162433 20251017144614.0 doi 10.1038/s41746-025-01890-x sideral 145008 ART-2025-145008 eng Camacho-Gomez, Daniel Universidad de Zaragoza Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests 2025 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/DGA/T50-23R info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101018587-ICoMICS info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709 info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Borau, Carlos Garcia-Aznar, Jose Manuel Universidad de Zaragoza (orcid)0000-0002-9864-7683 Gomez-Benito, Maria Jose Universidad de Zaragoza (orcid)0000-0002-1878-8997 Girolami, Mark Perez, Maria Angeles Universidad de Zaragoza (orcid)0000-0002-2901-4188 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 8, 1 (2025), 485 [10 pp.] npj digit. med. npj digital medicine 2398-6352 1552698 http://zaguan.unizar.es/record/162433/files/texto_completo.pdf Versión publicada 2946939 http://zaguan.unizar.es/record/162433/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:162433 articulos driver 2025-10-17-14:18:40 ARTICLE