A quantitative framework to assess the potential of earlier cancer detection to improve cancer survival

BACKGROUND: Earlier cancer detection through advancements in screening technologies and increased screening access and adherence may improve cancer survival. We developed a quantitative approach using a matrix equation to characterize cancer stage shift resulting from cancer screening interventions and estimated resulting survival improvements. METHODS: The expected percent survival improvement was defined a priori as 20%, and the analysis sought to characterize the stage shift required to achieve this goal. The matrix equation was populated with incidence and cause-specific survival for 16 cancers by stage at diagnosis using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. Linear programming was used to solve for the matrix given a set of constraints formulated to steer the solution towards the least amount of downstaging required to achieve the objective and to partially compensate for length-time bias. RESULTS: Three common trends emerged across almost all cancer types: i) most of the survival improvement can be achieved by detecting disease prior to stage IV; ii) remaining survival improvements can be achieved by detecting just one stage earlier; and iii) stage I diagnosis is not necessary to achieve measurable survival improvement goals. Lung cancer required more aggressive earlier detection than others, an expected result as lung cancer has a very high incidence and very poor survival. CONCLUSIONS: This flexible mathematical framework may be helpful for public health officials to characterize how earlier cancer detection can impact cancer survival. IMPACT: Our results suggest that detecting cancer prior to distant metastases may significantly improve survival.