Evidence map›Paper›PMID 36268094›Full record

ArticleJournal of pathology informatics2022

A digital method to interpret the C-MYC stain in diffuse large B cell lymphoma.

Jayalakshmi Balakrishna, Jesse Kulewsky, Anil Parwani

Abstract read
In one paragraph

Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jayalakshmi BalakrishnaDepartment of pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.
Jesse KulewskyDepartment of pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.
Anil ParwaniDepartment of pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS) is a heterogenous group of aggressive lymphomas. C-MYC expression by immunohistochemical stain (IHC) is shown to be an independent prognostic factor in DLBCL. In the clinical setting, MYC stain is currently evaluated by manual quantification with a minimum positivity cut-off 40%. Manual quantification methods can be subjective and may show intra- and interobserver variability and variability between centers. Thus, stains which require definitive quantification such as MYC needs better standardized and precise methods. Here we present a simple digital algorithm for quantitative evaluation of MYC stain in DLBCL, NOS. For this, slides immunostained for C-MYC were scanned at 40X with a high-resolution, Philips Ultra Fast scanner (Koninklijke Philips N.V. Cambridge, MA). The images were manually assessed and appropriate areas with neoplastic cells were selected. For quantification, positive and negative C-MYC staining nuclei were scored using a modified Visiopharm APP Nuclei Detection, AI (Brightfield) using Visiopharm Image Analysis software (Visiopharm, Hørsholm, Denmark version 2018.09). The percentage positivity resulted by the digital method was concordant with the pathologist's interpretation with statistical significance (rs: 0.85968; p (2-tailed) = 0). Minor disadvantages were observed including failure to detect very weak staining and inability to separate neoplastic and non-neoplastic nuclei when admixed in the same area. If combined with a quick manual evaluation, a digital method like this with precision and reproducibility will be of great use in quantitative evaluation of MYC and other similar stains in clinical setting and will reduce intra- and interobserver variability.

Indexed as

C-MYCDiffuse large B-cell lymphomaDigital quantificationImage analysisImmunohistochemistry

Identifiers

PMID36268094
PMCPMC9576988

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LicenceCC BY-NC-ND
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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.