  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 of 100 glioblastoma cases. We quantified: 1)
agreement across neuroradiologists and VASARI-auto; 2) calibration of
performance equity; 3) an economic workforce analysis; and 4) fidelity in
predicting patient survival. Tumour segmentation was compatible with the
current state of the art and equally performant regardless of age or sex. A
modest inter-rater variability between in-house neuroradiologists was
comparable to between neuroradiologists and VASARI-auto, with far higher
agreement between VASARI-auto methods. The time taken for neuroradiologists to
derive VASARI was substantially higher than VASARI-auto (mean time per case 317
vs. 3 seconds). A UK hospital workforce analysis forecast that three years of
VASARI featurisation would demand 29,777 consultant neuroradiologist workforce
hours ({\pounds}1,574,935), reducible to 332 hours of computing time (and
{\pounds}146 of power) with VASARI-auto. The best-performing survival model
utilised VASARI-auto features as opposed to those derived by neuroradiologists.
VASARI-auto is a highly efficient automated labelling system with equitable
performance across patient age or sex, a favourable economic profile if used as
a decision support tool, and with non-inferior fidelity in downstream patient
survival prediction. Future work should iterate upon and integrate such tools
to enhance patient care.
