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Machine learning-based prediction of G-CSF-induced hematopoietic stem cell mobilization outcomes in healthy volunteers

Bone Marrow Transplantation volume 60, pages 1316–1324 (2025)Cite this article

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Abstract

Haematopoietic stem cell transplantation (HSC-T) has been established as a fundamental therapeutic intervention for a wide range of hematological malignancies and disorders, with a proven record of efficacy spanning over five decades. Peripheral blood (PB) has become the haematopoietic stem cell (HSC) source of choice, surpassing bone marrow, owing to its cost-effectiveness, reduced invasiveness, higher cell yields, and shorter hospitalizations. Clinically, granulocyte-colony stimulating factor (G-CSF) administration is the standard procedure for inducing HSC mobilization. Nevertheless, a significant proportion of potential donors, ranging from 5% to 10%, exhibit suboptimal mobilization responses to G-CSF. To investigate this, we carried out a retrospective analysis of mobilization data from 1056 donors who underwent G-CSF-induced HSC mobilization over the 5-year period from 2018 to 2023. This comprehensive study elucidated the complex interplay between mobilization efficacy, as measured primarily by CD34+ cell yield, and a variety of influencing factors. Our data indicate that better mobilization outcomes are achieved in male donors than female donors. Additionally, we found a positive correlation between increased body weight and improved mobilization efficiency, implying a more favourable response to G-CSF in obese donors. Moreover, the implementation of a split-dose regimen for the mobilization agent significantly improved outcomes, highlighting the critical role of dosing strategies. Notably, younger donors exhibited better mobilization responses, underscoring age as a pivotal determinant of mobilization outcomes. Leveraging machine learning (ML) algorithms, we developed seven predictive models designed to forecast G-CSF-induced HSC mobilization outcomes on the basis of these variables.

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Fig. 1: Flow chart of donor selection for the prediction analysis.
Fig. 2: Scatter plots and bar charts of various baseline laboratory data from donors and their Day4 CD34+ cell counts.
Fig. 3: Feature selection and importance.
Fig. 4: Comparison of performance among different models.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

Code to reproduce models, analyses, and figures can be found at the following Github repository: https://github.com/CeShi232/ML-HSC.

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Author notes

  1. These authors contributed equally: Ce Shi, Xiangjun Zeng, Jimei Ge.

Authors and Affiliations

  1. Bone Marrow Transplantation Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China

    Ce Shi, Xiangjun Zeng, Jimei Ge, Yunfei Qiu, Yi Luo, Jimin Shi, Jian Yu, Xiaoyu Lai, Yamin Tan, Lizhen Liu, Huarui Fu, Yishan Ye, Luxin Yang & Yibo Wu

  2. Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, China

    Ce Shi, Xiangjun Zeng, Jimei Ge, Yunfei Qiu, Yi Luo, Jimin Shi, Jian Yu, Xiaoyu Lai, Yamin Tan, Lizhen Liu, Huarui Fu, Yishan Ye, Luxin Yang & Yibo Wu

  3. Institute of Hematology, Zhejiang University, Hangzhou, China

    Ce Shi, Xiangjun Zeng, Jimei Ge, Yunfei Qiu, Yi Luo, Jimin Shi, Jian Yu, Xiaoyu Lai, Yamin Tan, Lizhen Liu, Huarui Fu, Yishan Ye, Luxin Yang, Yibo Wu, He Huang & Yanmin Zhao

  4. Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China

    Ce Shi, Xiangjun Zeng, Jimei Ge, Yunfei Qiu, Yi Luo, Jimin Shi, Jian Yu, Xiaoyu Lai, Yamin Tan, Lizhen Liu, Huarui Fu, Yishan Ye, Luxin Yang, Yibo Wu, He Huang & Yanmin Zhao

  5. Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, China

    He Huang & Yanmin Zhao

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Contributions

H.H. and Y.Z. conceived the study; C.S., X.Z., and Jimei Ge curated and analyzed the data; Y.Z. determined the methodology and acquired funding; C.S. wrote the original draft; and X.Z., Y.Q., Y.L., J.S., J.Y., X.L., Y.T., H.F., Y.Y., L.Y., and Y.W. reviewed and edited the manuscript.

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Correspondence to He Huang or Yanmin Zhao.

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Shi, C., Zeng, X., Ge, J. et al. Machine learning-based prediction of G-CSF-induced hematopoietic stem cell mobilization outcomes in healthy volunteers. Bone Marrow Transplant 60, 1316–1324 (2025). https://doi.org/10.1038/s41409-025-02666-3

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