Web Tool for Estimating the Cancer Hazard Rates in Aging

Abstract

A computational approach for estimating the overall, population, and individual cancer hazard rates was developed. The population rates characterize a risk of getting cancer of a specific site/type, occurring within an age-specific group of individuals from a specified population during a distinct time period. The individual rates characterize an analogous risk but only for the individuals susceptible to cancer. The approach uses a novel regularization and anchoring technique to solve an identifiability problem that occurs while determining the age, period, and cohort (APC) effects. These effects are used to estimate the overall rate, and to estimate the population and individual cancer hazard rates. To estimate the APC effects, as well as the population and individual rates, a new web-based computing tool, called the CancerHazard@Age, was developed. The tool uses data on the past and current history of cancer incidences collected during a long time period from the surveillance databases. The utility of the tool was demonstrated using data on the female lung cancers diagnosed during 1975–2009 in nine geographic areas within the USA. The developed tool can be applied equally well to process data on other cancer sites. The data obtained by this tool can be used to develop novel carcinogenic models and strategies for cancer prevention and treatment, as well as to project future cancer burden.

Invariant Characteristics of Carcinogenesis

“…Analogously, for individuals susceptible to cancer, the age-specific cancer hazard rates are referred to as the individual hazard rates [ 13 , 14 ], which are conditional (conditional to susceptibility) rates. In the present work, to determine the APC effects as well as the population and individual hazard rates (individual cancer presentation rates), the web tool developed in [ 19 ], CancerHazard@Age , was used.…”

“…For each of the considered cancer sites (pancreatic, stomach, gallbladder, colon and rectum, liver and esophagus), six case matrices ( Cases ) of 16x7 sizes (three matrices with numbers of men and three matrices with numbers of women who have been diagnosed within each of the 16 age intervals during each of the seven specified time-period intervals with the given type of cancer in the Entire, Eastern and Western regions, correspondingly) were obtained. (A detailed description on how to prepare the case matrices is given in [ 19 ].) The population distributions of men and women and the distributions of the number of the GI cancer cases diagnosed during 1975–2009 within men and women, who have lived in Eastern and Western regions, are given in S1 Appendix .…”

“…Further, using the databases introduced in [ 22 , 23 ], six population matrices ( Populations ) of 16x7 sizes (three matrices with numbers of men and three matrices with numbers of women in ages within each of the 16 age intervals who have lived during each of the seven considered time-period intervals in the Entire, Eastern and Western regions, correspondingly) were created. The procedures for determining the population matrices are described in detail in [ 19 ].…”

“…The Cases and Populations matrices, saved as the tab-separated-value files, were used as input data for the CancerHazard@Age web tool [ 19 ] that is freely available at http://registry.unmc.edu/CHA/ . The CancerHazard@Age was used while performing several computational experiments, the outcomes of which are described in the Results and Discussion section.…”

“…the age interval of 70–74 was used as an anchor). Note, the index for the anchored birth-cohort is determined by the CancerHazard@Age as k = j − i + n [ 19 ]. For instance, in this work we used j = 4 (as well as j = 7), i = 11 and n = 16.…”