Massachusetts Institute of Technology (MIT) researchers have discovered a potential solution to improve glioblastoma (GBM) treatment that integrates artificial intelligence (AI) and machine-learning technology.
MIT researchers will present the model at a US healthcare conference to discuss the potential to employ AI to administer effective and non-toxic chemotherapy and radiotherapy treatments for glioblastoma patients.
The new technique involved simulated trials of 50 patients and integrated AI to study patient data. The machine-learning technique will employ a reinforced learning approach.
The model incorporates AI to investigate current treatments and adjust dosage to offer an effective treatment plan. Researchers carried out simulated trials of 50 patients to determine effectivity which involved randomly selecting traditionally treated glioblastoma patients.
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MIT's Media Lab principal investigator Pratik Shah commented: "We kept the goal, where we have to help patients by reducing tumor sizes but, at the same time, we want to make sure the quality of life – the dosing toxicity – doesn’t lead to overwhelming sickness and harmful side effects."
In addition to the simulated trials, according to an MIT news release, for each patient studied, 20,000 trial-and-error tests were conducted to formulate regimens based on constraints the researchers provided.
"We said [to the model], 'Do you have to administer the same dose for all the patients?' And it said, 'No. I can give a quarter dose to this person, half to this person, and maybe we skip a dose for this person.' That was the most exciting part of this work, where we are able to generate precision medicine-based treatments by conducting one-person trials using unorthodox machine-learning architectures," Shah added.
According to the US National Cancer Institute, GBM is the most aggressive malignant primary brain tumor, with an incident rate of 3.19 per 100,000 people affected in the US and a median age of 64 years.