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

Biological sciences Cancer Oncology Systems biology

Journal

iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038

Informations de publication

Date de publication:
18 Nov 2022
Historique:
received: 12 03 2022
revised: 04 09 2022
accepted: 19 10 2022
entrez: 17 11 2022
pubmed: 18 11 2022
medline: 18 11 2022
Statut: epublish

Résumé

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).

Identifiants

pubmed: 36388979
doi: 10.1016/j.isci.2022.105430
pii: S2589-0042(22)01702-3
pmc: PMC9641236
doi:

Types de publication

Journal Article

Langues

eng

Pagination

105430

Informations de copyright

© 2022 The Author(s).

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Auteurs

Guillermo Lorenzo (G)

Department of Civil Engineering and Architecture, University of Pavia, via Ferrata 3, 27100 Pavia, Italy.
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th Street, Austin, TX 78712-1229, USA.

Nadia di Muzio (N)

Department of Radiation Oncology, IRCCS San Raffaele Hospital and Scientific Institute, via Olgettina 60, 20132 Milan, Italy.
Vita-Salute San Raffaele University, via Olgettina 58, 20132 Milan, Italy.

Chiara Lucrezia Deantoni (CL)

Department of Radiation Oncology, IRCCS San Raffaele Hospital and Scientific Institute, via Olgettina 60, 20132 Milan, Italy.

Cesare Cozzarini (C)

Department of Radiation Oncology, IRCCS San Raffaele Hospital and Scientific Institute, via Olgettina 60, 20132 Milan, Italy.

Andrei Fodor (A)

Department of Radiation Oncology, IRCCS San Raffaele Hospital and Scientific Institute, via Olgettina 60, 20132 Milan, Italy.

Alberto Briganti (A)

Vita-Salute San Raffaele University, via Olgettina 58, 20132 Milan, Italy.
Department of Urology, IRCCS San Raffaele Hospital and Scientific Institute, via Olgettina 60, 20132 Milan, Italy.

Francesco Montorsi (F)

Vita-Salute San Raffaele University, via Olgettina 58, 20132 Milan, Italy.
Department of Urology, IRCCS San Raffaele Hospital and Scientific Institute, via Olgettina 60, 20132 Milan, Italy.

Víctor M Pérez-García (VM)

Mathematical Oncology Laboratory, University of Castilla-La Mancha, Edificio Politécnico, Avenida Camilo José Cela 3, 13071 Ciudad Real, Spain.

Hector Gomez (H)

School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA.
Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907, USA.
Purdue Center for Cancer Research, Purdue University, 201 S. University Street, West Lafayette, IN 47907, USA.

Alessandro Reali (A)

Department of Civil Engineering and Architecture, University of Pavia, via Ferrata 3, 27100 Pavia, Italy.

Classifications MeSH