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

Reproducible 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.

[1]  M L JACKS.  Man versus the machine. , 1951, Nursing times.

[2]  C. Davatzikos,et al.  Survival Analysis of Patients with High-Grade Gliomas Based on Data Mining of Imaging Variables , 2012, American Journal of Neuroradiology.

[3]  P. Marsden,et al.  False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review , 2015, PloS one.

[4]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[5]  Maciej A Mazurowski,et al.  A fully automatic extraction of magnetic resonance image features in glioblastoma patients. , 2014, Medical physics.

[6]  S. Bauer,et al.  Multi-Modal Glioblastoma Segmentation: Man versus Machine , 2014, PloS one.

[7]  Rupert G. Miller,et al.  Survival Analysis , 2022, The SAGE Encyclopedia of Research Design.

[8]  William D. Dunn,et al.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. , 2013, Radiology.

[9]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[10]  D. Black,et al.  Mindfulness Training for Children and Adolescents A State‑of‑the‑Science Review , 2014 .

[11]  M A Deeley,et al.  Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study , 2011, Physics in medicine and biology.

[12]  Stefan Bauer,et al.  Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization , 2011, MICCAI.

[13]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[14]  J. Barnholtz-Sloan,et al.  The epidemiology of glioma in adults: a "state of the science" review. , 2014, Neuro-oncology.

[15]  Ferenc A. Jolesz,et al.  Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2011, PloS one.

[16]  Aedín C. Culhane,et al.  survcomp: an R/Bioconductor package for performance assessment and comparison of survival models , 2011, Bioinform..

[17]  Keith L. Ligon,et al.  Somatic mutations associated with MRI-derived volumetric features in glioblastoma , 2015, Neuroradiology.

[18]  E. Mohammadi,et al.  Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[19]  Ron Kikinis,et al.  Volumetric CT-based segmentation of NSCLC using 3D-Slicer , 2013, Scientific Reports.

[20]  S. Bauer,et al.  A survey of MRI-based medical image analysis for brain tumor studies , 2013, Physics in medicine and biology.

[21]  Maciej A. Mazurowski,et al.  Computer-extracted MR imaging features are associated with survival in glioblastoma patients , 2014, Journal of Neuro-Oncology.

[22]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[23]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[24]  James V. Miller,et al.  GBM Volumetry using the 3D Slicer Medical Image Computing Platform , 2013, Scientific Reports.

[25]  Stefan Bauer,et al.  Patient-Specific Semi-supervised Learning for Postoperative Brain Tumor Segmentation , 2014, MICCAI.

[26]  Xuzhu Chen,et al.  Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of MRI , 2014, Journal of Neuro-Oncology.

[27]  K. Aldape,et al.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules , 2008, Proceedings of the National Academy of Sciences.

[28]  Paul S Mischel,et al.  MR imaging correlates of survival in patients with high-grade gliomas. , 2005, AJNR. American journal of neuroradiology.

[29]  George Fountzilas,et al.  Volumetric and MGMT parameters in glioblastoma patients: Survival analysis , 2012, BMC Cancer.

[30]  John D. Storey,et al.  False Discovery Rate , 2020, International Encyclopedia of Statistical Science.

[31]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[32]  Paul S Mischel,et al.  Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. , 2008, Radiology.

[33]  Benjamin M Ellingson,et al.  Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images. , 2013, Neuro-oncology.

[34]  Veit Rohde,et al.  EXTENT OF RESECTION AND SURVIVAL IN GLIOBLASTOMA MULTIFORME: IDENTIFICATION OF AND ADJUSTMENT FOR BIAS , 2008, Neurosurgery.

[35]  S. Plevritis,et al.  Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. , 2014, Radiology.

[36]  Benjamin Haibe-Kains,et al.  mRMRe: an R package for parallelized mRMR ensemble feature selection , 2013, Bioinform..

[37]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[38]  M. Pagano,et al.  Survival analysis. , 1996, Nutrition.