## Thursday, June 13, 2013

### The time-evolution of DCIS size distributions with applications to breast cancer growth and progression

This paper looks at the growth dynamics of ductal carcinoma in situ (DCIS) in the breast, but instead of focusing on the dynamics of a single tumour the authors derive an equation for the size distribution of DCIS across an entire population. The equation for the size distribution has a stationary solution, and by comparing the analytical expression with data from mammographic screening the parameters of the growth model were estimated.

# The time-evolution of DCIS size distributions with applications to breast cancer growth and progression

Ductal carcinoma {\em in situ} (DCIS) lesions are non-invasive tumours of the breast which are thought to precede most invasive breast cancers (IBC). As individual DCIS lesions are initiated, grow and invade (i.e. become IBC) the size distribution of the DCIS lesions present in a given human population will evolve. We derive a differential equation governing this evolution and show, for given assumptions about growth and invasion, that there is a unique distribution which does not vary with time. Further, we show that any initial distribution converges to this stationary distribution exponentially quickly. It is therefore reasonable to assume that the stationary distribution governs the size of DCIS lesions in human populations which are relatively stable with respect to the determinants of breast cancer. Based on this assumption and the size data of 110 DCIS lesions detected in a mammographic screening program between 1993 and 2000, we produce maximum likelihood estimates for certain growth and invasion parameters. Assuming that DCIS size is proportional to a positive power $p$ of the time since tumour initiation, we estimate $p$ to be 0.50 with a 95% confidence interval of $(0.35, 0.71)$. Therefore we estimate that DCIS lesions follow a square-root growth law and hence that they grow rapidly when small and relatively slowly when large. Our approach and results should be useful for other mathematical studies of cancer, especially those investigating biological mechanisms of invasion.