Evidence map›Paper›PMID 28923066›Full record

ArticleDiagnostic pathology2017

Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate.

Zhaoxuan Ma, Stephen L Shiao, Emi J Yoshida, Steven Swartwood, Fangjin Huang, Michael E Doche, Alice P Chung, Beatrice S Knudsen, Arkadiusz Gertych

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostic pathology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Review
  9. Observational
  10. Review
  11. Article
  12. Article
  13. Precision immunoprofiling by image analysis and artificial intelligence.Virchows Archiv : an international journal of pathology · 2019
    Review
  14. Review
  15. Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018
    Review
  16. 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

9 authors.

Zhaoxuan MaDepartment of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Stephen L ShiaoDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Emi J YoshidaDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Steven SwartwoodDepartment of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Fangjin HuangDepartment of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Michael E DocheDepartment of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Alice P ChungDepartment of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Beatrice S KnudsenDepartment of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Beatrice.Knudsen@cshs.org.
Arkadiusz GertychDepartment of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Arkadiusz.Gertych@cshs.org.ORCID http://orcid.org/0000-0002-3107-602X

Funding

Cedars-Sinai Biobank and Translational Research Core Facility (CS-BRCF)G20RR030860 · NCRR · CEDARS-SINAI MEDICAL CENTER · PI MELMED, SHLOMO · 2010 to 2010
$9.6M
Impact of the Microbiome on the Efficacy of Radiation TherapyR01CA220000 · NCI · CEDARS-SINAI MEDICAL CENTER · PI SHIAO, STEPHEN L. · 2017 to 2021
$1.7M
The Impact of Macrophage Polarization on the Efficacy of Radiation TherapyK08CA191139 · NCI · CEDARS-SINAI MEDICAL CENTER · PI SHIAO, STEPHEN L. · 2015 to 2019
$884k
NCI NIH HHS K08 CA191139NCI NIH HHS R01 CA220000NCRR NIH HHS G20 RR030860
6 · The paper itself

Abstract

backgroundImmune cell infiltrates (ICI) of tumors are scored by pathologists around tumor glands. To obtain a better understanding of the immune infiltrate, individual immune cell types, their activation states and location relative to tumor cells need to be determined. This process requires precise identification of the tumor area and enumeration of immune cell subtypes separately in the stroma and inside tumor nests. Such measurements can be accomplished by a multiplex format using immunohistochemistry (IHC).

methodWe developed a pipeline that combines immunohistochemistry (IHC) and digital image analysis. One slide was stained with pan-cytokeratin and CD45 and the other slide with CD8, CD4 and CD68. The tumor mask generated through pan-cytokeratin staining was transferred from one slide to the other using affine image co-registration. Bland-Altman plots and Pearson correlation were used to investigate differences between densities and counts of immune cell underneath the transferred versus manually annotated tumor masks. One-way ANOVA was used to compare the mask transfer error for tissues with solid and glandular tumor architecture.

resultsThe overlap between manual and transferred tumor masks ranged from 20%-90% across all cases. The error of transferring the mask was 2- to 4-fold greater in tumor regions with glandular compared to solid growth pattern (p < 10

conclusionsIn summary, we developed a general method to integrate data from IHC stained slides into a single dataset. Because of the transfer error between slides, we recommend applying the antibody for demarcation of the tumor on the same slide as the ICI antibodies.

Indexed as

Biomarkers, TumorBreast NeoplasmsCell CountCohort StudiesFemaleHumansImage Processing, Computer-AssistedImmunohistochemistryInflammationKeratinsLeukocyte Common AntigensBiomarkers, TumorKeratinsLeukocyte Common AntigensPTPRC protein, humanBreast cancerImage analysisImmunohistochemistryQuantitative imagingTumor immune infiltrate

Identifiers

PMID28923066
PMCPMC5604347

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.