Doctors and scientists have developed an artificial intelligence tool that can accurately predict how likely tumours are to grow back in cancer patients after they have undergone treatment.
The breakthrough is described as exciting by clinical oncologists. There is still a risk that the disease will come back, despite treatment advances in recent years.
Monitoring patients after treatment is important to ensure that the cancer is not recurred. Doctors tend to rely on traditional methods to predict how a patient will fare in the future.
A model using machine-learning, a type of artificial intelligence, can predict the risk of cancer coming back, according to a study by the Institute of Cancer Research, London, and Imperial College London.
Dr Richard Lee, a consultant physician, said that this is an important step forward in being able to use artificial intelligence to understand which patients are at highest risk of cancer recurrence and to detect this relapse sooner so that re-treatment can be more effective.
The chief investigator of the study, Lee, told the Guardian that it could prove vital in not only improving outcomes for cancer patients, but alleviating their fears, with relapse being a key source of anxiety for many.
If the tool is used to detect recurrence earlier in patients at high risk, it will ensure they receive treatment more quickly, but it will also result in less follow-up scans and hospital visits for those deemed low risk.
Reducing the number of scans needed in this setting can be helpful, and also reduce radiation exposure, hospital visits, and make more efficient use of resources.
Doctors, scientists and researchers developed a machine learning model to determine if it could identify lung cancer patients at risk of recurred after treatment. Machine learning is a form of artificial intelligence.
Lung cancer is the leading cause of cancer death in the UK, accounting for 20% of all cancer deaths. Lung cancer is the most common cancer in the world, accounting for 85% of cases. In the UK, over a third of patients experience a repeat.
The researchers used clinical data from 657 patients treated at five UK hospitals to feed their model, and added in data on various factors to better predict a patient's chance of recurrence.
The patient's age, gender, body mass index, smoking status, intensity of radiotherapy, and their tumour's characteristics were included. Researchers used an artificial intelligence model to classify patients into low and high risk of recurrence, how long a period they might experience before a recurrence, and overall survival two years after treatment.
Traditional methods were found to be more accurate than the tool. The results of the study were published in a journal.
There is no set framework for the monitoring of lung cancer patients who have received radiotherapy in the UK.
This type of data can be accessed easily, so this methodology could be replicated across different health systems.
The study is an exciting first step towards rolling out a tool nationally and internationally to guide the post-treatment surveillance of cancer patients.
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