The controlled "VASARI" terminology for describing the MR features of human gliomas was devised based upon prior work (REMBRANDT project). This comprehensive featureset consists of 24 observations familiar to neuroradiologists to describe the morphology of brain tumors on routine contrast-enhanced MRI. De-identified baseline MRI studies for 88 glioblastomas were collected from subjects analyzed as part of The Cancer Genome Atlas (TCGA) initiative. Neuroradiologists in disparate geographic locations were recruited and trained in the use of the featureset using a visual guidebook. Training cases were employed to assess competency and to ensure agreement. A open-source PACS workstation (Clear Canvas) was customized for clinical imaging research evaluation and deployed at each of the centers.  As studies were evaluated, scores were uploaded from Clearcanvas to a central AIM (Annotation and Image Markup) Data Service for QC checks and interim analysis. Case assignments were deliberately staged in a staggered fashion to ensure that a minimum of three evaluations were efficiently obtained. Administrative tools were employed by coordinators in a fourth location. Qualitative assessments included: (1) effectiveness of training, (2) ease of deployment & functionality of the informatics solutions and (3) efficiency of the process. Inter-observer variation for each feature was assessed with the generalized kappa statistic of Berry&Miekle.

Training, deployment of resources and completion of three evaluations per case were accomplished in 30 days. Functionality of the IT solutions was rated superior in qualitative assessment. The results indicated strong overall average inter-observer agreement among all six readers. Agreement was highest for tumor side (generalized kappa statistic k=0.943, 95% CI 0.915-0.982) and tumor location (k=0.837, 95% CI 0.807-0.902). Other features with high agreement included proportion enhancing tumor (k=0.656, 95% CI 0.596-0.757), presence of satellites (k=0.663, 95% CI 0.591-0.780), and diffusion (k=0.730, 95% CI 0.664-0.828). Of the remaining, only three features (12.5%) showed low agreement (k<0.4): presence of calvarial remodeling (k=0.366, 95% CI 0.124-0.626), cortical involvement (k=0.167, 95% CI 0.157-0.335), and definition of non-enhancing margin (k=0.374, 95% CI 0.347-0.514).

Inclusion of vetted, tested and validated controlled terminologies into imaging arms of clinical trials is essential in adding value of imaging as a biomarker in cross-cutting correlative studies. Controlled terminologies such as the one described herein for assessment of gliomas can be effectively used by domain experts following a relatively short training period. Technologies developed through the caBIG initiative provide an effective and efficient framework for federated imaging assessments that can expedite cross-correlative analysis with other data repositories (e.g. genomics / proteomics / pathology).

Updates to the TCGA-GBM imaging data set are being stored within TCIA and the research group is continuing to expand on their work.

Supporting Documentation and Metadata

The VASARI project has been conducted in multiple rounds.  In the first round 88 subjects were reviewed using the feature set, resulting in 75 finalized scores after removal of cases missing the appropriate scans required to score the case.  Similarly in the second round an additional 41 cases were reviewed resulting in 36 new cases being added.  Minor changes were made to the VASARI feature definitions between rounds 1 and 2.

Round 1

Round 2