Estimating latent, dynamic processes of breast cancer tumour growth and distant metastatic spread from mammography screening data.

ABSTRACT: We propose a framework for jointly modelling tumour size at diagnosis and time to distant metastatic spread, from diagnosis, based on latent dynamic sub-models of growth of the primary tumour and of distant metastatic detection. The framework also includes a sub-model for screening sensitivity as a function of latent tumour size. Our approach connects post-diagnosis events to the natural history of cancer and, once refined, may prove useful for evaluating new interventions, such as personalised screening regimes. We evaluate our model-fitting procedure using Monte Carlo simulation, showing that the estimation algorithm can retrieve the correct model parameters, that key patterns in the data can be captured by the model even with misspecification of some structural assumptions, and that, st

SUBMITTER: Gasparini A 

PROVIDER: S-EPMC9099158 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature