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  • Round 2 VASARI Feature Scores: Multiple reads per case, available upon email request to cancerimagingarchive@mail.nih.gov.
  • Round 2 VASARI Feature Scores: 1 read per case consolidated by majority vote, available upon email request to cancerimagingarchive@mail.nih.gov. 
  • Round 2 TCIA Shared Lists (see Section 3.7 of TCIA User Guide for help with Shared Lists)
    • 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 were utilized in the final data sets referenced above
  • Round 2 Google Form: In Round 2 the readers scored the features for 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 to re-use this Google Form in your own research we can clone a copy for you upon request to cancerimagingarchive@mail.nih.gov.
  • Round 2 Clearcanvas AIM Template: This template was used in conjunction with AIM Clearcanvas v3.0.2 to collect the readers' markup.

References

  1. Louis, D.N., Ohgaki, H., Wiestler, O.D., Cavenee, W.K., WHO Classification of Tumours of the Central Nervous System.4th ed. 2007, Lyon: Intl. Agency for Research. 309.
  2. CBTRUS, CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors in the United States in 2004-2006. Published by the Central Brain Tumor Registry of the United States. 2010: Hinsdale, IL.
  3. Stupp, R., et al., Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med, 2005.352(10): p. 987-96.
  4. Bonavia, R., et al., Heterogeneity Maintenance in Glioblastoma: A Social Network. Cancer Research, 2011. 71(12): p. 4055-4060.
  5. Park, J.K., et al., Scale to Predict Survival After Surgery for Recurrent Glioblastoma Multiforme. Journal of Clinical Oncology, 2010. 28(24): p. 3838-3843.
  6. Pope, W.B., et al., MR Imaging Correlates of Survival in Patients with High-Grade Gliomas. American Journal of Neuroradiology, 2005. 26(10): p. 2466-2474.
  7. Pope, W.B., et al., Relationship between Gene Expression and Enhancement in Glioblastoma Multiforme: Exploratory DNA Microarray Analysis, Radiology. 2008 October; 249(1): 268-277.
  8. Lacroix, M., et al., A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. Journal of Neurosurgery, 2001. 95(2): p. 190-198.
  9. Hammoud, M.A., et al., Prognostic significance of preoperative MRI scans in glioblastoma multiforme. Journal of Neuro-Oncology, 1996. 27(1): p. 65-73.
  10. Verhaak, R.G., et al., Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 2010. 17(1): p. 98-110.
  11. Phillips, H.S., et al., Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell, 2006. 9(3): p. 157-73.
  12. New, A.S., et al., Laboratory induced aggression: A PET study of borderline personality disorder. Biological Psychiatry, 2007. 61(8): p. 14s-14s.
  13. Channin, D.S., et al., The Annotation and Image Mark-up project. Radiology, 2009. 253(3): p. 590-2.
  14. Channin, D.S., et al., The caBIG annotation and image Markup project. J Digit Imaging, 2010. 23(2): p. 217-25.
  15. Rubin, D.L., P. Mongkolwat, and D.S. Channin, A semantic image annotation model to enable integrative translational research. Summit on Translat Bioinforma, 2009. 2009: p. 106-10.
  16. Showalter, T.N., et al., Multifocal glioblastoma multiforme: prognostic factors and patterns of progression. Int J Radiat Oncol Biol Phys, 2007. 69(3): p. 820-4.
  17. Scott, C.B., et al., Validation and predictive power of Radiation Therapy Oncology Group (RTOG) recursive partitioning analysis classes for malignant glioma patients: a report using RTOG 90-06. Int J Radiat Oncol Biol Phys, 1998. 40(1): p. 51-5.
  18. Curran, W.J., Jr., et al., Recursive partitioning analysis of prognostic factors in three Radiation Therapy Oncology Group malignant glioma trials. J Natl Cancer Inst, 1993. 85(9): p. 704-10.
  19. Krippendorff, K. Computing Krippendorf's Alpha-Reliability. 2011 (cited 2011 12/12/2011); Available from:http://repository.upenn.edu/asc_papers/43/Image Added.
  20. Burnham, K.P. and D.R. Anderson, Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd ed. 2002: Springer-Verlag.