Random effects models of lymph node metastases in breast cancer: quantifying the roles of covariates and screening using a continuous growth model

Gabriel Isheden, Kamila Czene and Keith Humphreys

Biometrics, 2022, vol. 78, issue 1, 376-387

Abstract: We recently described a joint model of breast cancer tumor size and number of affected lymph nodes, which conditions on screening history, mammographic density, and mode of detection, and can be used to infer growth rates, time to symptomatic detection, screening sensitivity, and rates of lymph node spread. The model of lymph node spread can be estimated in isolation from measurements of tumor volume and number of affected lymph nodes, giving inference identical to the joint model. Here, we extend our model to include covariate effects. We also derive theoretical results in order to study the role of screening on lymph node metastases at diagnosis. We analyze the association between hormone replacement therapy (HRT) and breast cancer lymph node spread, using data from a case‐control study designed specifically to study the effects of HRT on breast cancer. Using our method, we estimate that women using HRT at time of diagnosis have a 36% lower rate of lymph node spread than nonusers (95% confidence interval [CI] =(8%,58%)). This can be contrasted with the effect of HRT on the tumor growth rate, estimated here to be 15% slower in HRT users (95% CI = (−34%,+7%)). For screen‐detected cancers, we illustrate how lead time can relate to lymph node spread; and using symptomatic cancers, we illustrate the potential consequences of false negative screens in terms of lymph node spread.

Date: 2022
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