Mechanistic modeling of metastatic relapse in early breast cancer to investigate the biological impact of prognostic biomarkers

Estimating the risk of metastatic relapse is a major challenge to decide adjuvant treatment options in early-stage breast cancer (eBC). To date, distant metastasis-free survival (DMFS) analysis mainly relies on classical, agnostic, statistical models (e.g., Cox regression). Instead, we propose here to derive mechanistic models of DMFS.

Keywords

NLME cancer metastases mechanistic modelling identifiability survival analysis

Domains

Cancer Statistics [math.ST] Applications [stat.AP]

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Mechanistic modeling of metastatic relapse - final version.pdf (608.78 Ko)

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Submitted on : Tuesday, January 9, 2024-9:38:17 AM

Last modification on : Thursday, July 18, 2024-4:25:25 PM

Dates and versions

hal-04008520 , version 1 (09-01-2024)

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Attribution - ShareAlike

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HAL Id : hal-04008520 , version 1
DOI : 10.1016/j.cmpb.2023.107401

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Célestin Bigarré, François Bertucci, Pascal Finetti, Gaëtan Macgrogan, Xavier Muracciole, et al.. Mechanistic modeling of metastatic relapse in early breast cancer to investigate the biological impact of prognostic biomarkers. Computer Methods and Programs in Biomedicine, 2023, 231, pp.107401. ⟨10.1016/j.cmpb.2023.107401⟩. ⟨hal-04008520⟩

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