Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.
Gabriel IshedenLinda AbrahamssonTherese AnderssonKamila CzeneKeith HumphreysPublished in: Statistical methods in medical research (2019)
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.
Keyphrases
- lymph node
- neoadjuvant chemotherapy
- sentinel lymph node
- electronic health record
- cardiovascular disease
- squamous cell carcinoma
- breast cancer risk
- single cell
- radiation therapy
- machine learning
- polycystic ovary syndrome
- young adults
- case report
- deep learning
- artificial intelligence
- adipose tissue
- label free
- cervical cancer screening