A team of researchers have developed a new method for the automated image analysis of brain tumours.

In their publication published in The Lancet Oncology, the authors show that machine learning methods carefully trained on standard magnetic resonance imaging (MRI) are more reliable and precise than established radiological methods in the treatment of brain tumours.

Thus, they make a valuable contribution to the individualised treatment of tumours.

In addition, the validated method is an important first step towards the automated high-throughput analysis of medical image data of brain tumours.

Gliomas are the most common and most malignant brain tumours in adults.

In Germany, approximately 4,500 people are diagnosed with a glioma every year.

The tumours often cannot be completely removed by surgery.

Chemotherapy or radiotherapy have limited effectiveness as tumour are highly resistant.

New and precisely validated treatment approaches are therefore urgently needed.

One of the essential criteria for the precise assessment of the efficacy of a new therapy for brain tumours is the growth dynamic, which is determined by MRI.

However, the manual measurement of tumour expansion in two planes in the contrast-enhanced MRI scans is prone to errors and leads to slightly different results.

"This can have a negative effect on the assessment of therapy response and hence the reproducibility and precision of scientific statements based on imaging," explained Martin Bendszus, Medical Director of the Department of Neuroradiology at the University Hospital in Heidelberg.

In the study, doctors and scientists from the University Hospital of Heidelberg and the German Cancer Research Center (DKFZ) describe the huge potential of machine learning methods in radiological diagnostics.

The team has developed neuronal networks in order to assess and clinically validate the therapeutic response of brain tumours on the basis of MRI in a standardised and fully automated way.

A team led by Philipp Kickingereder from the Department of Neuroradiology at Heidelberg University Hospital, researchers from the Division of Medical Image Processing at the German Cancer Research Center and colleagues from the National Center for Tumour Diseases (NCT) and the Neurological Department of the University Hospital Heidelberg (Medical Director: Wolfgang Wick) worked together on this project.

Using a reference database with MRI scans of almost 500 brain tumour patients at Heidelberg University Hospital, the algorithms were able to automatically recognise and localise brain tumours using artificial neural networks.

In addition, the algorithms were trained to volumetrically measure the individual areas (contrast medium-absorbing tumour portion, peritumoral edema) and to precisely assess the response to therapy.

The results were validated in cooperation with the European Organization for Research and Treatment of Cancer (EORTC).

"The evaluation of more than 2,000 MRI scans of 534 glioblastoma patients from all over Europe shows that our computer-based approach allows a more reliable assessment of therapy response than the conventional method of manual measurement. We were able to improve the reliability of the assessment by 36 percent. This can be crucial for the image-based assessment of therapy efficacy in clinical trials. The prediction of overall survival was also more precise with our new method," explained Kickingereder.

The goal of the Heidelberg physicians and scientists is to use the promising technology for the standardised and fully automated assessment of the therapy response of brain tumours as quickly as possible in clinical studies and, in future, also in clinical routine.

In addition, the researchers designed and evaluated a software infrastructure that enables the complete integration of the new technique into existing radiological infrastructure.

"In this way, we are creating the prerequisites for broad application and fully automated processing and analysis of MRI scans of brain tumours within a few minutes," added Klaus Maier-Hein.

The new technology is currently being re-evaluated at the NCT Heidelberg as part of a clinical study to improve the treatment of glioblastoma patients. "For precision therapies, a standardised and reliable assessment of the effectiveness of the new treatment approaches is of outstanding importance. The technology we have developed may be able to make a decisive contribution here," explained Wolfgang Wick.

"With this study, we were able to demonstrate the great potential of artificial neural networks in radiological diagnostics," Philipp Kickingereder summarised.

"In the future, we want to advance the technology for automated high-throughput analysis of medical image data and transfer it not only to brain tumours but also to other diseases such as brain metastases or multiple sclerosis," concluded Klaus Maier Hein.

Source: German Cancer Research Center (DEUTSCHES KREBSFORSCHUNGSZENTRUM, DKFZ)