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

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 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.