Publicacións (17) Publicacións de Marta Sonia González Pérez

2024

  1. Predictors of unsustained measurable residual disease negativity in transplant-eligible patients with multiple myeloma

    Blood, Vol. 143, Núm. 7, pp. 597-603

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. 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

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

    Frontiers in Oncology, Vol. 12

  3. 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

  4. 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

  2. Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia

    PloS one, Vol. 16, Núm. 2, pp. e0247093

  3. Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling

    Frontiers in Oncology, Vol. 11

  4. Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models

    Frontiers in Oncology, Vol. 11

  5. Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

    Leukemia, Vol. 35, Núm. 10, pp. 2924-2935

2020

  1. FLT3 inhibitors in the treatment of acute myeloid leukemia: Current status and future perspectives

    Minerva Medica, Vol. 111, Núm. 5, pp. 427-442

  2. Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

    BMC Cancer, Vol. 20, Núm. 1

  3. Novel Mutation Hotspots within Non-Coding Regulatory Regions of the Chronic Lymphocytic Leukemia Genome

    Scientific Reports, Vol. 10, Núm. 1

  4. Treatment with daratumumab in patients with relapsed/refractory AL amyloidosis: a multicentric retrospective study and review of the literature

    Amyloid, Vol. 27, Núm. 3, pp. 163-167

2019

  1. Isatuximab plus pomalidomide and low-dose dexamethasone versus pomalidomide and low-dose dexamethasone in patients with relapsed and refractory multiple myeloma (ICARIA-MM): a randomised, multicentre, open-label, phase 3 study

    The Lancet, Vol. 394, Núm. 10214, pp. 2096-2107