Histological interpretation of spitzoid tumours: an extensive machine learning‐based concordance analysis for improving decision making Article Swipe
Andrés Mosquera‐Zamudio
,
Laëtitia Launet
,
Adrián Colomer
,
Katharina Wiedemeyer
,
Juan Carlos Hiromi López-Takegami
,
Luis F Palma
,
Erling Undersrud
,
Emiel A. M. Janssen
,
Thomas Brenn
,
Valery Naranjo
,
Carlos Monteagudo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1111/his.15187
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1111/his.15187
The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms that objectively categorise the most relevant features in order of importance. The data set comprises 122 tumours (39 benign, 44 atypical and 39 malignant) from four different countries. BRAF and NRAS mutation status was evaluated in 51. Analysis of variance score was performed to rank 22 clinicopathological variables. The Gaussian naive Bayes algorithm achieved in distinguishing Spitz naevus from malignant spitzoid tumours with an accuracy of 0.95 and kappa score of 0.87, utilising the 12 most important variables. For benign versus non‐benign Spitz tumours, the test reached a kappa score of 0.88 using the 13 highest‐scored features. Furthermore, for the atypical Spitz tumours (AST) versus Spitz melanoma comparison, the logistic regression algorithm achieved a kappa value of 0.66 and an accuracy rate of 0.85. When the three categories were compared most AST were classified as melanoma, because of the similarities on histological features between the two groups. Our results show promise in supporting the histological classification of these tumours in clinical practice, and provide valuable insight into the use of ML to improve the accuracy and objectivity of this process while minimising interobserver variability. These proposed algorithms represent a potential solution to the lack of a clear threshold for the Spitz/spitzoid tumour classification, and its high accuracy supports its usefulness as a helpful tool to improve diagnostic decision‐making.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/his.15187
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/his.15187
- OA Status
- bronze
- Cited By
- 3
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394767405
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394767405Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1111/his.15187Digital Object Identifier
- Title
-
Histological interpretation of spitzoid tumours: an extensive machine learning‐based concordance analysis for improving decision makingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-12Full publication date if available
- Authors
-
Andrés Mosquera‐Zamudio, Laëtitia Launet, Adrián Colomer, Katharina Wiedemeyer, Juan Carlos Hiromi López-Takegami, Luis F Palma, Erling Undersrud, Emiel A. M. Janssen, Thomas Brenn, Valery Naranjo, Carlos MonteagudoList of authors in order
- Landing page
-
https://doi.org/10.1111/his.15187Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/his.15187Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/his.15187Direct OA link when available
- Concepts
-
Concordance, Cohen's kappa, Artificial intelligence, Medicine, Kappa, Naive Bayes classifier, Logistic regression, Machine learning, Pathology, Computer science, Internal medicine, Mathematics, Support vector machine, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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