e18800 Background: As oncology treatment has evolved to become more individualized, prognostic rationale has also undergone important changes. In breast cancer, disease staging was historically based upon anatomic features of the primary tumor, in combination with involvement of adjacent/distant tissues. However, as the understanding of molecular/genomic involvement became more advanced, staging definitions were redefined to incorporate receptors, histologic grade, and genetic expression. In this analysis, we use autoregressive integrated moving average (ARIMA) forecasting to understand how AJCC updates to prognostic definitions have contributed to stage migration, and to comment on whether better detection, or definitional changes, may be responsible for the increasing incidence in early stage breast cancer. Methods: In this time series forecast, ARIMA models, per stage (early: stage I/II vs. late: stage III/IV) were constructed based on 2004-2016 historic breast cancer incidence rates, as reported by the NCDB. Multiple models were generated, using differing autoregressive parameters; the most predictive model was chosen using the lowest Bayesian Information Criteria (BIC), and mean absolute percentage error (MAPE) to ensure best fit. Similar methodology has already been published to predict prostate cancer incidence. The best fit models were applied to forecast annual incidence, in the NCDB, in 2017. These data were compared to the real-world data captured in 2017. Statistics were performed using modeling systems in SPSS, version 27. Results: n=1,661,971 cases were included for these models, and 12 years of pre-AJCC updated NCDB breast cancer data were used. Using ARIMA modeling, best fit, stationary averages were identified, with autoregressive and difference terms which contributed to the lowest BIC, and MAPE < 5%, for both models. The best fit models forecasted 2017 incidence, by stage, without AJCC updates to staging criteria, and this data is compared to actual 2017 incidence with current updated AJCC 8th staging criteria (Table). Conclusions: During 2017, the first year of AJCC staging updates, there was an observed decrease in late stage diagnoses, and increase in early stage diagnoses, when compared with incidence rates that were forecasted using the old, anatomic AJCC criteria. Therefore, part of the stage migration noted may be a product of staging semantics, using updated definitions. Confirming appropriate improvement in long-term outcomes, based on new staging would be helpful. It is also important for clinicians and public health officials to bear this in-mind when interpreting epidemiologic data, for allocating resources, as shifts in staging may be a product of guideline changes, and not necessarily screening efficacy or early detection only.[Table: see text]
Visualizing and testing the impact of place on late-stage breast cancer incidence: A non-parametric geostatistical approach
