Evidence map›Paper›PMID 42179893›Full record

ArticleJAAD international2026

Assessment of high- and low-risk histopathologic subtypes of basal cell carcinoma using artificial intelligence and dermatologist evaluation.

Victor Liang, Parasto Shahrouki, Daniel Nejad, Eva Backman, Magdalena Claeson, Åsa Ingvar, Isabelle Krakowski, Kari Nielsen, Jenna Pakka, John Paoli and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JAAD international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Victor LiangDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Parasto ShahroukiDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Daniel NejadDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Eva BackmanDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Magdalena ClaesonDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Åsa IngvarDivision of Dermatology, Department of Clinical Sciences, and Lund University Cancer Center, LUCC, Faculty of Medicine, Lund University, Lund, Sweden.
Isabelle KrakowskiDepartment of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Kari NielsenDivision of Dermatology, Department of Clinical Sciences, and Lund University Cancer Center, LUCC, Faculty of Medicine, Lund University, Lund, Sweden.
Jenna PakkaDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
John PaoliDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Niki RadrosTheme Cancer, Karolinska University Hospital, Stockholm, Sweden.
Mari SalmivuoriDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Sam PolesieDepartment of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Basal cell carcinoma (BCC) is the most common skin cancer, and accurate histopathologic risk stratification is essential for treatment selection. Convolutional neural networks (CNNs) demonstrate promise in image analysis, yet few studies have evaluated classification of BCC subtypes. Objective: To assess the diagnostic performance of a CNN for binary classification of high- Methods: In this retrospective single-center study, 1580 dermoscopic images of histopathologically confirmed BCCs were used to train a preconditioned CNN. A test set ( Results: The CNN achieved an AUC of 0.75 (95% confidence interval [CI], 0.69-0.81) comparable to the dermatologists (AUC 0.79, 95% CI: 0.73-0.84, Limitations: Retrospective single-center design and limited metadata for the CNN. Conclusion: CNNs appear useful for binary classification of BCC histopathologic subtypes.

Indexed as

artificial intelligencebasal cell carcinomaconvolutional neural networkmachine learning

Identifiers

PMID42179893
PMCPMC13196092

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.