title: Statistical evaluation of manual segmentation of a diffuse low-grade glioma MRI dataset.
abstract: Software-based manual segmentation is critical to the supervision of diffuse low-grade glioma patients and to the optimal treatment's choice. However, manual segmentation being time-consuming, it is difficult to include it in the clinical routine. An alternative to circumvent the time cost of manual segmentation could be to share the task among different practitioners, providing it can be reproduced. The goal of our work is to assess diffuse low-grade gliomas' manual segmentation's reproducibility on MRI scans, with regard to practitioners, their experience and field of expertise. A panel of 13 experts manually segmented 12 diffuse low-grade glioma clinical MRI datasets using the OSIRIX software. A statistical analysis gave promising results, as the practitioner factor, the medical specialty and the years of experience seem to have no significant impact on the average values of the tumor volume variable.
authors.forename: Meriem
authors.initials: M
authors.lastname: Ben Abdallah
authors.forename: Marie
authors.initials: M
authors.lastname: Blonski
authors.forename: Sophie
authors.initials: S
authors.lastname: Wantz-Mezieres
authors.forename: Yann
authors.initials: Y
authors.lastname: Gaudeau
authors.forename: Luc
authors.initials: L
authors.lastname: Taillandier
authors.forename: Jean-Marie
authors.initials: JM
authors.lastname: Moureaux
mesh_terms: D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D004058:Diffusion; D005910:Glioma / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D060787:Neoplasm Grading; D015203:Reproducibility of Results; D012984:Software; D013223:Statistics as Topic; D047368:Tumor Burden