2022/12/21 | Research | Artificial Intelligence

LUMIERE Dataset for Glioblastoma Released

The Medical Image Analysis group at the ARTORG Center of the University of Bern in collaboration with colleagues at Inselspital, Bern, has released the first publicly available single-center dataset of Glioblastoma Magnetic Resonance Imaging (MRI) with expert ratings. It comprises over 600 multi-sequence MRI acquisitions and expert readings of selected studies according to the response assessment in neuro-oncology (RANO) criteria, automated tumor segmentations, and a rich set of complementary information, such as advanced imaging biomarkers, patient demographics and pathology information.

Example of longitudinal GBM data. CT1/T1: T1-weighted post-/pre-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR). (https://doi.org/10.1038/s41597-022-01881-7)

Publicly accessible data is the cornerstone of research on Deep Learning for applications in Radiology. Data availability is hence critical to establishing new techniques and testing systems with external datasets. For Glioblastoma (GBM) patients, datasets predominantly include pre-operative Magnetic Resonance Imaging (MRI) or contain few follow-up images for each patient. Access to fully longitudinal datasets is therefore critical to advance the field.

Dr. Yannick Suter, senior researcher and joint first author of the study together with Dr. med. Urspeter Knecht, comments “With this work we hope to contribute towards the improvement of clinical and computer-assisted response assessment approaches, facilitate new insights into disease progression patterns with radiomics and pathology information, as well as the development of automated medical image analysis methods used by experts fighting this disease.”

The authors thank the support from the Swiss Cancer Research Foundation, the Swiss National Science Foundation, the Swiss Personalized Health Network and all colleagues at Inselspital, Bern who helped in making this initiative possible.

Study description: https://www.nature.com/articles/s41597-022-01881-7

Medical Image Analysis