AI method could help clinicians accurately identify brain tumors and radiation necrosis

While targeted radiation can be an effective treatment for brain tumors, subsequent potential necrosis of the treated areas can be hard to distinguish from the tumors on a standard MRI. A new study published today led by a York University professor in the Lassonde School of Engineering found that a novel AI-based method is better able to distinguish between the two types of lesions on advanced MRI than the human eye alone, a discovery that could help clinicians more accurately identify and treat the issues.

The study shows, for the first time, that novel attention-guided AI methods coupled with advanced MRI can differentiate, with high accuracy, between tumor progression and radiation necrosis in patients with brain metastasis treated with stereotactic radiosurgery. Timely differentiation between tumor progression and radiation necrosis after radiotherapy in brain tumors is a crucial challenge in cancer centers, since these two conditions require quite different treatment approaches."

Ali Sadeghi-Naini, York Research Chair, senior author of the paper and associate professor of biomedical engineering and computer science

The study, published in the International Journal of Radiation Oncology, Biology, Physics, was conducted in close collaboration with imaging scientists, neuro-oncologists and neuro-radiologists at Sunnybrook Health Sciences Centre using data acquired from more than 90 cancer patients whose original cancer had metastasized to the brain.

Sadeghi-Naini says the incidence of brain metastasis is rising as treatments improve and survival rates increase. Stereotactic radiosurgery (SRS), where a concentrated doses of radiation are applied to the cancer lesions only, is effective at controlling the tumors. In up to 30 per cent of cases, SRS is not able to control the tumor and it continues to grow. Where it is successful, healthy brain tissue immediately surrounding the tumor may also die off, called brain radiation necrosis, and it can come with significant side effects.

Sadeghi-Naini and his colleagues introduced a 3D deep learning AI model with two advanced attention mechanisms to differentiate between tumor progression and radiation necrosis using a specialized MRI technique, called chemical exchange saturation transfer (CEST), and found that the AI was able to differentiate between the two conditions with over 85 per accuracy. Sadeghi-Naini says with a standard MRI the two conditions are accurately diagnosed about 60 per cent of the time, and with more advanced MRI techniques alone, the rate increases to about 70 per cent.

"Differentiating tumor progression and radiation necrosis is very important - one needs more anti-cancer therapies and may need to be aggressively treated with more radiation, sometimes surgery. The other may require observation, anti-inflammatory drugs, so getting this right is crucial for patients."