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Predicting progression events in multiple myeloma from routine blood work

This study introduces a system for predicting disease progression events in multiple myeloma patients from the CoMMpass study (N = 1186). Utilizing a hybrid neural network architecture, our model predicts future blood work from historical lab results with high accuracy, significantly outperforming b...

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Main Authors: Ferle, Maximilian (Author) , Grieb, Nora (Author) , Kreuz, Markus (Author) , Ader, Jonas (Author) , Goldschmidt, Hartmut (Author) , Mai, Elias K. (Author) , Bertsch, Uta (Author) , Platzbecker, Uwe (Author) , Neumuth, Thomas (Author) , Reiche, Kristin (Author) , Oeser, Alexander (Author) , Merz, Maximilian (Author)
Format: Article (Journal)
Language:English
Published: 30 April 2025
In: npj digital medicine
Year: 2025, Volume: 8, Pages: 1-15
ISSN:2398-6352
DOI:10.1038/s41746-025-01636-9
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41746-025-01636-9
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41746-025-01636-9
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Author Notes:Maximilian Ferle, Nora Grieb, Markus Kreuz, Jonas Ader, Hartmut Goldschmidt, Elias K. Mai, Uta Bertsch, Uwe Platzbecker, Thomas Neumuth, Kristin Reiche, Alexander Oeser and Maximilian Merz
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Summary:This study introduces a system for predicting disease progression events in multiple myeloma patients from the CoMMpass study (N = 1186). Utilizing a hybrid neural network architecture, our model predicts future blood work from historical lab results with high accuracy, significantly outperforming baseline estimators for key disease parameters. Disease progression events are annotated in the forecasted data, predicting these events with significant reliability. We externally validated our model using the GMMG-MM5 study dataset (N = 504), and could reproduce the main results of our study. Our approach enables early detection and personalized monitoring of patients at risk of impeding progression. Designed modularly, our system enhances interpretability, facilitates integration of additional modules, and uses routine blood work measurements to ensure accessibility in clinical settings. With this, we contribute to the development of a scalable, cost-effective virtual human twin system for optimized healthcare resource utilization and improved outcomes in multiple myeloma patient care.
Item Description:Gesehen am 04.11.2025
Physical Description:Online Resource
ISSN:2398-6352
DOI:10.1038/s41746-025-01636-9

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