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14 results on '"Senín, María Alicia"'

1. Impact of somatic gene mutations on the risk of thrombosis in myelofibrosis

Author

Pastor-Galán, Irene, Pereira, Arturo, Arellano-Rodrigo, Eduardo, Martín, Iván, Mosquera-Orgueira, Adrián, Gómez-Casares, María-Teresa, Hernández-Sánchez, Alberto, Ferrer-Marín, Francisca, Mora, Elvira, Velez, Patricia, Ayala, Rosa, Angona, Anna, de las Heras, Natalia, Magro, Elena, Mata-Vázquez, María-Isabel, Fox, María-Laura, González de Villambrosía, Sonia, Ramírez, María-José, García, Ana, García-Gutiérrez, Valentín, Cáceres, Amparo, Durán, María-Antonia, Senín, María-Alicia, Raya, José-María, González, José Antonio, Cuevas, Beatriz, Xicoy, Blanca, Garrote, Marta, Ferrer, Blanca, Pérez-Encinas, Manuel, Hernández-Rivas, Jesús María, Bellosillo, Beatriz, Álvarez-Larrán, Alberto, and Hernández-Boluda, Juan Carlos

Published
2024
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2. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Author

Mosquera Orgueira, Adrián, Pérez Encinas, Manuel, Hernández Sánchez, Alberto, González Martínez, Teresa, Arellano Rodrigo, Eduardo, Martínez Elicegui, Javier, Villaverde Ramiro, Ángela, Raya, José María, Ayala, Rosa, Ferrer Marín, Francisca, Fox, María Laura, Velez, Patricia, Mora, Elvira, Xicoy, Blanca, Mata Vázquez, María Isabel, García Fortes, María, Angona, Anna, Cuevas, Beatriz, Senín, María Alicia, Ramírez Payer, Angel, Ramírez, María José, Pérez López, Raúl, González de Villambrosía, Sonia, Martínez Valverde, Clara, Gómez Casares, María Teresa, García Hernández, Carmen, Gasior, Mercedes, Bellosillo Paricio, Beatriz, Steegmann, Juan Luis, Álvarez Larrán, Alberto, Hernández Rivas, Jesús María, Hernández Boluda, Juan Carlos, The Spanish MPN Group (GEMFIN)., Institut Català de la Salut, [Mosquera-Orgueira A, Pérez-Encinas M] Hospital Clínico Universitario, Santiago de Compostela, Spain. [Hernández-Sánchez A, González-Martínez T, Martínez-Elicegui J] Hospital Clínico, Salamanca, Spain. [Arellano-Rodrigo E] Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain. [Fox ML] Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Vall d’Hebron Hospital Universitari, Barcelona, Spain, and Vall d'Hebron Barcelona Hospital Campus

Subjects
Intel·ligència artificial - Aplicacions a la medicina, Pronòstic mèdic, Mielofibrosi, Immunology, Myelofibrosis, Neoplasms::Neoplasms by Histologic Type::Leukemia::Leukemia, Myeloid::Leukemia, Myeloid, Acute [DISEASES], enfermedades hematológicas y linfáticas::enfermedades hematológicas::enfermedades de la médula ósea::trastornos mieloproliferativos [ENFERMEDADES], Cell Biology, Hematology, Prognosis, Biochemistry, Ciencias de la información::metodologías computacionales::algoritmos::inteligencia artificial::aprendizaje automático [CIENCIA DE LA INFORMACIÓN], neoplasias::neoplasias por tipo histológico::leucemia::leucemia mieloide::leucemia mieloide aguda [ENFERMEDADES], Aprenentatge automàtic, Machine learning, Sang - Malalties, Hemic and Lymphatic Diseases::Hematologic Diseases::Bone Marrow Diseases::Myeloproliferative Disorders [DISEASES], Information Science::Computing Methodologies::Algorithms::Artificial Intelligence::Machine Learning [INFORMATION SCIENCE]
Abstract

Aprendizaje automático; Mielofibrosis Aprenentatge automàtic; Mielofibrosi Machine learning; Myelofibrosis Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification.

Published
2023

3. Coexisting Myeloproliferative and Lymphoproliferative Neoplasms: A European Multicenter Retrospective Study

Author

Fiallo Suarez, Dolly Viviana Viviana, Stuckey, Ruth, Bilbao, Cristina, Tapia Torres, Maria, Lemes Castellano, Maria Angelina, Fernández-Fuertes, Fernando, Pérez-Ortiz, Leonor, Borsani, Oscar, Rumi, Elisa, Ianotto, Jean-Christophe, Ferrer Marin, Francisca, Gasior Kabat, Mercedes, Cuevas, Beatriz, Lewandowski, Krzysztof, Alvarez-Larran, Alberto, Sobas, Marta Anna, Santoro, Marco, Foncillas, María Ángeles, Drozd-Sokolowska, Joanna, Kandula, Zuzanna, Hernandez Boluda, Juan Carlos, Angona, Anna, Carreño, Gonzalo, Senin, María Alicia, Mata Vazquez, Maria Isabel, Fox, Maria Laura, Del Orbe, Rafael, Velez Tenza, Patricia, Vallansot, Rolando, and Gómez-Casares, María Teresa

