Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia - Research methodology

Aug 5, 2026

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Orphan diseases such as T-cell prolymphocytic leukemia face major obstacles because scarce patient data limits evidence-based care. This study applies machine learning to allogeneic hematopoietic cell transplantation (allo-HCT) to predict acute graft-versus-host disease (aGvHD) occurrence and its grades. Models are trained and evaluated using Center for International Blood and Marrow Transplant Research data, examining how variable counts affect performance. Balanced accuracy, F1, and ROC AUC are used; Linear Discriminant Analysis achieved the best test balanced accuracy of 0.58 but struggled in multiclass grade prediction.

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Chandra et al. BMC Medical Research BMC Medical Research Methodology (2024) 24:112 [https://doi.org/10.1186/s12874-024-02237-y](https://doi.org/10.1186/s12874-024-02237-y Methodology)[ Methodology](https://doi.org/10.1186/s12874-024-02237-y Methodology) RESEARCH Open Access Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation forT-cell prolymphocytic leukaemia Gunjan Chandra1*, Junfeng Wang2, Pekka Siirtola1 and Juha Röning1 Abstract Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strategies for orphan diseases, specifically focusing on allogeneic hematopoietic cell transplantation (allo-HCT) inT-cell prolymphocytic leukemia. The investigation evaluates how varying numbers of variables impact model performance, considering the rarity of the disease. Utilizing data from the Center for International Blood and Marrow Transplant Research, the study scrutinizes outcomes following allo-HCT forT-cell prolymphocytic leukemia. Diverse machine learning models were developed to forecast acute graft-versus-host disease (aGvHD) occurrence and its distinct grades post-allo-HCT. Assessment of model performance relied on balanced accuracy, F1 score, and ROC AUC metrics. The findings highlight the Linear Discriminant Analysis (LDA) classifier achieving the highest testing balanced accuracy of 0.58 in predicting aGvHD. However, challenges arose in its performance during multiclass classification tasks. While affirming the potential of machine learning in enhancing care for orphan diseases, the study underscores the impact of limited data and disease rarity on model performance. Keywords Orphan diseases, Machine learning, Allogeneic hematopoietic cell transplantation, T-cell prolymphocytic leukemia, Acute graft-versus-host disease, Data size, Model performance Introduction T-cell prolymphocytic leukemia (T-PLL), constituting about 2% of mature lymphocytic leukemias in adults, exemplifies an orphan disease. These rare conditions, marked by their scarcity and a restricted *Correspondence: Gunjan Chandra [gunjan.chandra@oulu.fi](gunjan.chandra@oulu.fi) 1 Biomimetics and Intelligent Systems Group, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland 2 Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands patient population [2], present substantial challenges in research, diagnosis, and treatment [11] . The scarcity of data and resources for orphan diseases often hinders the development of effective care strategies. Hematopoietic stem cell transplantation (HSCT) is a commonly used therapeutic approach for treating various hematological disorders, including leukemia and lymphoma [6] . However, HSCT comes with a considerable risk of complications, and graft-versus-host disease (GvHD) is one of the most significant challenges faced by HSCT patients [10] . GvHD occurs when the donor’s immune cells recognize the recipient’s tissues as foreign and initiate an immune response against them [10] . The severity of GvHD can range from mild © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence an

FAQs

Why are orphan diseases like T-cell prolymphocytic leukemia difficult for machine learning research?

They involve limited data availability and complex care pathways, which restrict model training and evaluation. This rarity affects how well predictive models can generalize.

What is the main goal of this study in allo-HCT patients?

To develop machine learning models that forecast acute graft-versus-host disease (aGvHD) occurrence and predict its distinct grades after allo-HCT for T-cell prolymphocytic leukemia.

Which metrics and model performed best according to the findings?

Model performance is assessed using balanced accuracy, F1 score, and ROC AUC. Linear Discriminant Analysis achieved the highest testing balanced accuracy of 0.58 for predicting aGvHD, though multiclass grade prediction remained challenging.

Aug 5, 2026

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