VASARI-auto: equitable, efficient, and economical featurisation of glioma MRI

arXiv:2404.15318v1 Announce Type: cross
Abstract: The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clinical practice. This is a problem that machine learning could plausibly automate. Using glioma data from 1172 patients, we developed VASARI-auto, an automated labelling software applied to both open-source lesion masks and our openly available tumour segmentation model. In parallel, two consultant neuroradiologists independently quantified VASARI features in a subsample …

abstract arxiv auto automate clinical cs.cv data feature imaging machine machine learning mri patients practice q-bio.qm q-bio.to quantitative set type