Predicting progression events in multiple myeloma from routine blood work
Abstract 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 outper...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01636-9 |
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| _version_ | 1850231101674487808 |
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| author | Maximilian Ferle Nora Grieb Markus Kreuz Jonas Ader Hartmut Goldschmidt Elias K. Mai Uta Bertsch Uwe Platzbecker Thomas Neumuth Kristin Reiche Alexander Oeser Maximilian Merz |
| author_facet | Maximilian Ferle Nora Grieb Markus Kreuz Jonas Ader Hartmut Goldschmidt Elias K. Mai Uta Bertsch Uwe Platzbecker Thomas Neumuth Kristin Reiche Alexander Oeser Maximilian Merz |
| author_sort | Maximilian Ferle |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-b00a2c8dad6e4d128d7a8c95c270d8b4 |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-b00a2c8dad6e4d128d7a8c95c270d8b42025-08-20T02:03:39ZengNature Portfolionpj Digital Medicine2398-63522025-04-018111510.1038/s41746-025-01636-9Predicting progression events in multiple myeloma from routine blood workMaximilian Ferle0Nora Grieb1Markus Kreuz2Jonas Ader3Hartmut Goldschmidt4Elias K. Mai5Uta Bertsch6Uwe Platzbecker7Thomas Neumuth8Kristin Reiche9Alexander Oeser10Maximilian Merz11Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität LeipzigInnovation Center Computer Assisted Surgery (ICCAS), University of LeipzigDepartment of Medical Bioinformatics, Fraunhofer Institute for Cell Therapy and ImmunologyCenter for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität LeipzigDepartment of Internal Medicine V, University Hospital HeidelbergDepartment of Internal Medicine V, University Hospital HeidelbergDepartment of Internal Medicine V, University Hospital HeidelbergDepartment of Hematology, Hemostaseology, Cellular Therapy and Infectiology, University Hospital of LeipzigCenter for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität LeipzigCenter for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Universität LeipzigInnovation Center Computer Assisted Surgery (ICCAS), University of LeipzigDepartment of Hematology, Hemostaseology, Cellular Therapy and Infectiology, University Hospital of LeipzigAbstract 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.https://doi.org/10.1038/s41746-025-01636-9 |
| spellingShingle | Maximilian Ferle Nora Grieb Markus Kreuz Jonas Ader Hartmut Goldschmidt Elias K. Mai Uta Bertsch Uwe Platzbecker Thomas Neumuth Kristin Reiche Alexander Oeser Maximilian Merz Predicting progression events in multiple myeloma from routine blood work npj Digital Medicine |
| title | Predicting progression events in multiple myeloma from routine blood work |
| title_full | Predicting progression events in multiple myeloma from routine blood work |
| title_fullStr | Predicting progression events in multiple myeloma from routine blood work |
| title_full_unstemmed | Predicting progression events in multiple myeloma from routine blood work |
| title_short | Predicting progression events in multiple myeloma from routine blood work |
| title_sort | predicting progression events in multiple myeloma from routine blood work |
| url | https://doi.org/10.1038/s41746-025-01636-9 |
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