Published
2022
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4. Toxicity of Asciminib in Real Clinical Practice; Analysis of Side Effects and Cross-Intolerance with Tyrosine Kinase Inhibitors

Author

Pérez-Lamas, Lucía, Luna, Alejandro, Boque, Concepcion, Senin, María Alicia, Xicoy, Blanca, Giraldo, Pilar, Perez Lopez, Raul, Ruiz Nuño, Concepción, De las Heras, Natalia, Mora Casterá, Elvira, Garcia-Hernandez, Carmen, Segura Diaz, Adrian, Steegmann, Juan Luis, Velez Tenza, Patricia, Sanchez-Guijo, Fermin, Garcia-Noblejas Moya, Ana Maria, Juan Vera Goñi, Juan Antonio, Moreno Vega, Melania, Alvarez-Larran, Alberto, Cortes, Montse, Perez Encinas, Manuel, Serrano, Luis, Angona, Anna, Rosell, Ana, Lakhwani, Sunil, Colorado, Mercedes, Ramila, Elena, Cervero, Carlos, Cuevas, Beatriz, Villalon Blanco, Lucia, Hernandez Boluda, Juan Carlos, Paz Coll, Antonio, Gómez García de Soria, Valle, Fernández, Maria Jose, Casado, Luis Felipe, Alonso-Dominguez, Juan Manuel, Anguita Arance, Maria Magdalena, Carrascosa Mastell, Patricia, Salamanca Cuenca, Araceli, Jiménez-Velasco, Antonio, Ramírez, María José, López Esteban, Miguel, Sierra Pacho, Magdalena, Santaliestra, Marta, Alda Alvarez, Olga, de Paz, Raquel, and García Gutiérrez, Valentín

Published
2022
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5. Outcomes of Chronic Myeloid Leukemia Patients after Therapeutic Failure to Asciminib, a Multicenter Observational Study

Author

Pérez-Lamas, Lucía, Segura Diaz, Adrian, Mora, Elvira, Hidalgo Soto, Marta, Paz Coll, Antonio, De Las Heras, Natalia, Cortes, Montse, Velez Tenza, Patricia, Sierra Pacho, Magdalena, Senin, María Alicia, Angona, Anna, Cervero, Carlos, De Paz Arias, Raquel, Salamanca Cuenca, Araceli, Santaliestra, Marta, López Marin, Javier, Casado Montero, Luis Felipe, and García Gutiérrez, Valentín

Published
2023
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6. Clinical and Molecular Characterization of Triple-Negative Essential Thrombocythemia: Data from the Prospective Spanish Registry of Essential Thrombocythemia

Author

Gil-Manso, Rodrigo, Carreño, Gonzalo, Hernandez Boluda, Juan Carlos, Perez Lopez, Raul, Gómez-Casares, Maria Teresa, Ferrer Marin, Francisca, Arellano-Rodrigo, Eduardo, Angona, Anna, García Gutiérrez, Valentín, Noya, Maria Soledad, Senin, María Alicia, Magro, Elena, Pastor, Irene, Mata, María Isabel, Cuevas, Beatriz, Mora, Elvira, Martinez, Cristina, Alonso-Dominguez, Juan M., Pérez-Encinas, Manuel, Caballero Navarro, Gonzalo, Cortes Vázquez, Miguel A., del Río, Carmen, Martinez Lopez, Joaquin, Ayala, Rosa, and Alvarez-Larran, Alberto

Published
2023
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7. Venetoclax Exposure Reduction in Venetoclax-Hypomethylating Agents Combination Is Feasible and Has the Same Efficacy in Patients with Acute Myeloblastic Leukemia. Experience of a Single Center

Author

Arribas, Iago, Pomares, Helena, Quiñones, Teresa, Villarreal Hernandez, Jasson, Ribes-Amoros, Julia, Maluquer Artigal, Clara, Quiñones, Shiomara, Sotelo, Maria Isabel, Senin, María Alicia, Galiano, Mercedes, Gamundí, Enric, Zamora, Lurdes, Ruiz Xiville, Neus, Sureda Balari, Anna Maria, and Arnan Sangerman, Montserrat

Published
2023
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8. LONG-TERM Outcomes after Venetoclax-Hypomethylating Agent Combination Therapy Discontinuation, in Patients with ACUTE Myeloid Leukemia. Experience of a Single Center

Author

Pomares, Helena, Arribas, Iago, Quiñones, Teresa, Villarreal Hernandez, Jasson, Ribes Amoros, Julia, Maluquer Artigal, Clara, Quiñones, Shiomara, Sotelo, Maria Isabel, Senin, María Alicia, Galiano, Mercedes, Gamundi, Enric, Zamora, Lurdes, Ruiz Xiville, Neus, Sureda Balari, Anna Maria, and Arnan Sangerman, Montserrat

Published
2023
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9. Retrospective Single Center Descriptive Analysis of a Cohort with Hypereosinophilia Evaluated in the Last Decade

