Juan Carlos Hernández-Boluda, MD, PhD, is an associated physician at the Hematology and Medical Oncology Service of the Hospital Clínico of Valencia

Juan Carlos Hernández-Boluda, MD, PhD, details the advantages of EBMT machine learning vs existing transplant risk assessment tools in myelofibrosis.

"The technology [of this machine learning model] is completely different and more accurate than the available models. It’s also far more comprehensive because it is based on data from [5183] patients transplanted within EBMT centers and includes [data like] graft source and methods of graft-vs-host disease."

Juan Carlos Hernández-Boluda, MD, PhD, an associated physician at the Hematology and Medical Oncology Service of the Hospital Clínico of Valencia, discussed the development of a unique 

 to predict survival outcomes following allogeneic hematopoietic cell transplant (allo-HCT) in patients with myelofibrosis and expanded on how this model potentially improves upon existing transplant risk assessment tools.

Hernández-Boluda and colleagues at the EBMT developed a random survival forest (RSF) machine learning model using data from 5183 adult patients with primary or secondary myelofibrosis who first underwent allo-HCT between 2005 and 2020 across EBMT centers. The RSF model demonstrated superior predictive performance compared with other machine learning approaches, yielding higher concordance indices across subgroups of patients with both primary and secondary myelofibrosis. The model also stratified 25% of patients as high risk for non-relapse mortality. The open-access tool was officially launched on March 27, 2025, and is now freely available online.

What distinguishes this EBMT model from prior transplant risk assessment tools is its integration of contemporary, large-scale data and use of RSF algorithms, which can handle complex, nonlinear interactions without assumptions about variable distributions, Hernández-Boluda explained. The model’s accuracy reflects the inclusion of detailed clinical parameters—such as graft source and graft-vs-host disease prophylaxis strategies—collected from a total of 288 EBMT centers, which currently represent standards of care in allo-HCT for patients with myelofibrosis, he detailed. Unlike older models that relied on smaller cohorts or less granular data, this RSF tool provides more individualized risk prediction and is aligned with modern transplant practices, Hernández-Boluda concluded.

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“[There are] some limitations of the prognostic scores that we had so far [for determining patient transplant eligibility], and it will be very good to have a tool—a prognostic tool—in order to decide which patients should be taken to transplant, to conventional treatment, or even to clinical trials.”

Juan Carlos Hernández-Boluda, MD, PhD, an associated physician at the Hematology and Medical Oncology Service of the Hospital Clínico of Valencia, discussed the rationale for investigating the use of an EBMT machine learning model to predict survival outcomes following allogeneic hematopoietic cell transplant (allo-HCT) in patients with myelofibrosis.

Hernández-Boluda began by stating that he has over 15 years of experience using nontransplant therapies to treat patients with myelofibrosis. In this setting, he has used prognostic scoring systems to help guide treatment decisions, he stated. Recently, Hernández-Boluda and colleagues applied machine learning methodologies to develop more reliable prognostic tools for patients with myelofibrosis receiving nontransplant treatment, he explained. Based on this work, Hernández-Boluda identified limitations in existing transplant-related prognostic models as well as the potential value of a more robust prognostic tool to better inform decisions regarding patient eligibility for transplant vs other standard therapies vs enrollment in clinical trials.

Accordingly, Hernández-Boluda and colleagues developed an interactive, web-based machine-learning model to predict overall survival (OS) outcomes following allo-HCT among patients with myelofibrosis. A study evaluating the efficacy of different machine-learning models used data from 5183 patients with myelofibrosis who first underwent allo-HCT between 2005 and 2020 at EBMT centers. Investigators developed a Random Survival Forests (RSF) model that used patient baseline characteristics to predict OS outcomes. This RSF model outperformed all comparator machine-learning models used in the study, as it had better concordance indices across subgroups of patients with primary and secondary myelofibrosis. Furthermore, the RSF model identified 25% of patients as being at high risk for non-relapse mortality.

Juan Carlos Hernández-Boluda, MD, PhD, discusses the rationale for using machine learning to predict OS outcomes after allo-HCT in myelofibrosis.

Transplant Risk in Myelofibrosis Juan Carlos Hernández-Boluda, MD, PhD, an associated physician at the Hematology and Medical Oncology Service of the Hospital Clínico of Valencia, discusses an EBMT machine learning–based model for identifying and stratifying transplant risk for patients with myelofibrosis.

Dr Hernández-Boluda on an EBMT Machine Learning Model for Identifying and Stratifying Transplant Risk in Myelofibrosis

Juan Carlos Hernández-Boluda, MD, PhD, an associated physician at the Hematology and Medical Oncology Service of the Hospital Clínico of Valencia, discussed an EBMT machine learning–based model for identifying and stratifying transplant risk for patients with myelofibrosis.

On March 27, 2025, a team from the European Society for Blood and Marrow Transplantation (EBMT) announced that a machine learning model they had developed for identifying and stratifying transplant risk for patients with myelofibrosis outperformed standard statistical models. The machine learning approach was trained using records of 3887 patients with myelofibrosis who received their allogeneic hematopoietic stem cell transplantation (allo-HCT) between 2005 and 2020. Data from 1296 patients were used to assess and validate the model. 

The open-access tool outperformed standard models in terms of accuracy, identifying a subset of patients with high-risk disease who had a 40% chance of dying within 1 year of transplant and a non-relapse mortality rate of approximately 35%. Moreover, the model identified 25% of patients as being a part of this group at a high-risk for poor outcomes after transplantation, compared with 10.1% via a Cox-derived score and 8.2% using the Center for International Blood and Marrow Transplant Research model.

When determining if a patient with myelofibrosis is a good candidate for allo-HCT, investigators must first determine if the patient falls into the high-risk category using standardized prognostic scoring systems, Hernández-Boluda explained. However, currently available approaches are not very good at predicting the toxicity of the transplant for the patient, he continued. The machine learning tool will be able to provide both risk stratification information and transplantation toxicity risk, he said.

The machine learning approach can also visualize the transplantation risk, and this image could be shared with patients to better inform them of their risk category, Hernández-Boluda said. Combining previous approaches with the machine learning tool will help clinicians make a better-informed decision regarding allo-HCT, he concluded.

Juan Carlos Hernández-Boluda, MD, PhD, discusses an EBMT machine learning–based model for identifying and stratifying transplant risk in myelofibrosis.

Juan Carlos Hernández-Boluda, MD, PhD

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