Evidence map›Paper›PMID 40610733›Full record

ArticleVirchows Archiv : an international journal of pathology2025

Computer-aided tumor cell fraction (TCF) estimation by medical students, residents, and pathologists improves inter-observer agreement while highlighting the risk of automation bias.

Ana Leni Frei, Amjad Khan, Raphaël Oberson, Stefan Reinhard, Yara Banz, Frédérique Meeuwsen, Andrew Janowczyk, Rainer Grobholz, Heather E Dawson, Alessandro Lugli and 3 more

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Article in Virchows Archiv : an international journal of pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. [The problem of determining tumor cell content].Pathologie (Heidelberg, Germany) · 2026
    Article
  3. 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

13 authors.

Ana Leni FreiInstitute of Tissue Medicine and Pathology (ITMP), University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland.
Amjad KhanInstitute of Tissue Medicine and Pathology (ITMP), University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland.
Raphaël ObersonInstitute of Tissue Medicine and Pathology (ITMP), University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland.
Stefan ReinhardInstitute of Tissue Medicine and Pathology (ITMP), University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland.
Yara BanzInstitute of Tissue Medicine and Pathology (ITMP), University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland.
Frédérique MeeuwsenDepartment of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
Andrew JanowczykDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
Rainer GrobholzMedical Faculty, University of Zurich, Zurich, Switzerland.
Heather E DawsonInstitute of Tissue Medicine and Pathology (ITMP), University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland.
Alessandro LugliInstitute of Tissue Medicine and Pathology (ITMP), University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland.
Marius IliéLaboratory of Clinical and Experimental Pathology, Hospital-Integrated Biobank (BB-0033-00025), IHU RespirERA, University Hospital Centre Nice, University Côte d'Azur, Nice, France.
Jeroen van der LaakDepartment of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
Inti ZlobecInstitute of Tissue Medicine and Pathology (ITMP), University of Bern, Murtenstrasse 31, 3008, Bern, Switzerland. Inti.zlobec@unibe.ch.ORCID http://orcid.org/0000-0001-6741-3000

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The potential for computer-aided diagnostics (CAD) to augment learning outcomes for medical education in histopathology are vast. However, over-reliance in CAD tools could lead to important errors if the output is inaccurate, such as automation bias. Here, we analyze the influence of a CAD tool for tumor cell fraction (TCF) estimation, named TCFCAD, on medical students with limited histopathological expertise, compared to pathology residents and practicing pathologists. Participants were third-year medical students from the University of Bern (n = 18), residents and pathologists from the European Masters in Molecular Pathology (EMMP; n = 23), and 63 practicing pathologists stratified by < 20 or > 20 years of professional experience (n = 28 and 32, respectively). Each group evaluated 10 colorectal cancer regions of interest (ROI) with and without TCFCAD assistance. The ground truth (GT) was evaluated by a laborious count of all tumor and non-tumor cells per ROI. All groups demonstrated reduced variability in TCF scores with TCFCAD assistance. The standard deviation (SD) of the TCF scores compared to the GT values before and after TCFCAD assistance were 9.09 to 4.95 for medical students, 9.93 to 5.55 for EMMP, and 9.98 to 5.69 and 9.9 to 6.13 for pathologists with < 20 and > 20 years of experience. For one image, both medical students and EMMP participants' scores swayed to the TCFCAD's output despite (1) tool output being inaccurate and (2) participants' original scores without TCFCAD assistance being closer to the GT value. This confirmed a trend of medical students following the tool's recommendation across all ROIs. All groups benefitted most from TCFCAD output for a tumor with marked inflammatory infiltrates. Although TCFCAD improved interobserver agreement, independently of professional experience, there is a demonstrated danger in over-relying on its output. This important bias must be addressed when training students and pathologists on the use of CAD tools.

Indexed as

AIComputer-aided diagnosticsDigital pathologyMedical educationPathology

Identifiers

PMID40610733

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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.