Literature DB >> 35058297

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

A Biswas1,2, A Amirabadi1,2, M W Wagner1,2, B B Ertl-Wagner3,2.   

Abstract

BACKGROUND AND
PURPOSE: At present, no evidence-based lexicon exists for pediatric intracranial tumors. The Visually AcceSAble Rembrandt Images terminology describes reproducible MR imaging features of adult gliomas for prediction of tumor grade, molecular markers, and survival. Our aim was to assess the interrater reliability of the pre-resection features of Visually AcceSAble Rembrandt Images in pediatric brain tumors.
MATERIALS AND METHODS: Fifty consecutive pre-resection brain MR imaging examinations of pediatric intracranial neoplasms were independently reviewed by 3 neuroradiologists. The intraclass correlation coefficient for continuous variables and the Krippendorf alpha were used to evaluate the interrater agreement. Subgroup analysis was performed for 30 gliomas.
RESULTS: Parameters with almost perfect agreement (α > .8) included tumor location (F1) and proportion of enhancing tumor (F5). Parameters with substantial agreement (α = .61-.80) were side of tumor epicenter (F2), involvement of eloquent brain (F3), enhancement quality (F4), proportion of non-contrast-enhancing tumor (F6), and deep white matter invasion (F21). The other parameters showed either moderate (α = .41-.60; n = 11), fair (α = .21-.40; n = 5), or slight agreement (α = 0-.20; n = 1). Subgroup analysis of 30 gliomas showed almost perfect agreement for tumor location (F1), involvement of eloquent brain (F3), and proportion of enhancing tumor (F5); and substantial agreement for side of tumor epicenter (F2), enhancement quality (F4), proportion of noncontrast enhancing tumor (F6), cysts (F8), thickness of enhancing margin (F11), and deep white matter invasion (F21). The intraclass correlation coefficient for measurements in the axial plane was excellent in both the main group (0.984 [F29] and 0.982 [F30]) and the glioma subgroup (0.973 [F29] and 0.973 [F30]).
CONCLUSIONS: Nine features of Visually AcceSAble Rembrandt Images have an acceptable interrater agreement in pediatric brain tumors. For the subgroup of pediatric gliomas, 11 features of Visually AcceSAble Rembrandt Images have an acceptable interrater agreement. The low degree of reproducibility of the remainder of the features necessitates the use of features tailored to the pediatric age group and is likely related to the more heterogeneous imaging morphology of pediatric brain tumors.
© 2022 by American Journal of Neuroradiology.

Entities:  

Mesh:

Year:  2022        PMID: 35058297      PMCID: PMC8985665          DOI: 10.3174/ajnr.A7399

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  15 in total

1.  MRI Features Can Predict 1p/19q Status in Intracranial Gliomas.

Authors:  A Lasocki; F Gaillard; A Gorelik; M Gonzales
Journal:  AJNR Am J Neuroradiol       Date:  2018-03-08       Impact factor: 3.825

2.  MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas.

Authors:  Chae Jung Park; Kyunghwa Han; Haesol Shin; Sung Soo Ahn; Yoon Seong Choi; Yae Won Park; Jong Hee Chang; Se Hoon Kim; Rajan Jain; Seung-Koo Lee
Journal:  Eur Radiol       Date:  2020-02-14       Impact factor: 5.315

3.  Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival.

Authors:  Pattana Wangaryattawanich; Masumeh Hatami; Jixin Wang; Ginu Thomas; Adam Flanders; Justin Kirby; Max Wintermark; Erich S Huang; Ali Shojaee Bakhtiari; Markus M Luedi; Syed S Hashmi; Daniel L Rubin; James Y Chen; Scott N Hwang; John Freymann; Chad A Holder; Pascal O Zinn; Rivka R Colen
Journal:  Neuro Oncol       Date:  2015-07-22       Impact factor: 12.300

4.  Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.

Authors:  Manal Nicolasjilwan; Ying Hu; Chunhua Yan; Daoud Meerzaman; Chad A Holder; David Gutman; Rajan Jain; Rivka Colen; Daniel L Rubin; Pascal O Zinn; Scott N Hwang; Prashant Raghavan; Dima A Hammoud; Lisa M Scarpace; Tom Mikkelsen; James Chen; Olivier Gevaert; Kenneth Buetow; John Freymann; Justin Kirby; Adam E Flanders; Max Wintermark
Journal:  J Neuroradiol       Date:  2014-07-02       Impact factor: 3.447

5.  Potential Utility of Visually AcceSAble Rembrandt Images Assessment in Brain Astrocytoma Grading.

Authors:  Jing Yu; Min Wang; Jiacheng Song; DongYa Huang; Xunning Hong
Journal:  J Comput Assist Tomogr       Date:  2016 Mar-Apr       Impact factor: 1.826

6.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.

Authors:  David A Gutman; Lee A D Cooper; Scott N Hwang; Chad A Holder; Jingjing Gao; Tarun D Aurora; William D Dunn; Lisa Scarpace; Tom Mikkelsen; Rajan Jain; Max Wintermark; Manal Jilwan; Prashant Raghavan; Erich Huang; Robert J Clifford; Pattanasak Mongkolwat; Vladimir Kleper; John Freymann; Justin Kirby; Pascal O Zinn; Carlos S Moreno; Carl Jaffe; Rivka Colen; Daniel L Rubin; Joel Saltz; Adam Flanders; Daniel J Brat
Journal:  Radiology       Date:  2013-02-07       Impact factor: 11.105

7.  Imaging descriptors improve the predictive power of survival models for glioblastoma patients.

Authors:  Maciej Andrzej Mazurowski; Annick Desjardins; Jordan Milton Malof
Journal:  Neuro Oncol       Date:  2013-02-07       Impact factor: 12.300

8.  The Child With Macrocephaly: Differential Diagnosis and Neuroimaging Findings.

Authors:  Emanuele Orrù; Sonia F Calloni; Aylin Tekes; Thierry A G M Huisman; Bruno P Soares
Journal:  AJR Am J Roentgenol       Date:  2018-02-22       Impact factor: 3.959

9.  Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor.

Authors:  Rajan Jain; Laila M Poisson; David Gutman; Lisa Scarpace; Scott N Hwang; Chad A Holder; Max Wintermark; Arvind Rao; Rivka R Colen; Justin Kirby; John Freymann; C Carl Jaffe; Tom Mikkelsen; Adam Flanders
Journal:  Radiology       Date:  2014-03-19       Impact factor: 11.105

Review 10.  Pediatric low-grade glioma in the era of molecular diagnostics.

Authors:  Scott Ryall; Uri Tabori; Cynthia Hawkins
Journal:  Acta Neuropathol Commun       Date:  2020-03-12       Impact factor: 7.801

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