New software is under development that doctors hope will help them identify brain tumours in children that will grow aggressively.
Some brain tumours in children remain benign and doctors choose not to operate. But a small percentage of those will suddenly start to grow aggressively.
Doctors have not identified what triggers that aggressive tumour growth, despite the vast array of data they hold on their child patients - demographic, environmental, genetic and clinical data, as well as images such as MRI and CAT scans of the developing tumours.
But a new software tool called AITION can integrate all the medical data from a tumour patient and then analyse it to calculate the probable factors that are stimulating tumour development, combining up to 30 correlated variables. AITION provides an overview of the causal relationship across all factors.
Graphical network of causal relationships
AITION's conclusions are displayed as a 'knowledge model', a graphical network of medical factors with links that represent the correlations between them. Strongly interdependent concepts are directly connected, loosely dependent concepts are not connected at all. The patient's doctors can play around with the knowledge model. They can improve the model by adding information they know to be true about the patient. They can use the model to test the likely effects of different types of medication, surgery or treatments on the tumour's growth and the patient's health.
"We have shown the knowledge models to doctors treating brain tumours, juvenile idiopathic arthritis, [as well as] to cardiologists and they have found it quite intuitive," says Harry Dimitropoulos, one of the researchers from the University of Athens where AITION is being developed as part of the EU-funded Health-e-Child project.
"Because of the graphical way it presents the data they have found it easy to click on the links. Some training is required if they want to look in depth at how conclusions were reached, or to modify the statistics or the graph."
The causal-probabilistic algorithms within AITION are well established, solid and reliable, according to Dr Dimitropoulos.
However, because the diseases are rare, data is available on only small numbers of children. An AITION test on juvenile idiopathic arthritis had only 50 patients initially. That has been expanded to 200 and the tool is becoming more stable and more reliable.
AITION's logic can lead to mistakes. For instance, if most of the patients over 16 years old in a knowledge model are also smokers, AITION may infer that being a smoker causes one's age to be over 16. To try to eliminate that kind of error, AITION uses a priori knowledge encapsulation (grouping variables in hierarchies) to constrict the possible conclusions that can be drawn from the data.