Evidence map›Paper›PMID 24362266›Full record

ArticleClinical biochemistry2014

Digital pathology and image analysis augment biospecimen annotation and biobank quality assurance harmonization.

Bih-Rong Wei, R Mark Simpson

Abstract read
In one paragraph

Article in Clinical biochemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. 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

2 authors.

Bih-Rong WeiLaboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH Building 37, 37 Convent Drive, Bethesda, MD 20892, USA.
R Mark SimpsonLaboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. Electronic address: ms43b@nih.gov.

Funding

Molecular Pathology Research for Cancer Diagnostics and BiomarkersZICBC010953 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI SIMPSON, ROBERT · 2009 to 2025
$22.9M
Intramural NIH HHS ZIC BC010953
6 · The paper itself

Abstract

Standardization of biorepository best practices will enhance the quality of translational biomedical research utilizing patient-derived biobank specimens. Harmonization of pathology quality assurance procedures for biobank accessions has lagged behind other avenues of biospecimen research and biobank development. Comprehension of the cellular content of biorepository specimens is important for discovery of tissue-specific clinically relevant biomarkers for diagnosis and treatment. While rapidly emerging technologies in molecular analyses and data mining create focus on appropriate measures for minimizing pre-analytic artifact-inducing variables, less attention gets paid to annotating the constituent makeup of biospecimens for more effective specimen selection by biobank clients. Both pre-analytic tissue processing and specimen composition influence acquisition of relevant macromolecules for downstream assays. Pathologist review of biorepository submissions, particularly tissues as part of quality assurance procedures, helps to ensure that the intended target cells are present and in sufficient quantity in accessioned specimens. This manual procedure can be tedious and subjective. Incorporating digital pathology into biobank quality assurance procedures, using automated pattern recognition morphometric image analysis to quantify tissue feature areas in digital whole slide images of tissue sections, can minimize variability and subjectivity associated with routine pathologic evaluations in biorepositories. Whole-slide images and pathologist-reviewed morphometric analyses can be provided to researchers to guide specimen selection. Harmonization of pathology quality assurance methods that minimize subjectivity and improve reproducibility among collections would facilitate research-relevant specimen selection by investigators and could facilitate information sharing in an integrated network approach to biobanking.

Indexed as

Biological Specimen BanksDiagnosis, Computer-AssistedHumansImage Processing, Computer-AssistedPattern Recognition, AutomatedQuality ControlReproducibility of ResultsSpecimen HandlingTranslational Research, BiomedicalBiological specimen bankBiomarkersComputer-assisted diagnosisIsolation and purificationMorphometryPattern recognition image analysisQuantitative pathologyStandards

Identifiers

PMID24362266
PMCPMC4975042

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.