Evidence map›Paper›PMID 29049424›Full record

ArticleJAMA dermatology2017

Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study.

Michael N Kent, Thomas G Olsen, Theresa A Feeser, Katherine C Tesno, John C Moad, Michael P Conroy, Mary Jo Kendrick, Sean R Stephenson, Michael R Murchland, Ayesha U Khan and 3 more

Registry-linked trialOpen access · bronzeAbstract readComparative Study
In one paragraph

Article in JAMA dermatology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07705386 (Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis), which is not on this map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
4.8field-weighted citation impact, top 4% of its field
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.

NCT07705386 active not recruitingnot on this mapstarted 2026, after this paper: background citation

Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study

TypeobservationalSponsorCairo UniversityRan2026 to 2026Enrolled463ConditionsCutaneous Lymphoproliferative Diseases, Mycosis Fungoides of Skin (Diagnosis), T Cell Dyscrasia, PLEVA-PLC SpectrumArmsAI-assisted histopathology image analysis
3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.

  1. Pooled it
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  10. Digital validation of breast biomarkers (ER, PR, AR, and HER2) in cytology specimens using three different scanners.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022
    Article
  11. Review
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  15. A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021
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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

13 authors at 4 institutions in 1 country.

Michael N KentDermatopathology Laboratory of Central States, Dayton, Ohio.
Thomas G OlsenDermatopathology Laboratory of Central States, Dayton, Ohio.
Theresa A FeeserDermatopathology Laboratory of Central States, Dayton, Ohio.
Katherine C TesnoDermatopathology Laboratory of Central States, Dayton, Ohio.
John C MoadDermatopathology Laboratory of Central States, Dayton, Ohio.
Michael P ConroyDermatopathology Laboratory of Central States, Dayton, Ohio.
Mary Jo KendrickDermatopathology Laboratory of Central States, Dayton, Ohio.
Sean R StephensonDermatopathology Laboratory of Central States, Dayton, Ohio.
Michael R MurchlandDermatopathology Laboratory of Central States, Dayton, Ohio.
Ayesha U KhanDermatopathology Laboratory of Central States, Dayton, Ohio.
Elizabeth A PeacockDermatopathology Laboratory of Central States, Dayton, Ohio.
Alexa BrumfielDermatopathology Laboratory of Central States, Dayton, Ohio.
Michael A BottomleyStatistical Consulting Center, College of Science and Mathematics, Wright State University, Dayton, Ohio.
Central Dermatology · USWright State University · USMichigan State University · USOhioHealth · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Digital pathology represents a transformative technology that impacts dermatologists and dermatopathologists from residency to academic and private practice. Two concerns are accuracy of interpretation from whole-slide images (WSI) and effect on workflow. Studies of considerably large series involving single-organ systems are lacking. Objective: To evaluate whether diagnosis from WSI on a digital microscope is inferior to diagnosis of glass slides from traditional microscopy (TM) in a large cohort of dermatopathology cases with attention on image resolution, specifically eosinophils in inflammatory cases and mitotic figures in melanomas, and to measure the workflow efficiency of WSI compared with TM. Design, Setting, and Participants: Three dermatopathologists established interobserver ground truth consensus (GTC) diagnosis for 499 previously diagnosed cases proportionally representing the spectrum of diagnoses seen in the laboratory. Cases were distributed to 3 different dermatopathologists who diagnosed by WSI and TM with a minimum 30-day washout between methodologies. Intraobserver WSI/TM diagnoses were compared, followed by interobserver comparison with GTC. Concordance, major discrepancies, and minor discrepancies were calculated and analyzed by paired noninferiority testing. We also measured pathologists' read rates to evaluate workflow efficiency between WSI and TM. This retrospective study was caried out in an independent, national, university-affiliated dermatopathology laboratory. Main Outcomes and Measures: Intraobserver concordance of diagnoses between WSI and TM methods and interobserver variance from GTC, following College of American Pathology guidelines. Results: Mean intraobserver concordance between WSI and TM was 94%. Mean interobserver concordance was 94% for WSI and GTC and 94% for TM and GTC. Mean interobserver concordance between WSI, TM, and GTC was 91%. Diagnoses from WSI were noninferior to those from TM. Whole-slide image read rates were commensurate with WSI experience, achieving parity with TM by the most experienced user. Conclusions and Relevance: Diagnosis from WSI was found equivalent to diagnosis from glass slides using TM in this statistically powerful study of 499 dermatopathology cases. This study supports the viability of WSI for primary diagnosis in the clinical setting.

Indexed as

User-Computer InterfaceDermatologistsDermatologyEosinophilsHumansImage Interpretation, Computer-AssistedInflammationMelanomaMicroscopyObserver VariationPathology, ClinicalRetrospective StudiesSkin DiseasesSkin NeoplasmsWorkflow

Identifiers

PMID29049424
PMCPMC5817435
OpenAlexW2763457723

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.