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Machine learning to predict overall short-term mortality in cutaneous melanoma
BACKGROUND: Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889591/ https://www.ncbi.nlm.nih.gov/pubmed/36719475 http://dx.doi.org/10.1007/s12672-023-00622-5 |
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author | Cozzolino, C. Buja, A. Rugge, M. Miatton, A. Zorzi, M. Vecchiato, A. Del Fiore, P. Tropea, S. Brazzale, A. Damiani, G. dall’Olmo, L. Rossi, C. R. Mocellin, S. |
author_facet | Cozzolino, C. Buja, A. Rugge, M. Miatton, A. Zorzi, M. Vecchiato, A. Del Fiore, P. Tropea, S. Brazzale, A. Damiani, G. dall’Olmo, L. Rossi, C. R. Mocellin, S. |
author_sort | Cozzolino, C. |
collection | PubMed |
description | BACKGROUND: Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival. METHODS: CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool. RESULTS: The univariate analysis confirmed the significant prognostic value of TNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction. CONCLUSIONS: Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented (unipd.link/melanomaprediction). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model’s accuracy beyond the original research context. |
format | Online Article Text |
id | pubmed-9889591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-98895912023-02-02 Machine learning to predict overall short-term mortality in cutaneous melanoma Cozzolino, C. Buja, A. Rugge, M. Miatton, A. Zorzi, M. Vecchiato, A. Del Fiore, P. Tropea, S. Brazzale, A. Damiani, G. dall’Olmo, L. Rossi, C. R. Mocellin, S. Discov Oncol Research BACKGROUND: Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival. METHODS: CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool. RESULTS: The univariate analysis confirmed the significant prognostic value of TNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction. CONCLUSIONS: Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented (unipd.link/melanomaprediction). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model’s accuracy beyond the original research context. Springer US 2023-01-31 /pmc/articles/PMC9889591/ /pubmed/36719475 http://dx.doi.org/10.1007/s12672-023-00622-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Cozzolino, C. Buja, A. Rugge, M. Miatton, A. Zorzi, M. Vecchiato, A. Del Fiore, P. Tropea, S. Brazzale, A. Damiani, G. dall’Olmo, L. Rossi, C. R. Mocellin, S. Machine learning to predict overall short-term mortality in cutaneous melanoma |
title | Machine learning to predict overall short-term mortality in cutaneous melanoma |
title_full | Machine learning to predict overall short-term mortality in cutaneous melanoma |
title_fullStr | Machine learning to predict overall short-term mortality in cutaneous melanoma |
title_full_unstemmed | Machine learning to predict overall short-term mortality in cutaneous melanoma |
title_short | Machine learning to predict overall short-term mortality in cutaneous melanoma |
title_sort | machine learning to predict overall short-term mortality in cutaneous melanoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889591/ https://www.ncbi.nlm.nih.gov/pubmed/36719475 http://dx.doi.org/10.1007/s12672-023-00622-5 |
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