Features of Visually AcceSAble Rembrandt Images: Interrater Reliability in Pediatric Brain Tumors

References

1. 1.↵
   VASARI Research Project. The Cancer Imaging Archive (TCIA). https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project. Accessed January 13, 2022
2. 2.↵
   1. Gusev Y,
   2. Bhuvaneshwar K,
   3. Song L, et al

   . The REMBRANDT study, a large collection of genomic data from brain cancer patients. Sci Data 2018;5:180158 doi:10.1038/sdata.2018.158 pmid:30106394

   CrossRefPubMed
3. 3.↵
   1. Gutman DA,
   2. Cooper LA,
   3. Hwang SN, et al

   . MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 2013;267:560–69 doi:10.1148/radiol.13120118 pmid:23392431

   CrossRefPubMedWeb of Science
4. 4.↵
   1. Zhou H,
   2. Vallières M,
   3. Bai HX, et al

   . MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 2017;19:862–70 doi:10.1093/neuonc/now256 pmid:28339588

   CrossRefPubMed
5. 5.↵
   1. Yu J,
   2. Wang M,
   3. Song J, et al

   . Potential utility of Visually AcceSAble Rembrandt Images assessment in brain astrocytoma grading. J Comput Assist Tomogr 2016;40:301–06 doi:10.1097/RCT.0000000000000352 pmid:26978002

   CrossRefPubMed
6. 6.↵
   1. Nicolasjilwan M,
   2. Hu Y,
   3. Yan C, et al

   ; TCGA Glioma Phenotype Research Group. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. J Neuroradiol 2015;42:212–21 doi:10.1016/j.neurad.2014.02.006 pmid:24997477

   CrossRefPubMed
7. 7.↵
   R Core Team. R: A Language and Environment for Statistical Computing. http://web.mit.edu/r_v3.4.1/fullrefman.pdf. Accessed January 13, 2022
8. 8.↵
   1. Hayes AF,
   2. Krippendorff K

   . Answering the call for a standard reliability measure for coding data. Communication Methods and Measures 2007;1:77–89 doi:10.1080/19312450709336664

   CrossRef
9. 9.↵
   1. Zhou J,
   2. Reddy MV,
   3. Wilson BK, et al

   . MR imaging characteristics associate with tumor-associated macrophages in glioblastoma and provide an improved signature for survival prognostication. AJNR Am J Neuroradiol 2018;39:252–59 doi:10.3174/ajnr.A5441 pmid:29191871

   Abstract/FREE Full Text
10. 10.↵
    1. Mazurowski MA,
    2. Desjardins A,
    3. Malof JM

    . Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro Oncol 2013;15:1389–94 doi:10.1093/neuonc/nos335 pmid:23396489

    CrossRefPubMed
11. 11.↵
    1. Wangaryattawanich P,
    2. Hatami M,
    3. Wang J, et al

    . Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro Oncol 2015;17:1525–37 doi:10.1093/neuonc/nov117 pmid:26203066

    CrossRefPubMed
12. 12.↵
    1. Colen RR,
    2. Vangel M,
    3. Wang J, et al

    ; TCGA Glioma Phenotype Research Group. Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project. BMC Med Genomics 2014;7:30 doi:10.1186/1755-8794-7-30 pmid:24889866

    CrossRefPubMed
13. 13.↵
    1. Jain R,
    2. Poisson LM,
    3. Gutman D, et al

    . Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology 2014;272:484–93 doi:10.1148/radiol.14131691 pmid:24646147

    CrossRefPubMed
14. 14.↵
    1. Park CJ,
    2. Han K,
    3. Shin H, et al

    . MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas. Eur Radiol 2020;30:3035–45 doi:10.1007/s00330-020-06683-2 pmid:32060714

    CrossRefPubMed
15. 15.↵
    1. Lasocki A,
    2. Gaillard F,
    3. Gorelik A, et al

    . MRI features can predict 1p/19q status in intracranial gliomas. AJNR Am J Neuroradiol 2018;39:687–92 doi:10.3174/ajnr.A5572 pmid:29519793

    Abstract/FREE Full Text
16. 16.↵
    1. Ryall S,
    2. Zapotocky M,
    3. Fukuoka K, et al

    . Integrated molecular and clinical analysis of 1,000 pediatric low-grade gliomas. Cancer Cell 2020;37:569–583.e5 doi:10.1016/j.ccell.2020.03.011 pmid:32289278

    CrossRefPubMed
17. 17.↵
    1. Ryall S,
    2. Tabori U,
    3. Hawkins C

    . Pediatric low-grade glioma in the era of molecular diagnostics. Acta Neuropathol Commun 2020;8:30 doi:10.1186/s40478-020-00902-z pmid:32164789

    CrossRefPubMed
18. 18.↵
    1. Orrù E,
    2. Calloni SF,
    3. Tekes A, et al

    . The child with macrocephaly: differential diagnosis and neuroimaging findings. AJR Am J Roentgenol 2018;210:848–59 doi:10.2214/AJR.17.18693 pmid:29470161

    CrossRefPubMed