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AI is helping to make treatment for cancer more bearable

Researchers from MIT are using artificial intelligence to make treatment for cancer less debilitating but just as effective for patients.

The AI learns from historical patient data to determine what the lowest doses and frequencies of medication delivered the desired results to shrink tumours. In some cases, the monthly administration of doses was reduced to just twice per year while achieving the same goal. Based on a trial of fifty patients, treatments were reduced to between a quarter and half of the prior doses.

Pratik Shah, Principal Investigator at MIT Media Lab, says:

“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.”

Some of the side effects of cancer medication can do more harm than good to a patient’s quality of life. By implementing the AI’s treatment strategy, the least toxic doses can be used. The current model focuses on glioblastoma treatment.

Glioblastoma is the most aggressive form of brain cancer, although it can also be found in the spinal cord. It’s more commonly found in older adults but can impact any age. Sufferers are often given a life expectancy of up to five years. Doctors often administer the maximum safe dosages to shrink tumours as much as possible, but with side effects that can impact a patient’s quality of life over that period.

In a press release, MIT said: “The researchers’ model, at each action, has the flexibility to find a dose that doesn’t necessarily solely maximize tumour reduction, but that strikes a perfect balance between maximum tumour reduction and low toxicity.”

“This technique has various medical and clinical trial applications, where actions for treating patients must be regulated to prevent harmful side effects.”

Reinforced learning was used for the model whereby the AI seeks ‘rewards’ and wants to avoid ‘penalties’ so it optimises all of its actions. The model started by determining whether to administer or withhold a dose. If administered, whether a full dose or just a portion is necessary. A second clinical model is pinged each time an action is taken in order to predict the effect on the tumour.

In order to prevent just giving frequent maximum dosages each time – the researchers’ AI received a penalty whenever it handed out full doses, or a medication too often. Without the penalty in place, the results were very similar to a treatment regime created by humans. With the penalties, the frequency and potency of the doses were significantly reduced.

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