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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
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.
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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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