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NIH grant 1R01CA295896-01A1: Advancing the Use of the Kettles Esophageal and Cardia Adenocarcinoma Prediction Tool
Institute: {'code': 'CA', 'abbreviation': 'NCI', 'name': 'National Cancer Institute', 'admin_org_id': '12500008', 'admin_funding_url': ''}. PI: JOEL H RUBENSTEIN. Award: $543710.
Start: 2026-09-18T00:00:00.
Esophageal and gastric cardia adenocarcinomas (EAC/GCA) are clinically similar cancers that have
been rising in incidence while incidence for most other cancers has been decreasing. EAC/GCA rarely
present at an early stage, and overall 5-year survival is less than 20%. And yet, NIH funding for EAC/GCA
research is disproportionally low compared to its lethality. The individuals at highest risk include White
men with symptoms of gastroesophageal reflux disease who are at least 60 years of age, but they represent
less than 1/3 of all cases of EAC/GCA. Screening by esophagogastroduodenoscopy (EGD) for the precursor,
dysplastic Barrett’s esophagus (BE), is associated with reduced mortality, and has been endorsed by specialty
societies for higher risk individuals. But fewer than 20% of those with EAC/GCA had an EGD prior to their
cancer diagnosis. EAC/GCA are not common enough to warrant population-wide screening, so a personalized
approach to screening is necessary. Validated prediction tools exist but are not commonly used. A tool that can
be easily implemented is critical, such as by incorporation in the electronic health record (EHR). We used
machine learning to adapt the tools for use within the Veterans Health Administration (VHA) EHR among
11,395 cancer cases and 10 million controls. By imputing missing data and leveraging novel associations with
routine laboratory values and diagnostic codes, we developed and internally validated the Kettles Esophageal
and Cardia Adenocarcinoma predictioN tool (K-ECAN). Despite these advances, there are key unanswered
questions regarding K-ECAN, including the accuracy of K-ECAN in other healthcare systems, the optimal
threshold risk above which screening should be offered, and barriers and facilitators to implementing K-ECAN
across diverse care delivery settings.
Therefore, we aim to:
1. Improve K-ECAN and externally validate its accuracy for identifying patients with dysplastic BE and
predicting EAC/GCA.
2. Estimate the optimal cost-effective threshold K-ECAN score above which to offer screening.
3. Identify barriers and facilitators to implementing K-ECAN, and implementation strategies to support its
uptake and adoption across diverse care delivery settings.
EAC/GCA are screening-preventable cancers, but they are too uncommon to justify mass screening. Instead, a
personalized approach to screening is necessary. K-ECAN can be rapidly scaled up for use by providers to
select patients for screening. But key knowledge gaps need to be addressed first by this proposal. Completion
of this award will lead to improvements in, and adaptation and validation of K-ECAN for EHRs beyond
VHA and to planning trials of using K-ECAN to select patients for screening. Implementation of such an
intervention would bridge the gap between population-health screening and precision medicine.document fetched 2026-09-27 02:45