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Rubenstein, J. H.

Publications and source records attributed to Rubenstein, J. H..

2 recordsLinked to original sources

Barrett's esophagus is the precursor of all esophageal adenocarcinomas

ObjectiveBarretts esophagus (BE) is a known precursor to esophageal adenocarcinoma (EAC) but current clinical data have not been consolidated to address whether BE is the origin of all incident EAC, which would reinforce evidence for BE screening efforts. We aimed to answer whether all expected prevalent BE, diagnosed and undiagnosed, could account for all incident EACs in the US cancer registry data. DesignWe used a multi-scale computational model of EAC that includes the evolutionary process from normal esophagus through BE in individuals from the US population. The model was previously calibrated to fit SEER cancer incidence curves. Here we also utilized age- and sex-specific US census data for numbers at-risk. The primary outcome for model validation was the expected number of EAC cases for a given calendar year. Secondary outcomes included the comparisons of resulting model-predicted prevalence of BE and BE-to-EAC progression to the observed prevalence and progression rates. ResultsThe model estimated the total number of EAC cases in 2010 was 9,970 (95% CI 9,140 - 11,980), which recapitulates all EAC cases from population data. The model simultaneously predicted 8-9% BE prevalence in high-risk males age 45-55, and 0.1-0.2% non-dysplastic BE-to-EAC annual progression in males, consistent with clinical studies. ConclusionThere are no additional EAC cases that plausibly arise in the US population outside the BE pathway. Effective screening of high-risk patients could capture the majority of population destined for EAC progression and decrease mortality through early detection and curative removal of small (pre)cancers during surveillance. Summary BoxWhat is already known about this subject? O_LIBarretts esophagus (BE) patients have a 40 to 50-fold higher risk of developing esophageal adenocarcinoma (EAC) than the general population yet many remain undiagnosed. C_LIO_LIIdentified BE patients receiving surveillance can have early cancers discovered endoscopically, which decreases the high overall EAC-associated mortality. C_LIO_LICurrently around 90% of patients who develop EAC were never part of a BE surveillance program, and those BE patients on surveillance have a low annual progression rate of 0.1 - 0.3% to develop EAC. C_LI What are the new findings?O_LIBy applying a model that incorporates the evolution from normal cells to BE to EAC in patients, we found that the numbers add up - the expected number of EAC cases in the US population are explained by the published rates of BE described above. C_LIO_LIWe cohesively examined the published estimates to determine that all EAC likely arises from both identified BE and occult, undiagnosed BE in the population. C_LI How might it impact on clinical practice in the foreseeable future?O_LIBased on current best estimates, our findings suggest there is no public health need to seek cases of a non-BE alternative pathway to EAC. C_LIO_LIIncreasing efforts for effective, sensitive screening and surveillance of the true BE population will decrease EAC mortality in the coming years. C_LI

cancer biology

Optimal timing for cancer screening and adaptive surveillance using mathematical modeling

Cancer screening and early detection efforts have been partially successful in reducing incidence and mortality but many improvements are needed. Although current medical practice is mostly informed by epidemiological studies, the decisions for guidelines are ultimately made ad hoc. We propose that quantitative optimization of protocols can potentially increase screening success and reduce overdiagnosis. Mathematical modeling of the stochastic process of cancer evolution can be used to derive and to optimize the timing of clinical screens so that the probability is maximal that a patient is screened within a certain "window of opportunity" for intervention when early cancer development may be observable. Alternative to a strictly empirical approach, or microsimulations of a multitude of possible scenarios, biologically-based mechanistic modeling can be used for predicting when best to screen and begin adaptive surveillance. We introduce a methodology for optimizing screening, assessing potential risks, and quantifying associated costs to healthcare using multiscale models. As a case study in Barretts esophagus (BE), we applied our methods for a model of esophageal adenocarcinoma (EAC) that was previously calibrated to US cancer registry data. We found optimal screening ages for patients with symptomatic gastroesophageal reflux disease to be older (58 for men, 64 for women) than what is currently recommended (age > 50 years). These ages are in a cost-effective range to start screening and were independently validated by data used in current guidelines. Our framework captures critical aspects of cancer evolution within BE patients for a more personalized screening design. SignificanceOur study demonstrates how mathematical modeling of cancer evolution can be used to optimize screening regimes. Surveillance regimes could also be improved if they were based on these models. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/927475v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@6f9324org.highwire.dtl.DTLVardef@1254188org.highwire.dtl.DTLVardef@f1777eorg.highwire.dtl.DTLVardef@dc0e66_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology