Adrián Mosquera Orgueira, MD, PhD, is a specialist in Hematology and Hemotherapy and lead researcher of the Computational and Genomic Hematology Group at the Health Research Institute of Santiago de Compostela.

Mosquera Orgueira

“The study was conducted to explore the visibility and impact of machine learning tools in risk stratification for [patients with] myelofibrosis before allogeneic stem cell transplantation. This is a very complex decision-making process, and we wanted to develop a tool that could enhance the capacities of clinicians through data-driven approaches.”

Adrián Mosquera Orgueira, MD, PhD, a specialist in hematology and hemotherapy, and lead investigator of the Computational and Genomic Hematology Group at the Health Research Institute of Santiago de Compostela, discussed the development and clinical relevance of a machine learning–based prognostic model designed to support risk stratification for patients with myelofibrosis undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). The model was built using data from the EBMT registry, encompassing over 5000 patients with myelofibrosis who subsequently underwent allo-HSCT.

The model employed a random survival forest (RSF) algorithm to estimate overall survival and relapse-free survival outcomes, Mosquera Orgueira explained. Ten routinely collected baseline variables were used as inputs with the goal of producing a tool that could be broadly implemented across transplant centers. Compared with traditional statistical models, such as Cox regression, and alternative machine-learning methods, including XGBoost and deep-learning architectures, the RSF model offered modest improvements in predictive accuracy.

A key advantage of the RSF approach was its flexibility in modeling nonlinear interactions and handling high-dimensional data without relying on proportional hazards assumptions, Mosquera Orgueira noted. Internal validation demonstrated the model’s capacity to stratify patients by post-transplant outcomes without compromising generalizability through cross-validation techniques.

However, the model’s predictive power remains incomplete, with a notable proportion of post-transplant events—particularly non-relapse mortality—left unexplained, he acknowledged. This limitation may reflect biologic heterogeneity and the absence of molecular or treatment-specific variables in the dataset. Future iterations of the model may benefit from the integration of genomic biomarkers, patient-reported outcomes, and dynamic clinical variables, Mosquera Orgueira suggested.

Although exploratory analyses examined the potential use of the model in guiding transplant-related decisions, such as conditioning intensity or graft-vs-host disease prophylaxis, no actionable clinical patterns were identified, he stated. As such, the model should not be used to determine specific therapeutic interventions, he emphasized.

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Dr Mosquera Orgueira on the Development of a Machine Learning–Based Prognostic Model for Risk Stratification in Myelofibrosis

Dr Orgueira on Refining the Use of Machine Learning to Predict Survival Outcomes After Transplant in Myelofibrosis

"It is still surprising that a large part of [patient] mortality is still not explained by these machine learning models, even with such big cohorts and multinational data. This means that we need to explore other areas of improvement in the future by taking probably other layers of complexity into the model, like imaging or molecular biology."

Adrián Mosquera Orgueira, MD, PhD, a specialist in Hematology and Hemotherapy and lead researcher of the Computational and Genomic Hematology Group at the Health Research Institute of Santiago de Compostela, discussed the limitations of machine learning–based prognostic models for overall survival into clinical practice for patients with myelofibrosis undergoing allogeneic hematopoietic stem cell transplant (allo-HSCT). He also talked through avenues for future improvement of one such model generated by the EBMT.

The model, which used a random survival forest (RSF) approach, demonstrated modest improvements in predictive accuracy over traditional statistical models,such as Cox regression, Orgueira began. One key strength of the RSF model was its ability to avoid overfitting through cross-validation, allowing for more robust generalizability across diverse patient cohorts, he added. The study drew on data from a large multinational population of patients with myelofibrosis who received allo-HSCT, Orgueira noted.

Despite these advantages, a substantial proportion of post-transplant mortality remains unexplained by the model, likely reflecting unmeasured variables or biological complexities not captured by existing inputs, Orgueira acknowledged. To improve future predictive accuracy, integration of additional data types—including imaging biomarkers, molecular profiling, and dynamic clinical parameters—should be considered, he suggested.

The study also explored whether the model could inform decision-making regarding transplant conditioning regimens or graft-vs-host disease (GVHD) prophylaxis but found no consistent patterns to guide clinical strategy, Orgueira stated. He emphasized that although the tool may help identify patients at highest risk of poor post-transplant outcomes, it should not be used to determine specific therapeutic interventions, such as conditioning intensity or GVHD management.

Rather, the model’s utility lies in facilitating early discussions among hematologists and patients about risks and goals of care before transplantation, Orgueira explained. The model is best applied as a risk stratification tool rather than a decision-making algorithm, he concluded.

Adrián Mosquera Orgueira, MD, PhD, discusses research to improve machine learning models for predicting post-transplant survival outcomes in myelofibrosis.

Dr Orgueira on the Use of Machine Learning to Predict Survival Outcomes After Allo-HSCT in Myelofibrosis

"We used the advantage of a quantitative score like this machine learning tool to optimally find a threshold that can actually identify high-risk patients. We identified the optimal threshold [as the] 25% upper percentile of risk…that more than doubles the [number] of high-risk patients [identified using] other tools, and it is better aligned with what we see in the clinics."

Adrián Mosquera Orgueira, MD, PhD, a specialist in Hematology and Hemotherapy and lead researcher of the Computational and Genomic Hematology Group at the Health Research Institute of Santiago de Compostela, discussed results from a study investigating the accuracy of a prognostic model for overall survival (OS) that uses machine learning techniques to identify high-risk patients with myelofibrosis with poor outcomes following allogeneic hematopoetic stem cell transplant (allo-HSCT).

The data showed that a machine learning–based approach for predicting OS outcomes after allo-HSCT using random survival forest (RSF) demonstrated notable advantages over traditional methods, Orgueira reported. Specifically, the RSF model achieved slightly superior predictive performance compared with other tools, including Cox regression, he clarified. One key advantage of RSF was its generalizability, attributable to the use of cross-validation during training, which mitigates the risk of overfitting—a common limitation in machine learning models, he noted.

Although the improvement in accuracy and concordance index with the RSF model over conventional strategies was modest, the RSF model outperformed the previously established CIBMTR risk score, Orgueira detailed. The major differentiator, however, was the integration of clinical expertise into the interpretation and application of the machine learning outputs, he said. This enabled the identification of a clinically actionable threshold to classify high-risk patients—those for whom allogeneic stem cell transplantation may carry a significantly higher risk of poor outcomes, he explained.

Whereas the CIBMTR score and the Cox-derived score identified 8.2% and 10.1% of patients as high risk, respectively, the RSF model—through optimal threshold selection—classified 25% of patients as high risk, Orgueira continued. This revised threshold more accurately reflects the proportion of high-risk cases encountered in clinical practice and thus offers a more practical tool for patient stratification, he expanded. By expanding the identification of high-risk patients, this approach could inform transplant eligibility decisions and the need for intensified supportive care or alternative therapeutic strategies in this vulnerable subgroup, Orgueira concluded.

Adrián Mosquera Orgueira, MD, PhD, discusses whether machine learning could accurately predict OS and NRM outcomes after allo-HSCT in myelofibrosis.

Adrián Mosquera Orgueira, MD, PhD,

Articles

Dr Mosquera Orgueira on the Development of a Machine Learning–Based Prognostic Model for Risk Stratification in Myelofibrosis

Dr Orgueira on Refining the Use of Machine Learning to Predict Survival Outcomes After Transplant in Myelofibrosis

Dr Orgueira on the Use of Machine Learning to Predict Survival Outcomes After Allo-HSCT in Myelofibrosis