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Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes
Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantati...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569758/ https://www.ncbi.nlm.nih.gov/pubmed/37841754 http://dx.doi.org/10.1097/HS9.0000000000000961 |
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author | Mosquera Orgueira, Adrian Perez Encinas, Manuel Mateo Diaz Varela, Nicolas A Mora, Elvira Díaz-Beyá, Marina Montoro, María Julia Pomares, Helena Ramos, Fernando Tormo, Mar Jerez, Andres Nomdedeu, Josep F De Miguel Sanchez, Carlos Leonor, Arenillas Cárcel, Paula Cedena Romero, Maria Teresa Xicoy, Blanca Rivero, Eugenia del Orbe Barreto, Rafael Andres Diez-Campelo, Maria Benlloch, Luis E. Crucitti, Davide Valcárcel, David |
author_facet | Mosquera Orgueira, Adrian Perez Encinas, Manuel Mateo Diaz Varela, Nicolas A Mora, Elvira Díaz-Beyá, Marina Montoro, María Julia Pomares, Helena Ramos, Fernando Tormo, Mar Jerez, Andres Nomdedeu, Josep F De Miguel Sanchez, Carlos Leonor, Arenillas Cárcel, Paula Cedena Romero, Maria Teresa Xicoy, Blanca Rivero, Eugenia del Orbe Barreto, Rafael Andres Diez-Campelo, Maria Benlloch, Luis E. Crucitti, Davide Valcárcel, David |
author_sort | Mosquera Orgueira, Adrian |
collection | PubMed |
description | Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems. |
format | Online Article Text |
id | pubmed-10569758 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-105697582023-10-13 Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes Mosquera Orgueira, Adrian Perez Encinas, Manuel Mateo Diaz Varela, Nicolas A Mora, Elvira Díaz-Beyá, Marina Montoro, María Julia Pomares, Helena Ramos, Fernando Tormo, Mar Jerez, Andres Nomdedeu, Josep F De Miguel Sanchez, Carlos Leonor, Arenillas Cárcel, Paula Cedena Romero, Maria Teresa Xicoy, Blanca Rivero, Eugenia del Orbe Barreto, Rafael Andres Diez-Campelo, Maria Benlloch, Luis E. Crucitti, Davide Valcárcel, David Hemasphere Article Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems. Lippincott Williams & Wilkins 2023-10-11 /pmc/articles/PMC10569758/ /pubmed/37841754 http://dx.doi.org/10.1097/HS9.0000000000000961 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Hematology Association. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Article Mosquera Orgueira, Adrian Perez Encinas, Manuel Mateo Diaz Varela, Nicolas A Mora, Elvira Díaz-Beyá, Marina Montoro, María Julia Pomares, Helena Ramos, Fernando Tormo, Mar Jerez, Andres Nomdedeu, Josep F De Miguel Sanchez, Carlos Leonor, Arenillas Cárcel, Paula Cedena Romero, Maria Teresa Xicoy, Blanca Rivero, Eugenia del Orbe Barreto, Rafael Andres Diez-Campelo, Maria Benlloch, Luis E. Crucitti, Davide Valcárcel, David Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes |
title | Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes |
title_full | Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes |
title_fullStr | Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes |
title_full_unstemmed | Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes |
title_short | Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes |
title_sort | machine learning improves risk stratification in myelodysplastic neoplasms: an analysis of the spanish group of myelodysplastic syndromes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569758/ https://www.ncbi.nlm.nih.gov/pubmed/37841754 http://dx.doi.org/10.1097/HS9.0000000000000961 |
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