Publicacións (36) Publicacións de Adrián Mosquera Orgueira Ver datos de investigación referenciados.

2024

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

    Leukemia

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

    HemaSphere

  3. Prognostic and survival factors in head and neck extra-nodal non-Hodgkin's lymphoma

    Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology

  4. The prognostic impact of non-driver gene mutations and variant allele frequency in primary myelofibrosis

    American Journal of Hematology

  5. Validation of the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes in chronic myelomonocytic leukaemia: A novel approach for improved risk stratification

    British Journal of Haematology, Vol. 204, Núm. 4, pp. 1529-1535

2023

  1. A prognostic model based on gene expression parameters predicts a better response to bortezomib-containing immunochemotherapy in diffuse large B-cell lymphoma

    Frontiers in Oncology, Vol. 13

  2. AIPSS-MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib

    Cancer Reports, Vol. 6, Núm. 10

  3. Corrigendum: Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia(Front. Oncol., (2022), 12, (968340), 10.3389/fonc.2022.968340)

    Frontiers in Oncology

  4. Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes

    HemaSphere, Vol. 7, Núm. 10, pp. E961

  5. Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

    HemaSphere, Vol. 7, Núm. 1, pp. E818

  6. The implication of next-generation sequencing in the diagnosis and clinical management of non-Hodgkin lymphomas

    Frontiers in Oncology, Vol. 13

2022

  1. A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable MRD and Survival Outcomes in Multiple Myeloma

    Clinical cancer research : an official journal of the American Association for Cancer Research

  2. Erratum: Correction: Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia (PloS one (2021) 16 2 (e0247093))

    PloS one

  3. Erratum: Gene expression profiling identifies FLT3 mutation-like cases in wildtype FLT3 acute myeloid leukemia (PLoS ONE (2021) 16:2 (e0247093) DOI: 10.1371/journal.pone.0247093)

    PLoS ONE

  4. Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia

    Frontiers in Oncology, Vol. 12

  5. Immune Checkpoint Inhibitors in Acute Myeloid Leukemia: A Meta-Analysis

    Frontiers in Oncology, Vol. 12

  6. Prognostic Stratification of Diffuse Large B-cell Lymphoma Using Clinico-genomic Models: Validation and Improvement of the LymForest-25 Model

    HemaSphere, Vol. 6, Núm. 4

  7. Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model

    HemaSphere, Vol. 6, Núm. 8

  8. Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group

    Blood cancer journal, Vol. 12, Núm. 4, pp. 76

2021

  1. Detection of new drivers of frequent B-cell lymphoid neoplasms using an integrated analysis of whole genomes

    PloS one, Vol. 16, Núm. 5, pp. e0248886