Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making

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 feature...

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Autores: Mosquera-Zamudio, A, Launet, L, Colomer, A, Wiedemeyer, K, López-Takegami, JC, Palma, LF, Undersrud, E, Janssen, E, Brenn, T, Naranjo, V, Monteagudo, C
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:INCLIVA
Repositorio:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
OAI Identifier:oai:incliva.fundanetsuite.com:p18232
Acceso en línea:https://incliva.portalinvestigacion.com/publicaciones/18232
Access Level:acceso abierto
Palabra clave:computer-aided diagnosis
histopathology
machine learning
melanocytic tumours
spitzoid tumours
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spelling Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision makingMosquera-Zamudio, ALaunet, LColomer, AWiedemeyer, KLópez-Takegami, JCPalma, LFUndersrud, EJanssen, EBrenn, TNaranjo, VMonteagudo, Ccomputer-aided diagnosishistopathologymachine learningmelanocytic tumoursspitzoid tumoursThe 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. Our machine learning models improve histopathological diagnostic decision-making, ranking the importance of the histological variables for the classification of Spitz and spitzoid tumours. imageWILEY2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://incliva.portalinvestigacion.com/publicaciones/18232HISTOPATHOLOGYISSN: 03090167ISSNe: 13652559reponame:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVAinstname:INCLIVAInglésinfo:eu-repo/semantics/openAccessoai:incliva.fundanetsuite.com:p182322026-06-07T16:35:31Z
dc.title.none.fl_str_mv Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making
title Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making
spellingShingle Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making
Mosquera-Zamudio, A
computer-aided diagnosis
histopathology
machine learning
melanocytic tumours
spitzoid tumours
title_short Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making
title_full Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making
title_fullStr Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making
title_full_unstemmed Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making
title_sort Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making
dc.creator.none.fl_str_mv Mosquera-Zamudio, A
Launet, L
Colomer, A
Wiedemeyer, K
López-Takegami, JC
Palma, LF
Undersrud, E
Janssen, E
Brenn, T
Naranjo, V
Monteagudo, C
author Mosquera-Zamudio, A
author_facet Mosquera-Zamudio, A
Launet, L
Colomer, A
Wiedemeyer, K
López-Takegami, JC
Palma, LF
Undersrud, E
Janssen, E
Brenn, T
Naranjo, V
Monteagudo, C
author_role author
author2 Launet, L
Colomer, A
Wiedemeyer, K
López-Takegami, JC
Palma, LF
Undersrud, E
Janssen, E
Brenn, T
Naranjo, V
Monteagudo, C
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv computer-aided diagnosis
histopathology
machine learning
melanocytic tumours
spitzoid tumours
topic computer-aided diagnosis
histopathology
machine learning
melanocytic tumours
spitzoid tumours
description 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. Our machine learning models improve histopathological diagnostic decision-making, ranking the importance of the histological variables for the classification of Spitz and spitzoid tumours. image
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://incliva.portalinvestigacion.com/publicaciones/18232
url https://incliva.portalinvestigacion.com/publicaciones/18232
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv WILEY
publisher.none.fl_str_mv WILEY
dc.source.none.fl_str_mv HISTOPATHOLOGY
ISSN: 03090167
ISSNe: 13652559
reponame:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
instname:INCLIVA
instname_str INCLIVA
reponame_str r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
collection r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
repository.name.fl_str_mv
repository.mail.fl_str_mv
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