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EBMT 2026 | A machine learning approach combining HLA and non-HLA factors to optimize donor selection

In this video, Roberto Crocchiolo, MD, PhD, Niguarda Metropolitan Hospital, Milan, Italy, briefly discusses a machine-learning approach that combines HLA and non-HLA factors to support the selection of the “best” donor for stem cell transplantation. This model, built using data from 7,609 transplants, has the potential to aid clinicians in optimizing the donor selection process through an interactive tool currently undergoing prospective testing. This interview took place at the 52nd Annual Meeting of the EBMT in Madrid, Spain.

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Transcript

We provided a model on a large cohort facilitated by the Gruppo Italiano per il Trapianto di Midollo Osseo, GITMO, Italian Collaborative National Study, allowing us to build a model that can combine both HLA and non-HLA factors in order to create an interactive tool that is under prospective testing that could hopefully support the transplant physician and research coordinators in selecting the optimal stem cell donor...

We provided a model on a large cohort facilitated by the Gruppo Italiano per il Trapianto di Midollo Osseo, GITMO, Italian Collaborative National Study, allowing us to build a model that can combine both HLA and non-HLA factors in order to create an interactive tool that is under prospective testing that could hopefully support the transplant physician and research coordinators in selecting the optimal stem cell donor. This could be a tool, hopefully a prospective tool that can be also validated externally, that could indeed support in the best patient’s interest.

 

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