IMAGE: This is an example spatial distributions of ADC values in selected tumors. Visually, HCT116 tumors are more complex and variable than LoVo tumors. view more
Credit: Paul Tar
A machine learning approach for assessing images of the craters and dunes of Mars, which was developed at The University of Manchester, has now been adapted to help scientists measure the effects of treatments on tumours.
Because tumours are not uniform and different parts of them change at varying speeds, it is difficult for researchers to see what effects their treatments are having against a background of changes that would happen anyway.
Typically, to obtain meaningful results scientists have to look at average changes in tumours using many samples, often in animals. With conventional statistical methods, it can be difficult to assess the effects of treatment on individuals, as would be required for personalised medicine.
The machine learning technique was developed at Manchester to help planetary scientists map features on planets such as Mars. It was designed to better understand the errors and uncertainties of observations, thereby enabling researchers to present their findings with confidence.
The Manchester team, from the Division of Informatics, Imaging & Data Sciences worked in collaboration
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