Evidence map›Paper›PMID 42701405›Full record

ArticleJournal of pathology informatics2026

A protocol for evaluating robustness to H&E staining variation in computational pathology models.

Lydia A Schoenpflug, Nikki J van den Berg, Sonali Andani, Nanda Horeweg, Jurriaan Barkey Wolf, Tjalling Bosse, Viktor Koelzera, Maxime W Lafarge

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pathology informatics, 2026. 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

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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Lydia A SchoenpflugDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
Nikki J van den BergDepartment of Pathology, Leiden University Medical Center, Leiden, the Netherlands.
Sonali AndaniDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
Nanda HorewegDepartment of Radiation Oncology, Leiden University Medical Center, Leiden, the Netherlands.
Jurriaan Barkey WolfDepartment of Pathology, Leiden University Medical Center, Leiden, the Netherlands.
Tjalling BosseDepartment of Pathology, Leiden University Medical Center, Leiden, the Netherlands.
Viktor KoelzeraDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
Maxime W LafargeDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sensitivity to staining variation remains a major barrier to deploying computational pathology (CPath) models as hematoxylin and eosin (H&E) staining varies across labs, requiring systematic assessment of how this variability affects model prediction. Yet, current evaluation practices rarely attribute performance changes to specific, measurable staining properties, limiting interpretable robustness assessment under controlled conditions. We introduce a three-step protocol that explicitly simulates H&E staining variation toward known, measurable targets, supporting controlled assessment of model performance shifts. Step 1: Select reference staining conditions, Step 2: Characterize test set staining properties, Step 3: Apply CPath model(s) under simulated reference staining conditions. Here, we construct a new reference staining library based on the PLISM dataset. As a use case, we evaluate 306 microsatellite instability (MSI) classification models on the SurGen colorectal cancer dataset (

Indexed as

Computational pathologyDomain generalizationH&E stainingModel robustnessQuality control

Identifiers

PMID42701405
PMCPMC13545398

What OpenQuestion holds

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Registered trials

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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.