Literature DB >> 26331360

Histogram Analysis of Diffusion Tensor Imaging Parameters in Pediatric Cerebellar Tumors.

Matthias W Wagner1, Anand K Narayan2, Thangamadhan Bosemani2, Thierry A G M Huisman2, Andrea Poretti2.   

Abstract

BACKGROUND AND
PURPOSE: Apparent diffusion coefficient (ADC) values have been shown to assist in differentiating cerebellar pilocytic astrocytomas and medulloblastomas. Previous studies have applied only ADC measurements and calculated the mean/median values. Here we investigated the value of diffusion tensor imaging (DTI) histogram characteristics of the entire tumor for differentiation of cerebellar pilocytic astrocytomas and medulloblastomas.
METHODS: Presurgical DTI data were analyzed with a region of interest (ROI) approach to include the entire tumor. For each tumor, histogram-derived metrics including the 25th percentile, 75th percentile, and skewness were calculated for fractional anisotropy (FA) and mean (MD), axial (AD), and radial (RD) diffusivity. The histogram metrics were used as primary predictors of interest in a logistic regression model. Statistical significance levels were set at p < .01.
RESULTS: The study population included 17 children with pilocytic astrocytoma and 16 with medulloblastoma (mean age, 9.21 ± 5.18 years and 7.66 ± 4.97 years, respectively). Compared to children with medulloblastoma, children with pilocytic astrocytoma showed higher MD (P = .003 and P = .008), AD (P = .004 and P = .007), and RD (P = .003 and P = .009) values for the 25th and 75th percentile. In addition, histogram skewness showed statistically significant differences for MD between low- and high-grade tumors (P = .008).
CONCLUSIONS: The 25th percentile for MD yields the best results for the presurgical differentiation between pediatric cerebellar pilocytic astrocytomas and medulloblastomas. The analysis of other DTI metrics does not provide additional diagnostic value. Our study confirms the diagnostic value of the quantitative histogram analysis of DTI data in pediatric neuro-oncology.
Copyright © 2015 by the American Society of Neuroimaging.

Entities:  

Keywords:  Medulloblastoma; children; diffusion tensor imaging; histogram analysis; pilocytic astrocytoma

Mesh:

Year:  2015        PMID: 26331360     DOI: 10.1111/jon.12292

Source DB:  PubMed          Journal:  J Neuroimaging        ISSN: 1051-2284            Impact factor:   2.486


  10 in total

1.  Diffusion-weighted imaging and diffusion tensor imaging as adjuncts to conventional MRI for the diagnosis and management of peripheral nerve sheath tumors: current perspectives and future directions.

Authors:  Alexander T Mazal; Oganes Ashikyan; Jonathan Cheng; Lu Q Le; Avneesh Chhabra
Journal:  Eur Radiol       Date:  2018-12-07       Impact factor: 5.315

2.  Volumetric voxelwise apparent diffusion coefficient histogram analysis for differentiation of the fourth ventricular tumors.

Authors:  Seyedmehdi Payabvash; Tarik Tihan; Soonmee Cha
Journal:  Neuroradiol J       Date:  2018-09-19

3.  Whole-tumor histogram analysis of DWI and QSI for differentiating between meningioma and schwannoma: a pilot study.

Authors:  Hitomi Nagano; Koji Sakai; Jun Tazoe; Masashi Yasuike; Kentaro Akazawa; Kei Yamada
Journal:  Jpn J Radiol       Date:  2019-08-08       Impact factor: 2.374

4.  Application of Apparent Diffusion Coefficient Histogram Metrics for Differentiation of Pediatric Posterior Fossa Tumors : A Large Retrospective Study and Brief Review of Literature.

Authors:  Fabrício Guimarães Gonçalves; Alireza Zandifar; Jorge Du Ub Kim; Luis Octavio Tierradentro-García; Adarsh Ghosh; Dmitry Khrichenko; Savvas Andronikou; Arastoo Vossough
Journal:  Clin Neuroradiol       Date:  2022-06-08       Impact factor: 3.649

5.  Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters.

Authors:  Zhenxing Huang; Changyu Lu; Gen Li; Zhenye Li; Shengjun Sun; Yazhuo Zhang; Zonggang Hou; Jian Xie
Journal:  Front Oncol       Date:  2021-03-10       Impact factor: 6.244

6.  Characterization of Diffusion Metric Map Similarity in Data From a Clinical Data Repository Using Histogram Distances.

Authors:  Graham C Warner; Karl G Helmer
Journal:  Front Neurosci       Date:  2018-03-08       Impact factor: 4.677

7.  Histogram analysis parameters of apparent diffusion coefficient reflect tumor cellularity and proliferation activity in head and neck squamous cell carcinoma.

Authors:  Alexey Surov; Hans Jonas Meyer; Karsten Winter; Cindy Richter; Anna-Kathrin Hoehn
Journal:  Oncotarget       Date:  2018-05-04

8.  Use of Apparent Diffusion Coefficient Histogram in Differentiating Between Medulloblastoma and Pilocytic Astrocytoma in Children.

Authors:  Weijian Wang; Jingliang Cheng; Yong Zhang; Chaoyan Wang
Journal:  Med Sci Monit       Date:  2018-09-02

Review 9.  Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors.

Authors:  Francesco Sanvito; Antonella Castellano; Andrea Falini
Journal:  Cancers (Basel)       Date:  2021-01-23       Impact factor: 6.639

10.  CT-Based Radiomics to Differentiate Pelvic Rhabdomyosarcoma From Yolk Sac Tumors in Children.

Authors:  Xin Chen; Yan Huang; Ling He; Ting Zhang; Li Zhang; Hao Ding
Journal:  Front Oncol       Date:  2020-11-24       Impact factor: 6.244

  10 in total

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