very small, and dose comparisons to clinical plans showed high agreement.”

Kelly Kisling, an associate professor at the UC San Diego Health Department of Radiation Medicine & Applied Sciences and principal investigator of the study, explained that the team’s findings suggest that incorporating the new AI methods into automated radiotherapy planning systems could significantly reduce the time and effort required to develop high-quality treatment plans for patients with breast cancer.

“Our work was enabled by the National Institutes of Health (NIH) as well as the San Diego Supercomputer Center and the U.S. National Science Foundation (NSF) ACCESS program,” Kisling said. “Thanks to ACCESS allocations on Expanse, we were able to create a large model using resources that are beyond the scope of an individual research lab. This large model is more accurate and precise than other comparable models – making it a major step forward in personalized cancer care.”

The study was published in the International Journal of Medical Physics Research and Practice.

Resource Provider Institution(s): San Diego Supercomputer Center (SDSC)
Resources Used: Expanse
Affiliations: UC San Diego
Funding Agency: NSF, NIH
Grant or Allocation Number(s): MED210003, UL1TR001442

The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.