Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse

UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.grupoInvGrupo de Métodos Numéricos en Enxeñaría (GMNI)es_ES
UDC.issue11es_ES
UDC.journalTitleiSciencees_ES
UDC.startPage105430es_ES
UDC.volume25es_ES
dc.contributor.authorLorenzo, Guillermo
dc.contributor.authorDi Muzio, Nadia Gisella
dc.contributor.authorDeantoni, Chiara Lucrezia
dc.contributor.authorCozzarini, Cesare
dc.contributor.authorFodor, Andrei
dc.contributor.authorBriganti, Alberto
dc.contributor.authorMontorsi, Francesco
dc.contributor.authorPérez-García, Víctor M.
dc.contributor.authorGómez, Héctor
dc.contributor.authorReali, Alessandro
dc.date.accessioned2024-10-14T17:50:56Z
dc.date.available2024-10-14T17:50:56Z
dc.date.issued2022
dc.description.abstract[Abstract:] The detection of prostate cancer recurrence after external beam radiotherapy relies on the measurement of a sustained rise of serum prostate-specific antigen (PSA). However, this biochemical relapse may take years to occur, thereby delaying the delivery of a secondary treatment to patients with recurring tumors. To address this issue, we propose to use patient-specific forecasts of PSA dynamics to predict biochemical relapse earlier. Our forecasts are based on a mechanistic model of prostate cancer response to external beam radiotherapy, which is fit to patient-specific PSA data collected during standard posttreatment monitoring. Our results show a remarkable performance of our model in recapitulating the observed changes in PSA and yielding short-term predictions over approximately 1 year (cohort median root mean squared error of 0.10–0.47 ng/mL and 0.13 to 1.39 ng/mL, respectively). Additionally, we identify 3 model-based biomarkers that enable accurate identification of biochemical relapse (area under the receiver operating characteristic curve > 0.80) significantly earlier than standard practice (p < 0.01).es_ES
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 838786. A.R. was partially supported by the MIUR-PRIN project XFAST-SIMS (no. 20173C478N). H.G. was partially funded by the Purdue Center for Cancer Research through a Concept Grant. V.M.P.-G. is partially supported by the Spanish Ministerio de Ciencia e Innovación (grant PID2019-110895RB-100, https://doi.org/10.13039/501100011033).es_ES
dc.description.sponsorshipItalia. Ministero dell'Università e della Ricerca; 20173C478Nes_ES
dc.identifier.citationLorenzo, G., di Muzio, N., Deantoni, C. L., Cozzarini, C., Fodor, A., Briganti, A., Montorsi, F., Pérez-García, V. M., Gomez, H., & Reali, A. (2022). Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse. iScience, 25(11). https://doi.org/10.1016/J.ISCI.2022.105430es_ES
dc.identifier.doi10.1016/j.isci.2022.105430
dc.identifier.urihttp://hdl.handle.net/2183/39604
dc.language.isoenges_ES
dc.publisherCellPresses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/838786es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110895RB-100/ES/es_ES
dc.relation.urihttps://doi.org/10.1016/j.isci.2022.105430es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectOncologyes_ES
dc.subjectBiological scienceses_ES
dc.subjectSystems biologyes_ES
dc.subjectCanceres_ES
dc.titlePatient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapsees_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication9f720523-a782-4a42-995e-32753b82a0b5
relation.isAuthorOfPublication0976003a-599e-4b50-b5d0-f308a00ddb56
relation.isAuthorOfPublication.latestForDiscovery9f720523-a782-4a42-995e-32753b82a0b5

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Lorenzo_G_2022_patient-specific_iS-25-105430.pdf
Size:
3.37 MB
Format:
Adobe Portable Document Format
Description: