Evidence map›Paper›PMID 40579737›Full record

Observational studyInternational journal of dermatology2025

Cumulative Sum Analysis-Integrated E-Learning for Differentiation Between Basal Cell Carcinoma and Non-Basal Cell Carcinoma on Optical Coherence Tomography: An Observational Cohort Study.

Tom Wolswijk, Patricia Joan Nelemans, Fieke Adan, Frank van Leersum, Daniel Kreiter, Thomas Adams, Sem van Dorsten, Klara Mosterd

Registry-linked trialAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in International journal of dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05634421 (Training Health Care Professionals in Detecting Basal Cell Carcinoma on Optical Coherence Tomography Using E-learning and Cumulative Sum Analysis), which is not on this 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.

NCT05634421 completednot on this map

Training Health Care Professionals in Detecting Basal Cell Carcinoma on Optical Coherence Tomography Using E-learning and Cumulative Sum Analysis

TypeobservationalSponsorMaastricht University Medical CenterRan2022 to 2023Enrolled40ConditionsBasal Cell CarcinomaArmsE-learning
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

8 authors.

Tom WolswijkDepartment of Dermatology, Maastricht University Medical Center +, Maastricht, the Netherlands.ORCID https://orcid.org/0000-0002-0253-8969
Patricia Joan NelemansDepartment of Epidemiology, Maastricht University, Maastricht, the Netherlands.ORCID https://orcid.org/0000-0002-9669-7353
Fieke AdanDepartment of Dermatology, Maastricht University Medical Center +, Maastricht, the Netherlands.ORCID https://orcid.org/0000-0003-3331-6610
Frank van LeersumDepartment of Dermatology, Maastricht University Medical Center +, Maastricht, the Netherlands.ORCID https://orcid.org/0000-0002-1443-6345
Daniel KreiterDepartment of Neurology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands.ORCID https://orcid.org/0000-0003-0954-1834
Thomas AdamsMaastricht University, Faculty of Health Medicine and Life Sciences, Maastricht, the Netherlands.
Sem van DorstenMaastricht University, Faculty of Health Medicine and Life Sciences, Maastricht, the Netherlands.
Klara MosterdDepartment of Dermatology, Maastricht University Medical Center +, Maastricht, the Netherlands.ORCID https://orcid.org/0000-0002-9065-3050

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe clinical implementation of optical coherence tomography (OCT) for diagnosing clinically equivocal lesions suspicious for basal cell carcinoma (BCC) is limited by an OCT assessor shortage. Cumulative sum (CUSUM) analysis enables monitoring of diagnostic performance during training. The objective was to evaluate whether CUSUM-integrated e-learning is suitable for training healthcare professionals in achieving and maintaining an acceptable error rate for differentiating BCC from non-BCC lesions on OCT. Furthermore, we explored the diagnostic accuracy of high-confidence BCC diagnoses by the newly trained assessors.

methodsA CUSUM-integrated e-learning was developed. Trainee performance was monitored by CUSUM analysis. The number of OCT scans required to achieve and maintain a predefined acceptable error rate (percentage of correct diagnoses) was evaluated. Successfully trained assessors were asked to discriminate BCC from non-BCC on 100 OCT scans for a subsequent diagnostic accuracy study. Histopathology served as the reference standard.

resultsSeventeen trainees successfully completed the training. Adequate performance was achieved and maintained after assessing a median of 385 scans (interquartile range [IQR]: 314-429). The pooled area under the curve (AUC) as measure for the ability to differentiate BCC from non-BCC lesions was 0.852 (95% confidence interval [CI]: 0.833-0.870). Pooled specificity and sensitivity for a high-confidence diagnosis were 95.4% (95% CI: 93.2-96.9) and 31.1% (95% CI: 24.2-39.0), respectively.

conclusionsCUSUM-integrated e-learning was successfully applied to train healthcare professionals to differentiate BCC from non-BCC on OCT. Trainees achieved a diagnostic accuracy suitable to start using OCT in clinical practice. This method can be applied to overcome the OCT assessor shortage and for training other medical skills.

trial registrationClinicaltrials.gov: NCT05634421.

Indexed as

Basal Cell CarcinomaComputer-Assisted InstructionDermatologySkin NeoplasmsTomography, Optical CoherenceClinical CompetenceCohort StudiesDiagnosis, DifferentialFemaleHumansMalebasal cell carcinomacumulative sum analysise‐learningoptical coherence tomographytraining

Identifiers

PMID40579737
PMCPMC12418905

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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.