ArticleStatistical methods in medical research2019
Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A stochastic modelling framework for cancer patient trajectories: combining tumour growth, metastasis, and survival.Journal of mathematical biology · 2025Article
- Evaluation of optimal strategies for breast cancer screening in Ghana: A simulation study based on a continuous tumor growth model.PloS one · 2025Article
- Optimizing breast cancer screening strategies for women with different BMI levels in Ghana: A simulation-based study on BMI-dependent tumor growth model.PLOS global public health · 2025Article
- Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study.Cancers · 2023Article
- A natural history and copula-based joint model for regional and distant breast cancer metastasis.Statistical methods in medical research · 2022Article
- Estimating latent, dynamic processes of breast cancer tumour growth and distant metastatic spread from mammography screening data.Statistical methods in medical research · 2022Observational
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5 authors.
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Abstract
Continuous growth models show great potential for analysing cancer screening data. We recently described such a model for studying breast cancer tumour growth based on modelling tumour size at diagnosis, as a function of screening history, detection mode, and relevant patient characteristics. In this article, we describe how the approach can be extended to jointly model tumour size and number of lymph node metastases at diagnosis. We propose a new class of lymph node spread models which are biologically motivated and describe how they can be extended to incorporate random effects to allow for heterogeneity in underlying rates of spread. Our final model provides a dramatically better fit to empirical data on 1860 incident breast cancer cases than models in current use. We validate our lymph node spread model on an independent data set consisting of 3961 women diagnosed with invasive breast cancer.
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