Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features

• DOI: 10.1038/srep16822
• Publication Date: 2015-11-18T10:11:02Z
• Authors: Emmanuel Rios Vel...; Raphael Meier; William D. Dunn Jr; Brian Alexander; Roland Wiest; Stefan Bauer; David A. Gutman; Mauricio Reyes; Hugo J.W.L. Aerts
• Keywords: 610; Datasets as Topic; Reproducibility of Results; 610 Medicine & health; Prognosis; Magnetic Resonance Imaging; Article; Tumor Burden; 03 medical and health sciences; 0302 clinical medicine; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; 570 Life sciences; biology; Humans; Glioblastoma; Medical Informatics; Software

AbstractReproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 – 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55–0.77 and 0.65, CI: 0.54–0.76), comparable to manually defined volumes (0.64, CI: 0.53–0.75 and 0.63, CI: 0.52–0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.