A team from Heidelberg University Hospital and the German Cancer Research Centre has developed a new method for the automated image analysis of brain tumors. In their recent publication, 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 tumors. Thus, they make a valuable contribution to the individualized treatment of tumors. In addition, the validated method is an important first step towards the automated high-throughput analysis of medical image data of brain tumors.
Joint press release of the German Cancer Research Center and the University Hospital Heidelberg
One of the essential criteria for the precise assessment of the efficacy of a new therapy for brain tumors is the growth dynamic, which is determined by MRI. However, the manual measurement of tumor 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,” explains Martin Bendszus, Medical Director of the Department of Neuroradiology at the University Hospital in Heidelberg.
In their current 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 tumors on the basis of MRI in a standardized 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 (head: Klaus Maier-Hein) at the German Cancer Research Center and colleagues from the National Center for Tumor 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 tumor patients at Heidelberg University Hospital, the algorithms were able to automatically recognize and localize brain tumors using artificial neural networks. In addition, the algorithms were trained to volumetrically measure the individual areas (contrast medium-absorbing tumor 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,” explains Kickingereder.
The goal of the Heidelberg physicians and scientists is to use the promising technology for the standardized and fully automated assessment of the therapy response of brain tumors 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 tumors within a few minutes,” explains 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 standardized 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,” explains Wolfgang Wick.
“With this study, we were able to demonstrate the great potential of artificial neural networks in radiological diagnostics,” summarizes Philipp Kickingereder. “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 tumors but also to other diseases such as brain metastases or multiple sclerosis,” adds Klaus Maier Hein.
Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer HP, Heiland S, Platten M, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Maier-Hein KH. Automated quantitative tumor response assessment of MRI in neuro-oncology with artificial neural networks: a multicenter, retrospective study. Lancet Oncology 2019, http://dx.doi.org/10.1016/S1470-2045(19)30098-1
The German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) with its more than 3,000 employees is the largest biomedical research institute in Germany. At DKFZ, more than 1,000 scientists investigate how cancer develops, identify cancer risk factors and endeavor to find new strategies to prevent people from getting cancer. They develop novel approaches to make tumor diagnosis more precise and treatment of cancer patients more successful. The staff of the Cancer Information Service (KID) offers information about the widespread disease of cancer for patients, their families, and the general public. Jointly with Heidelberg University Hospital, DKFZ has established the National Center for Tumor Diseases (NCT) Heidelberg, where promising approaches from cancer research are translated into the clinic. In the German Consortium for Translational Cancer Research (DKTK), one of six German Centers for Health Research, DKFZ maintains translational centers at seven university partnering sites. Combining excellent university hospitals with high-profile research at a Helmholtz Center is an important contribution to improving the chances of cancer patients. DKFZ is a member of the Helmholtz Association of National Research Centers, with ninety percent of its funding coming from the German Federal Ministry of Education and Research and the remaining ten percent from the State of Baden-Württemberg.