Uncovering the Natural History of Metastatic Cancer from Autopsy Data

 

The goal of the study is to develop mathematical and statistical methodology for estimation of important unobservable characteristics of the natural history of metastatic cancer (such as the rates of growth of the primary tumor and metastases and mean metastatic latency time) from the autopsy data. The latter consists of the largest cross-sectional areas of liver metastases obtained from sections of the liver by parallel planes at certain distance apart from each other. Estimation of the cancer natural history is based on a previously proposed comprehensive stochastic model of cancer progression accounting for primary tumor growth, shedding of metastases, their selection, latency and growth in a given secondary site. The model was applied to the aforementioned autopsy data for one breast cancer and one lung cancer patient. Identifiable parameters of the model were estimated by the method of maximum likelihood. The model with these parameters provided very good fit to the data. Results of model-based data analysis will be discussed from biomedical standpoint.

 

This is a joint work with Jason Rose, a Ph.D. student at the Mathematics Department of Idaho State University.