Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests
Abstract Existing prostate cancer monitoring methods, reliant on prostate-specific antigen (PSA) measurements in blood tests often fail to detect tumor growth. We develop a computational framework to reconstruct tumor growth from the PSA integrating physics-based modeling and machine learning in dig...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01890-x |
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| author | Daniel Camacho-Gomez Carlos Borau Jose Manuel Garcia-Aznar Maria Jose Gomez-Benito Mark Girolami Maria Angeles Perez |
| author_facet | Daniel Camacho-Gomez Carlos Borau Jose Manuel Garcia-Aznar Maria Jose Gomez-Benito Mark Girolami Maria Angeles Perez |
| author_sort | Daniel Camacho-Gomez |
| collection | DOAJ |
| description | Abstract Existing prostate cancer monitoring methods, reliant on prostate-specific antigen (PSA) measurements in blood tests often fail to detect tumor growth. We develop a computational framework to reconstruct tumor growth from the PSA integrating physics-based modeling and machine learning in digital twins. The physics-based model considers PSA secretion and flux from tissue to blood, depending on local vascularity. This model is enhanced by deep learning, which regulates tumor growth dynamics through the patient’s PSA blood tests and 3D spatial interactions of physiological variables of the digital twin. We showcase our framework by reconstructing tumor growth in real patients over 2.5 years from diagnosis, with tumor volume relative errors ranging from 0.8% to 12.28%. Additionally, our results reveal scenarios of tumor growth despite no significant rise in PSA levels. Therefore, our framework serves as a promising tool for prostate cancer monitoring, supporting the advancement of personalized monitoring protocols. |
| format | Article |
| id | doaj-art-2cedeb56ee4b4eecbda4926ee8226768 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-2cedeb56ee4b4eecbda4926ee82267682025-08-20T04:03:11ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111010.1038/s41746-025-01890-xPhysics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA testsDaniel Camacho-Gomez0Carlos Borau1Jose Manuel Garcia-Aznar2Maria Jose Gomez-Benito3Mark Girolami4Maria Angeles Perez5Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering. Research (I3A), University of ZaragozaDepartment of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering. Research (I3A), University of ZaragozaDepartment of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering. Research (I3A), University of ZaragozaDepartment of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering. Research (I3A), University of ZaragozaDepartment of Engineering, University of CambridgeDepartment of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering. Research (I3A), University of ZaragozaAbstract Existing prostate cancer monitoring methods, reliant on prostate-specific antigen (PSA) measurements in blood tests often fail to detect tumor growth. We develop a computational framework to reconstruct tumor growth from the PSA integrating physics-based modeling and machine learning in digital twins. The physics-based model considers PSA secretion and flux from tissue to blood, depending on local vascularity. This model is enhanced by deep learning, which regulates tumor growth dynamics through the patient’s PSA blood tests and 3D spatial interactions of physiological variables of the digital twin. We showcase our framework by reconstructing tumor growth in real patients over 2.5 years from diagnosis, with tumor volume relative errors ranging from 0.8% to 12.28%. Additionally, our results reveal scenarios of tumor growth despite no significant rise in PSA levels. Therefore, our framework serves as a promising tool for prostate cancer monitoring, supporting the advancement of personalized monitoring protocols.https://doi.org/10.1038/s41746-025-01890-x |
| spellingShingle | Daniel Camacho-Gomez Carlos Borau Jose Manuel Garcia-Aznar Maria Jose Gomez-Benito Mark Girolami Maria Angeles Perez Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests npj Digital Medicine |
| title | Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests |
| title_full | Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests |
| title_fullStr | Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests |
| title_full_unstemmed | Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests |
| title_short | Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests |
| title_sort | physics informed machine learning digital twin for reconstructing prostate cancer tumor growth via psa tests |
| url | https://doi.org/10.1038/s41746-025-01890-x |
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