A flexible quantitative framework to assess the potential contribution of early cancer detection to improved cancer survival.

person Menggang Yu University of Wisconsin, Madison, WI info_outline Menggang Yu, Christopher Tyson, Paul J. Limburg, Tomasz M. Beer

Authors person Menggang Yu University of Wisconsin, Madison, WI info_outline Menggang Yu, Christopher Tyson, Paul J. Limburg, Tomasz M. Beer Organizations University of Wisconsin, Madison, WI, Exact Sciences Corporation, Madison, WI Abstract Disclosures Research Funding Pharmaceutical/Biotech Company Exact Sciences Corporation Background: Public health goals often articulate aspirations to improve cancer survival by some pre-specified percentage. Such survival improvements may be achieved through a combination of medical interventions and lifestyle change. Early detection of cancer can contribute to cancer survival improvements. Impact of early detection programs is realized both through innovation driving advances in screening technologies and increased access and adherence with screening tests. We develop a quantitative approach to characterize the magnitude of early detection across multiple cancer types that corresponds to a specified percent improvement in survival. Methods: A matrix equation is developed relating survival and stage shift. The percent of survival improvement is defined a priori as 20% in this case but can be set to other percentages. We populate the matrix equation with incidence and cause-specific survival for 15 cancers by stage at diagnosis from the National Cancer Institute’s Surveillance, Epidemiology, and End Results database. We then use linear programming to solve the matrix for all specified cancers simultaneously given a set of constraints formulated to steer the solution towards the least stage shift required to achieve the objective and to partially compensate for length-time bias. Results: Based on our target survival improvement of 20%, three common trends emerged across 14 of the 15 cancer types: first, we see that the bulk of survival improvement can be achieved by detecting most disease prior to stage 4; second, that the remaining survival improvement can be achieved by detecting most cancers just one stage earlier; and third, that stage 1 diagnosis is generally not necessary to achieve reasonable survival improvement goals. The solution revealed lung cancer as the one type that required more aggressive earlier detection than others, an expected result given that lung cancer has both very high incidence and very poor survival. Conclusions: The mathematical framework we develop is very flexible and can be helpful for public health officials and innovators to characterize the contribution that earlier cancer detection can make towards improved cancer survival. Our results suggest that detecting cancer prior to the development of distant metastases, even after progression beyond stage 1, has potential for significant public health benefit. This approach may also be extended to assess the impact that emerging treatment landscapes, coupled with early cancer detection, may have on improved cancer survival.