As a recipient of the prestigious Avant Early Career Research Program scholarship, Dr. Kevin Jang applies a new technique called radiomics. We can look at brain tumors more closely and better prescribe how to treat them.
Dr. Zhang’s use of radiomics includes: machine learning model Here, a computer is trained to recognize thousands of patterns in brain tumors from MRI data, resulting in better predictions about their characteristics.
This new study may solve the problem many people face with brain tumors after radiation therapy by noninvasively determining more definitive imaging results.
High-dose radiation therapy is commonly used and successful to extend life in brain tumor patients, but irradiated brain tissue often shows ambiguous features on follow-up scans.
As a result, clinicians struggle to distinguish between radiation necrosis, an effect of radiation therapy, and actual tumors. This can have detrimental consequences, as patients may receive unnecessary interventions or delay accurate diagnosis of tumor recurrence.
“Because the imaging features of conventional MRI overlap considerably, it can be difficult to distinguish between radiation necrosis and tumor progression. Accurate diagnosis is essential as these two tissues are managed very differently,” explains Dr. Zhang.
Employing AI as part of the radiomics approachPh.D. Chan aims to Accurately distinguish between radiation-induced necrosis and recurrent brain tumors to identify the optimal response faster.
“Developing a reliable, non-invasive method to distinguish between radiation necrosis and tumor progression will allow us to better target therapeutic strategies and monitor therapeutic response.“Dr. Kevin Jang, Researcher, Radiation Oncology Registrar
https://www.nsw.gov.au/health/nbmlhd/news/stories/research-uses-ai-to-improve-outcomes-for-brain-cancer-patients Research to use AI to improve outcomes for brain tumor patients