Tumor size distribution can yield valuable information on tumor growth and tumor control
In this work it is shown that tumor volume distributions, can yield valuable information on two completely different topics of cancer research. From the hypothesis that the intrinsic distributions of breast cancer volumes follows an exponential distribution, first the probability density function of tumor growth time was deduced. The resulting distribution of lag times can be used in tumor induction models instead of a fixed lag time to deduce the probability of tumor induction as a function of patient age. In a second step, the distribution of cancer volumes was used to model the variation of the clonogenic cell number for tumor control probability calculations for radiotherapy cancer patients. The integration of the volume variation into a Poisson-TCP model resulted in a logistic function which fits population averaged survival data of radiotherapy patients. To our knowledge this is the first direct derivation of a logistic TCP model for cohorts of patients from a Poisson TCP-model for individuals.