Author

Ribes-Amoros, Julia, Alonso, Esther, Granada, Isabel, Zamora, Lurdes, Arnan Sangerman, Montserrat, Climent, Fina, Pomares, Helena, Maluquer Artigal, Clara, Villarreal Hernandez, Jasson, Quiñones, Teresa, Arribas, Iago, Quiñones, Shiomara, Sotelo, Maria Isabel, Encuentra, Maria Teresa, Fernandez-Benages, Mònica, Cisneros, Adela, Bosch-Schips, Jan, Ratia, Miriam, Sureda Balari, Anna Maria, and Senin, María Alicia

Published
2023
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10. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Author

Mosquera Orgueira, Adrian, Perez Encinas, Manuel, Hernández Sánchez, Alberto, González, Teresa, Arellano-Rodrigo, Eduardo, Martínez Elicegui, Javier Martinez, Villaverde Ramiro, Angela, Raya Sanchez, Jose Maria, Ayala, Rosa, Ferrer Marin, Francisca, Fox, Maria Laura, Velez Tenza, Patricia, Mora Casterá, Elvira, Xicoy, Blanca, Mata Vazquez, Maria Isabel, García Fortes, María, Angona, Anna, Cuevas, Beatriz, Senin, María Alicia, Ramírez Payer, Angel, Ramírez, María José, Perez Lopez, Raul, González de Villambrosia, Sonia, Martínez, Clara, Gómez-Casares, María Teresa, Garcia-Hernandez, Carmen, Gasior Kabat, Mercedes, Bellosillo, Beatriz, Steegmann, Juan Luis, Alvarez-Larran, Alberto, Hernández Rivas, Jesús M, and Hernandez Boluda, Juan Carlos

Published
2022
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11. A Conservative Tailoring of Ponatinib Dose in Chronic Myeloid Leukemia Patients Can Diminish Adverse Events While Maintaining Efficacy

Author

García Gutiérrez, Valentín, Novo, Andrés, Jimenez-Velasco, Antonio, Casado Montero, Luis Felipe, Sanchez-Guijo, Fermin, Perez Lopez, Raul, Osorio-Prendes, Santiago, Lakhwani Lakhwani, Sunil, Ramirez Payer, Angel, Orti, Guillermo, Gomez-Casares, Maria Teresa, Alonso, Juan Manuel, Pérez-Encinas, Manuel, Vallansot, Rolando, Pérez Persona, Ernesto, Portero Frias, Maria Angeles, Sánchez, María José Ramírez, Senin, Maria Alicia, Caballero, Gonzalo, Noya, Soledad, Boque, Concepcion, Mata, Maribel, Romo, Mario, Steegmann, Juan Luis, and Hernandez-Boluda, Juan Carlos

Published
2017
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12. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis.

Author

Mosquera-Orgueira, Adrián, Pérez-Encinas, Manuel, Hernández-Sánchez, Alberto, González-Martínez, Teresa, Arellano-Rodrigo, Eduardo, Martínez-Elicegui, Javier, Villaverde-Ramiro, Ángela, Raya, José-María, Ayala, Rosa, Ferrer-Marín, Francisca, Fox, María-Laura, Velez, Patricia, Mora, Elvira, Xicoy, Blanca, Mata-Vázquez, María-Isabel, García-Fortes, María, Angona, Anna, Cuevas, Beatriz, Senín, María-Alicia, and Ramírez-Payer, Angel

Published
2023
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13. Integrating AIPSS-MF and molecular predictors: A comparative analysis of prognostic models for myelofibrosis.

Author

Mosquera-Orgueira A, Arellano-Rodrigo E, Garrote M, Martín I, Pérez-Encinas M, Gómez-Casares MT, Hernández-Sánchez A, Ferrer-Marín F, Mora E, Velez P, Ayala R, Angona A, Heras NL, Magro E, Pérez-Míguez C, Crucitti D, Mata-Vázquez MI, Fox ML, González de Villambrosía S, Ramírez MJ, García A, García-Gutiérrez V, Cáceres A, Durán MA, Senín MA, Raya JM, González JA, Cuevas B, Xicoy B, Nangalia J, Hernández-Rivas JM, Bellosillo B, Álvarez-Larrán A, and Hernández-Boluda JC

Abstract

Competing Interests: The authors declare no conflict of interest.

Published
2024
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14. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis.

Author

Mosquera-Orgueira A, Pérez-Encinas M, Hernández-Sánchez A, González-Martínez T, Arellano-Rodrigo E, Martínez-Elicegui J, Villaverde-Ramiro Á, Raya JM, Ayala R, Ferrer-Marín F, Fox ML, Velez P, Mora E, Xicoy B, Mata-Vázquez MI, García-Fortes M, Angona A, Cuevas B, Senín MA, Ramírez-Payer A, Ramírez MJ, Pérez-López R, González de Villambrosía S, Martínez-Valverde C, Gómez-Casares MT, García-Hernández C, Gasior M, Bellosillo B, Steegmann JL, Álvarez-Larrán A, Hernández-Rivas JM, and Hernández-Boluda JC

Abstract

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification., (Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Hematology Association.)

Published
2022
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