Date: Mon, 18 Mar 2024 21:22:06 -0500 (CDT)
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Summary
The controlled "VASARI" terminology for describing the MR features of hu=
man gliomas was devised based upon prior work (REMBRA=
NDT project). This comprehensive featureset consists of 24 observations=
familiar to neuroradiologists to describe the morphology of brain tumors o=
n routine contrast-enhanced MRI. De-identified baseline MRI studies for 88 =
glioblastomas were collected from subjects analyzed as part of The C=
ancer Genome Atlas (TCGA) initiative. Neuroradiologists in disparate ge=
ographic 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 o=
f the centers. As studies were evaluated, scores were uploaded from C=
learcanvas to a central AIM (Annotation and Image Markup) Data Service for =
QC checks and interim analysis. Case assignments were deliberately staged i=
n a staggered fashion to ensure that a minimum of three evaluations were ef=
ficiently obtained. Administrative tools were employed by coordinators in a=
fourth location. Qualitative assessments included: (1) effectiveness of tr=
aining, (2) ease of deployment & functionality of the informatics solut=
ions and (3) efficiency of the process. Inter-observer variation for each f=
eature was assessed with the generalized kappa statistic of Berry&Miekl=
e.
Training, deployment of resources and completion of three evaluations pe=
r case were accomplished in 30 days. Functionality of the IT solutions was =
rated superior in qualitative assessment. The results indicated strong over=
all average inter-observer agreement among all six readers. Agreement was h=
ighest for tumor side (generalized kappa statistic k=3D0.943, 95% CI 0.915-=
0.982) and tumor location (k=3D0.837, 95% CI 0.807-0.902). Other features w=
ith high agreement included proportion enhancing tumor (k=3D0.656, 95% CI 0=
.596-0.757), presence of satellites (k=3D0.663, 95% CI 0.591-0.780), and di=
ffusion (k=3D0.730, 95% CI 0.664-0.828). Of the remaining, only three featu=
res (12.5%) showed low agreement (k<0.4): presence of calvarial remodeli=
ng (k=3D0.366, 95% CI 0.124-0.626), cortical involvement (k=3D0.167, 95% CI=
0.157-0.335), and definition of non-enhancing margin (k=3D0.374, 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 effective=
ly used by domain experts following a relatively short training period. Tec=
hnologies developed through the caBIG initiative provide an effective and e=
fficient framework for federated imaging assessments that can expedite cros=
s-correlative analysis with other data repositories (e.g. genomics / proteo=
mics / pathology).
Updates to the The C=
ancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) imagin=
g data set are being stored within TCIA and the research group is continuin=
g to expand on their work.
The VASARI project has been conducted in multiple rounds. In the f=
irst round 88 subjects were reviewed using the feature set, resulting in 75=
finalized scores after removal of cases missing the appropriate scans requ=
ired to score the case. Similarly in the second round an additional 4=
1 cases were reviewed resulting in 36 new cases being added. Minor ch=
anges were made to the VASARI feature definitions between rounds 1 and 2.=
p>
Round 1
- Round 1 VASARI Feature Scores: Multiple reads per case.
- Round 1 VASARI Feature Scores 1 read per case consolidated by majority =
vote.
- Round 1 TCIA Shared Lists (see Section 3.7 of T=
CIA User Guide for help with Shared Lists)=20
- TCGA-GBM Round 1: Consists of all 88 subjects imaging studies who were =
reviewed for scoring
- VASARI Round 1 Final: Consists of only the 75 subjects whose scores wer=
e utilized in the final data sets referenced above
- Vasari MR =
Feature Guide v1.1: Image based examples of how the VASARI feature set =
was utilized in Round 1 analysis
Round 2
- Round 2 VASARI Feature Scores: Multiple reads per case.
- Round 2 VASARI Feature Scores: 1 read per case consolidated by majority=
vote.
- Round 2 TCIA Shared Lists (see Section 3.7 of T=
CIA User Guide for help with Shared Lists)=20
- VASARI Round 2 Disqualified: Consists of the 5 cases reviewed in round =
2 that were not utilized
- VASARI Round 2 Final: Consists of only the 36 subjects whose scores wer=
e utilized in the final data sets referenced above
- Round 2 Google Form: In Round 2 the readers scored the features fo=
r the cases using Google Forms (with exception to measuring the mass). =
; This link allows you to view and test the form. If you would like t=
o re-use this Google Form in your own research we can clone a copy for you =
upon request to help@cancerimagingarchive.net.
