Evidence map›Paper›PMID 41118723›Full record

ArticleCell reports methods2025

Deep learning finds convergent melanocytic morphology despite noisy archival slides.

Mikio Tada, Garrett Gaskins, Sina Ghandian, Nicholas Mew, Michael James Keiser, Elizabeth Sarah Keiser

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2025. 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

6 authors.

Mikio TadaInstitute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA 94158, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
Garrett GaskinsInstitute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA 94158, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
Sina GhandianInstitute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA 94158, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
Nicholas MewInstitute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA 94158, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
Michael James KeiserInstitute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA 94158, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address: keiser@keiserlab.org.
Elizabeth Sarah KeiserDepartment of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Pathology, San Diego Veterans Health Care System, San Diego, CA 92161, USA. Electronic address: e.keiser@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Melanocytic atypia often leads to diagnostic discordance, complicating its prediction by machine learning models. To overcome this, we paired H&E-stained histology images with contiguous or serial sections immunohistochemically (IHC) stained for melanocytic cells via antibodies for MelanA, MelPro, or SOX10. We developed a melanocytic atypia deep learning pipeline from real-world archives of 122 paired whole slide images from 61 confirmed melanoma in situ (MIS) cases at two institutions. Only 37.7% of pairs matched well enough for deep learning; nonetheless, MelanA+MelPro models achieved an average area under the receiver-operating characteristic (AUROC) = 0.948 and area under the precision-recall curve (AUPRC) = 0.611 (9.3× baseline) and SOX10 models achieved AUROC = 0.867 and AUPRC = 0.433 (7.3× baseline). Despite learning from biologically different nuclear versus cytoplasmic IHC stains, convolutional neural network models exhibited a convergent explainable AI rationale. The resulting multi-antibody virtual stains identified cytologic and small-scale architectural features directly from H&E images, supporting pathologists in assessing cutaneous MIS.

Indexed as

Deep LearningMelanocytesMelanomaSkin NeoplasmsHumansImmunohistochemistryNeural Networks, ComputerROC Curveconvolutional neural networksCP: cell biologyCP: computational biologydermatopathologydigital pathologyexplainable AImelanamelanoma in situmelprosox10virtual stainwhole slide image

Identifiers

PMID41118723
PMCPMC12570353

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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.