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AI predics metastasis outcomes better than human eye

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An innovative AI technique developed by researchers at York University in Toronto, Canada is considerably more effective than the human eye at predicting therapy outcomes in patients with brain metastases.

The scientists hope the new research and technology could eventually lead to more tailored treatment plans and better health outcomes for cancer patients.

York Research Chair Ali Sadeghi-Naini, associate professor of biomedical engineering and computer science in the Lassonde School of Engineering, and study lead, said:

“This is a sophisticated and comprehensive analysis of MRIs to find features and patterns that are not usually captured by the human eye.

“We hope our technique, which is a novel AI-based predictive method of detecting radiotherapy failure in brain metastasis, will be able to help oncologists and patients make better informed decisions and adjust treatment in a situation where time is of the essence.”

Brain metastases develop when primary cancers in the lungs, breasts, colon or other parts of the body are spread to the brain via the bloodstream or lymphatic system.

While there are various treatment options available, stereotactic radiotherapy is one of the more common, with treatment consisting of concentrated doses of radiation targeted at the area with the tumour.

Previous research has shown that using standard practices, such as MRI imaging, oncologists are able to predict treatment failure about 65 per cent of the time.

In the new study, researchers created and tested several AI models and their best one demonstrated an 83 per cent accuracy.

Sadeghi-Naini said:

“Not all of the tumours respond to radiation — up to 30 per cent of these patients have continued growth of their tumour, even after treatment.

“This is often not discovered until months after treatment via follow-up MRI.”

This delay is time patients with brain metastases cannot afford, as it is a particularly debilitating condition with most people succumbing to the disease within three months to five years of diagnosis.

“It’s very important to predict therapy response even before that therapy begins,” Sadeghi-Naini added

Using a deep learning technique, the researchers created artificial neural networks trained on a large pool of data, then taught the AI to pay more attention to specific areas.

Sadeghi-Naini said:

“When you look at an MRI, you see areas within or surrounding the tumour where the intensity and pattern is different, so you attend to those parts with your vision system more.

“But an AI algorithm is blind to this.

“The attention mechanism we incorporated into the algorithm helps these AI tools to learn which part of these images are more important and put more weight on that for analysis and prediction.”

The researcher says that while more research needs to be done, the findings point to AI being a potentially significant tool in precision management of brain metastasis and even other types of cancer down the line.

The next step to adopting this as a clinical practice would be to look at a larger cohort with a multi-institutional data set.

A clinical trial could then be developed.

Sadeghi-Naini said:

“If standard treatments can be tailored for patients based on their response to treatments – that can be predicted before treatment even starts – there’s a good chance that the overall survival of the patients can be improved.”

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