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A machine learning model based on tumor and immune biomarkers to predict undetectable measurable residual disease (MRD) in transplant-eligible multiple myeloma (MM)

Autores: Guerrero De Blois, Camila; Puig, N.; Cedena, M. T.; Goicoechea Oroz, Ibai; Pérez Menéndez, Cristina; Garcés Latre, Juan José; Botta, C.; Calasanz Abinzano, María José; Gutiérrez, N. C.; Martin-Ramos, M. L.; Oriol, A.; Ríos, R.; Teodoro-Hernández, M.; Martínez-Martínez, R.; Bargay, J.; de Arriba, F.; Palomera, L.; González-Rodríguez, A. P.; Martínez-López, J.; Lahuerta, J. J.; Rosinol, L.; Blade, J.; Mateos, M. V.; San Miguel Izquierdo, Jesús; Paiva, Bruno
Título de la revista: CLINICAL LYMPHOMA MYELOMA AND LEUKEMIA
ISSN: 2152-2650
Volumen: 21
Número: S2
Páginas: S35 - S35
Fecha de publicación: 2021
Resumen:
There is expectation of using biomarkers to personalize treatment. Yet, a successful treatment selection cannot be confirmed before 5 or 10 years of progression-free survival (PFS). Treatment individualization based on the probability of an individual patient to achieve undetectable MRD with a singular regimen, could represent a new model towards personalized treatment with fast assessment of its success. This idea has not been investigated previously.
DOI: https://doi.org/10.1016/S2152-2650(21)02128-5
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