- Round 2 Clearcanvas AIM Template: This templ=
ate was used in conjunction with AIM Clearcanvas v3.0.2 to collect the readers' markup.
References
- Louis, D.N., Ohgaki, H., Wiestler, O.D., Cavenee, W.K., WHO Classificat=
ion of Tumours of the Central Nervous System.4th ed. 2007, Lyon: Intl. Agen=
cy for Research. 309.
- CBTRUS, CBTRUS Statistical Report: Primary Brain and Central Nervous Sy=
stem Tumors in the United States in 2004-2006. Published by the Central Bra=
in Tumor Registry of the United States. 2010: Hinsdale, IL.
- Stupp, R., et al., Radiotherapy plus concomitant and adjuvant temozolom=
ide for glioblastoma. N Engl J Med, 2005.352(10): p. 987-96.
- Bonavia, R., et al., Heterogeneity Maintenance in Glioblastoma: A Socia=
l Network. Cancer Research, 2011. 71(12): p. 4055-4060.
- Park, J.K., et al., Scale to Predict Survival After Surgery for Recurre=
nt Glioblastoma Multiforme. Journal of Clinical Oncology, 2010. 28(24): p. =
3838-3843.
- Pope, W.B., et al., MR Imaging Correlates of Survival in Patients with =
High-Grade Gliomas. American Journal of Neuroradiology, 2005. 26(10): p. 24=
66-2474.
- Pope, W.B., et al., Relationship between Gene Expression and Enhancemen=
t in Glioblastoma Multiforme: Exploratory DNA Microarray Analysis, Radiolog=
y. 2008 October; 249(1): 268--277.
- Lacroix, M., et al., A multivariate analysis of 416 patients with gliob=
lastoma multiforme: prognosis, extent of resection, and survival. Journal o=
f Neurosurgery, 2001. 95(2): p. 190-198.
- Hammoud, M.A., et al., Prognostic significance of preoperative MRI scan=
s in glioblastoma multiforme. Journal of Neuro-Oncology, 1996. 27(1): p. 65=
-73.
- Verhaak, R.G., et al., Integrated genomic analysis identifies clinicall=
y relevant subtypes of glioblastoma characterized by abnormalities in PDGFR=
A, IDH1, EGFR, and NF1. Cancer Cell, 2010. 17(1): p. 98-110.
- Phillips, H.S., et al., Molecular subclasses of high-grade glioma predi=
ct prognosis, delineate a pattern of disease progression, and resemble stag=
es in neurogenesis. Cancer Cell, 2006. 9(3): p. 157-73.
- New, A.S., et al., Laboratory induced aggression: A PET study of border=
line personality disorder. Biological Psychiatry, 2007. 61(8): p. 14s-14s.<=
/li>
- Channin, D.S., et al., The Annotation and Image Mark-up project. Radiol=
ogy, 2009. 253(3): p. 590-2.
- Channin, D.S., et al., The caBIG annotation and image Markup project. J=
Digit Imaging, 2010. 23(2): p. 217-25.
- Rubin, D.L., P. Mongkolwat, and D.S. Channin, A semantic image annotati=
on model to enable integrative translational research. Summit on Translat B=
ioinforma, 2009. 2009: p. 106-10.
- Showalter, T.N., et al., Multifocal glioblastoma multiforme: prognostic=
factors and patterns of progression. Int J Radiat Oncol Biol Phys, 2007. 6=
9(3): p. 820-4.
- Scott, C.B., et al., Validation and predictive power of Radiation Thera=
py Oncology Group (RTOG) recursive partitioning analysis classes for malign=
ant glioma patients: a report using RTOG 90-06. Int J Radiat Oncol Biol Phy=
s, 1998. 40(1): p. 51-5.
- Curran, W.J., Jr., et al., Recursive partitioning analysis of prognosti=
c factors in three Radiation Therapy Oncology Group malignant glioma trials=
. J Natl Cancer Inst, 1993. 85(9): p. 704-10.
- Krippendorff, K. Computing Krippendorf's Alpha-Reliability. 2011 (cited=
2011 12/12/2011); Available from:http://repository.up=
enn.edu/asc_papers/43/.
- Burnham, K.P. and D.R. Anderson, Model Selection and Multimodel Inferen=
ce: A Practical Information-Theoretic Approach 2nd ed. 2002: Springer-Verla=
g.
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