ArticleJournal of the European Academy of Dermatology and Venereology : JEADV2022
Assessment of melanoma thickness based on dermoscopy images: an open, web-based, international, diagnostic study.
Article in Journal of the European Academy of Dermatology and Venereology : JEADV, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis.The British journal of dermatology · 2025Pooled it
- Non-invasive Imaging Modalities for Breslow Thickness Estimation in Melanoma: A Scoping Review.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2026Review
- Emerging Artificial Intelligence Models for Estimating Breslow Thickness from Dermoscopic Images.Biomedicines · 2026Review
- Predictive models of melanoma metastasis based on dermatoscopy in an international retrospective human reader study.Nature communications · 2025Article
- Ultra-wideband raster-scanning optoacoustic mesoscopy as a histologic-grade tool for melanoma depth assessment.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Concordance in Basal Cell Carcinoma Diagnosis. Building a Proper Standard Reference to Train Artificial Intelligence Tools.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2025Article
- Comparative Dermoscopic Analysis of Melanoma In Situ Versus Thin Invasive Melanoma Considering BRAF Mutational Status.Journal of clinical medicine · 2025Article
- Early-Stage Melanoma Benchmark Dataset.Cancers · 2025Article
- Reply to Pennington, T.E.; Thompson, J.F. Sentinel Node Biopsy in Melanoma Remains a Valuable Clinical Tool. Comment on "Dixon et al. Primary Cutaneous Melanoma-Management in 2024.Journal of clinical medicine · 2025Article
- Performance of a Machine Learning Algorithm on Lesions with a High Preoperative Suspicion of Invasive Melanoma.Acta dermato-venereologica · 2024Article
- Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care - a mixed method study.Scandinavian journal of primary health care · 2024Article
- How Does a Convolutional Neural Network Trained to Differentiate between Invasive Melanoma and Melanoma In situ Generalize when Assessing Dysplastic Naevi?Acta dermato-venereologica · 2023Article
- Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network.Acta dermato-venereologica · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPreoperative assessment of whether a melanoma is invasive or in situ (MIS) is a common task that might have important implications for triage, prognosis and the selection of surgical margins. Several dermoscopic features suggestive of melanoma have been described, but only a few of these are useful in differentiating MIS from invasive melanoma.
objectiveThe primary aim of this study was to evaluate how accurately a large number of international readers, individually as well as collectively, were able to discriminate between MIS and invasive melanomas as well as estimate the Breslow thickness of invasive melanomas based on dermoscopy images. The secondary aim was to compare the accuracy of two machine learning convolutional neural networks (CNNs) and the collective reader response.
methodsWe conducted an open, web-based, international, diagnostic reader study using an online platform. The online challenge opened on 10 May 2021 and closed on 19 July 2021 (71 days) and was advertised through several social media channels. The investigation included, 1456 dermoscopy images of melanomas (788 MIS; 474 melanomas ≤1.0 mm and 194 >1.0 mm). A test set comprising 277 MIS and 246 invasive melanomas was used to compare readers and CNNs.
resultsWe analysed 22 314 readings by 438 international readers. The overall accuracy (95% confidence interval) for melanoma thickness was 56.4% (55.7%-57.0%), 63.4% (62.5%-64.2%) for MIS and 71.0% (70.3%-72.1%) for invasive melanoma. Readers accurately predicted the thickness in 85.9% (85.4%-86.4%) of melanomas ≤1.0 mm (including MIS) and in 70.8% (69.2%-72.5%) of melanomas >1.0 mm. The reader collective outperformed a de novo CNN but not a pretrained CNN in differentiating MIS from invasive melanoma.
conclusionsUsing dermoscopy images, readers and CNNs predict melanoma thickness with fair to moderate accuracy. Readers most accurately discriminated between thin (≤1.0 mm including MIS) and thick melanomas (>1.0 mm).
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