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Adaptive therapy under parametric, structural, and measurement uncertainty — arXiv:2608.18387

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18387
License (as archived): CC0-1.0
Archived: 2026-10-18T13:56:55.192Z
SHA-256 of the archived text: 99110a22eb156080664245888e9b8ddeebe5071d57d184ec1bbe0d30e46e5e76
Stable URL of this page: https://metraipolis.com/documento/dc_38fc404ca644

How to cite

Adaptive therapy under parametric, structural, and measurement uncertainty — arXiv:2608.18387 — arXiv (en), https://arxiv.org/abs/2608.18387 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_38fc404ca644 (Metraipolis archive dc_38fc404ca644, sha256 99110a22eb156080664245888e9b8ddeebe5071d57d184ec1bbe0d30e46e5e76)

BibTeX

@misc{dc38fc404ca644,
  title = {Adaptive therapy under parametric, structural, and measurement uncertainty — arXiv:2608.18387},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18387},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_38fc404ca644; sha256 99110a22eb156080664245888e9b8ddeebe5071d57d184ec1bbe0d30e46e5e76},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18387. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Adaptive therapy has emerged as a promising treatment strategy that exploits within-tumour competition to delay disease progression. Implementation, however, typically relies on indirect measurements of tumour burden and must account for potentially substantial patient heterogeneity. In this work, we capture patient-to-patient variability, parameter uncertainty, and imperfect biomarker measurements with a mathematical and statistical model that we calibrate to clinical prostate cancer data using a Bayesian inference framework. We use the resulting virtual cohort to demonstrate that, within the simple but now well-established Lotka-Volterra-based model, adaptive therapy robustly improves time-to-progression for the subset of patients that are predicted to eventually progress by the model. To account for other risk factors associated with larger tumour volumes, we introduce a new metric based on the risk of metastasis that demonstrates how adaptive therapy may be disadvantageous when sustained tumour burden is also considered. Given the ubiquity of uncertainty in oncology, we then describe several future modelling directions that also capture uncertainty in the temporal evolution of the underlying tumour or biomarker dynamics. Finally, we demonstrate how model misspecification and non-identifiability can lead to unreliable predictions, especially if uncertainty is inadequately captured.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-20 in stat.ME).
Categorie arXiv: Tissues and Organs (q-bio.TO) ; Applications (stat.AP); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2608.18387 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:53:24.416Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).