Evidence map›Paper›PMID 30173245›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2018

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

Weijian Wang, Jingliang Cheng, Yong Zhang, Chaoyan Wang

Abstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Fusion of habitat analysis and deep learning on contrast-enhanced T1-weighted imaging for predicting Ki-67 status in pediatric brain tumors.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025
    Article
  3. Article
  4. Benign Glioma.Advances in experimental medicine and biology · 2023
    Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Weijian WangMagnetic Resonance Imaging (MRI) Division, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland).
Jingliang ChengMagnetic Resonance Imaging (MRI) Division, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland).
Yong ZhangMagnetic Resonance Imaging (MRI) Division, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland).
Chaoyan WangMagnetic Resonance Imaging (MRI) Division, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND This research aimed to investigate the value of apparent diffusion coefficient (ADC) histogram in differentiating between medulloblastoma and pilocytic astrocytoma in children. MATERIAL AND METHODS Thirty-three children with posterior cranial fossa tumor confirmed by operation and pathology participated in this retrospective study, including 18 children with medulloblastoma and 15 children with pilocytic astrocytoma. ADC images of the maximum lay of tumors were selected, and the region of interest was delineated by Mazda software and analyzed by histogram. Histogram characteristic parameters of the 2 tumors were statistically analyzed to determine the significantly different characteristic parameters between the 2 tumor types. RESULTS There were significant differences in the mean value, variance, skewness, kurtosis, and 1th, 10th, 50th, and 90th percentiles of 9 characteristic parameters extracted by histogram (P<0.05). The corresponding receiver operating characteristic (ROC) curves were drawn, in which the mean value and 50th percentile were best identified. When the maximum area under the ROC curve was 1 and the optimal threshold was 137.7 and 125.5, the specificity and sensitivity were both 100%. CONCLUSIONS ADC histograms can be used to differentiate between medulloblastoma and pilocytic astrocytoma in children and provide reliable and objective evidence for the differentiation.

Indexed as

AdolescentAstrocytomaChildDiffusion Magnetic Resonance ImagingFemaleHumansImage Interpretation, Computer-AssistedMaleMedulloblastomaRetrospective StudiesROC Curve

Identifiers

PMID30173245
PMCPMC6131977

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.