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New AI technique to make cancer treatment less toxic

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In one of the path-breaking inventions, MIT researchers have developed novel machine-learning techniques which will be beneficial to treat cancer patients.

The team of researchers, including one of Indian-origin has developed techniques to improve the quality of life for such patients by reducing toxic chemotherapy and radiotherapy dosing for an aggressive form of brain cancer.

Glioblastoma is a malignant tumour that appears in the brain or spinal cord, and the prognosis for adults is no more than five years. Patients are generally administered maximum safe drug doses to shrink the tumour as much as possible, but they still remain at risk of debilitating side effects.

The new “self-learning” machine-learning technique could make the dosing regimen less toxic but still effective.

“We kept the goal where we have to help patients by reducing tumour 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,” said Pratik Shah, principal investigator from the Massachusetts Institute of Technology (MIT) in Boston, US.

The findings will be presented at the 2018 Machine Learning for Healthcare conference at Stanford University in California, US. In simulated trials of 50 patients, the model comprising of artificially intelligent “agents”, designed treatment cycles that reduced the potency to a quarter or half of nearly all the doses while maintaining the same tumour-shrinking potential.

“If all we want to do is reduce the mean tumour diameter, and let it take whatever actions it wants, it will administer drugs irresponsibly,” Shah said.

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