A method used to map the surface of Mars has been adapted to help scientists measure the effects of treatments on tumours.
Mars Crater
The method, developed at the University of Manchester, uses machine learning to map features on planets to help scientists understand the errors and uncertainties of observations.
Called Linear Poisson Modelling, it works by learning patterns within data and how they can change. It can also evaluate the effects of errors in data and provide predictions of how accurate results will be. This means that fewer samples are needed to provide accurate results.
Now this method has been tested against cancer tumours on lab mice using the machine learning method. The results showed a four-fold increase in the precision of tumour change measurements that detected the beneficial effects of cancer therapies.
In tumours, it is difficult for researchers to see what effects treatments are having as different parts of tumours change at different speeds. Scientists typically need to use many samples to assess average changes in tumours, making it difficult to assess the effects of treatments.
The Manchester team, from the Division of Informatics, Imaging & Data Sciences worked in collaboration with Dr James O'Connor, head of Imaging within the Manchester Cancer Research Centre on studies of lab mice.
Dr Neil Thacker, from the University’s Division of Informatics, Imaging & Data Sciences, said:
“The results of this study show that we can present findings which researchers can be much more certain of. This means you can get the same quality of data from one sample instead of 16.”
This has important implications for research, meaning that instead of using 16 mice, in some studies only one is needed. This could help reduce the use of lab mice in medical research. It also opens up the potential for this technique to be used in patients by quickly and confidently identifying if drugs are having a specific effect on their tumours.”
Dr Paul Tar, who co-developed the method, added: “This technique is all about making the most of ‘small data’, which is common in medical studies where it is difficult to obtain large numbers of samples. Researchers use charitable or public money, so it is important that they use it in the most efficient way possible, something which this technique allows.”
Dr James O’Connor, a Cancer Research UK advanced clinician scientist, said: “Every person’s cancer is unique, which can make treating the disease challenging as a drug that works for one patient might not work for someone else. That’s why we’re increasingly looking at finding new ways to make treatment more personal, and this innovative work could be a step towards that goal. The next step will be further research to find out if that’s the case, and to help uncover this method’s potential.”
