Professor of Medicine University of Minnesota Rochester, MN
Piet de Groen, MD; University of Minnesota, Rochester, MN
Introduction: We discovered that the distribution of one or more colorectal polyps, a form of neoplasia, among cases follows a predictable pattern that can be modeled by omnipresent neoplasia equations (ONE). The equation CE(p)=Xp states that the chance that a random event occurs p times is the event chance X to the power p. A derivative from this equation is f(p)=Xp/(Ʃp=0→∞Xp) where f(p) is the fraction in the population at-risk for neoplasia for a specific value of p. The total at-risk fraction of the population is defined by Ʃp=0→∞f(p). Methods: We elected to investigate whether the ONE also applies to survival. To investigate this, we searched the literature for case series of pancreas cancer (PC) describing survival over time in numeric format. For survival analysis we chose death as the random event, and analyzed the number of deaths per 0.5 year. In order to investigate whether the ONE also models PC incidence we extracted PC average incidence by age for women from the Cancer Incidence in Five Continents databases from the World Health Organization (CI5). The congruency of observed and modeled data was analyzed by Pearson correlation coefficient (PCC); a PCC >0.9995 was rounded up to 1.0. Results: A report was identified on the Cancer Treatment Centers of America (CTCA) website with numeric data about cases with metastatic PC. The CTCA PC cohort, 1,555 cases, included only metastatic PC patients who had been initially diagnosed at CTCA and/or received at least part of their initial course of treatment at CTCA. The ONE modeled the data with near perfect accuracy; the PCC was 0.998 (Fig 1A). Based on results related to colorectal cancer, we estimated that 50% of the at-risk population actually develops PC. As colorectal cancer and PC are both adenocarcinoma, we applied all other CRC assumptions: PC incidence by age will fit a logistic pattern defining three population groups: all without PC risk, transition into at-risk for PC, and all in at-risk for PC groups. Second, mortality prevents that the population reaches the all at-risk stage for PC. Based on these assumptions the ONE modeled the data with near perfect accuracy; the PCC was 1.0 (Fig 1B). Discussion: The ONE allow modeling of expected metastatic PC survival and average PC incidence by age. The concept of the at-risk group is critical: being able to estimate the size of the at-risk group allows application of the ONE for the entire cohort or population under study. Individual risk is defined by duration in the at-risk group.
Disclosures: Piet de Groen indicated no relevant financial relationships.