The Importance of Early Detection in Esophageal and Stomach Cancer Prevention
Introduction
In the United States and other Western countries, the incidence of esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) has been on the rise over the past five decades. These types of cancer are highly fatal, making early detection and prevention crucial. While there have been guidelines for screening high-risk patients, many healthcare providers are still unfamiliar with them. However, recent advancements in technology and artificial intelligence have made it possible to bridge the gap between awareness among providers and patients at increased risk.
The Role of Screening Tests
Joel Rubenstein, MD, MS, a research scientist at the Veterans Affairs Center for Clinical Management Research Lt. Col. Charles S. Kettles and professor of internal medicine at Michigan Medicine, emphasizes the significance of preventative measures in combating esophageal and stomach cancer. Screening tests play a vital role in identifying precancerous changes such as Barrett’s esophagus, which is often diagnosed in individuals with long-term gastroesophageal reflux disease (GERD). Early detection enables patients to take additional steps to prevent the development of cancer.
Introducing the K-ECAN Prediction Tool
One groundbreaking development in the field of cancer prevention is the Kettles Cardiac and Esophageal Adenocarcinoma Prediction Tool, also known as K-ECAN. Developed and tested by Dr. Rubenstein and his team, this tool utilizes artificial intelligence and available electronic health record (EHR) data to assess an individual’s risk of developing esophageal adenocarcinoma and gastric cardia adenocarcinoma. By analyzing patient demographics, weight, previous diagnoses, and routine laboratory results, K-ECAN provides a more accurate prediction of cancer risk than published guidelines or existing prediction tools. Moreover, K-ECAN can predict cancer at least three years before a formal diagnosis.
Advantages of K-ECAN
K-ECAN’s ability to accurately identify individuals at high risk of developing esophageal or stomach cancer is particularly valuable. While GERD symptoms, such as heartburn, have been recognized as a risk factor for these types of cancer, many patients with GERD symptoms never develop them, and vice versa. K-ECAN can help identify high-risk individuals, regardless of the presence or absence of GERD symptoms.
Benefits of K-ECAN
- K-ECAN harnesses the power of machine learning to analyze vast amounts of EHR data and provide personalized risk assessments.
- Early detection through K-ECAN can lead to increased prevention measures and decreased mortality rates.
- The tool’s integration into EHR systems can automate the identification of high-risk patients, ensuring timely intervention and monitoring.
The Collaborative Effort Behind the Research
Akbar Waljee, MD, M.Sc., professor in the Departments of Health Sciences and Internal Medicine and lead author of the study, acknowledges the collaborative nature of the research. The publication and development of K-ECAN were made possible through the combined efforts of the VA Health Services Research and Innovation Center, Michigan Medicine, the University of Michigan Department of Statistics, and other organizations. This collaboration showcases the power of team science, data analysis, and artificial intelligence in improving cancer prevention strategies.
Implementation and Future Prospects
The successful integration of K-ECAN into electronic health records could revolutionize esophageal and stomach cancer prevention. By alerting healthcare providers with automated notifications about patients at higher risk, K-ECAN significantly reduces the burden associated with these deadly cancers. The tool’s accuracy and ability to predict cancer years in advance can lead to early intervention, enhanced surveillance, and targeted preventive measures.
Expanding the Reach of K-ECAN
Rubenstein’s team foresees further work to validate K-ECAN for use outside the Veterans Affairs (VA) system. This expansion would enable healthcare providers across various settings to benefit from this powerful predictive tool. The widespread adoption of K-ECAN could potentially save countless lives and improve patient outcomes through timely intervention and prevention.
Conclusion
The increasing rates of esophageal and stomach cancer demand effective prevention strategies. Thanks to advancements in technology and machine learning, tools like K-ECAN provide a ray of hope in the fight against these highly fatal cancers. Early detection through screening tests and the use of predictive tools like K-ECAN can help identify high-risk individuals and facilitate the implementation of preventive measures. By bridging the gap between awareness among providers and patients at risk, K-ECAN has the potential to significantly decrease the burden of esophageal and stomach cancer worldwide.
Summary
In the United States and other Western countries, the rates of esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA), two highly fatal types of cancer, have dramatically increased over the past five decades. However, recent advancements in technology have introduced innovative tools like the Kettles Cardiac and Esophageal Adenocarcinoma Prediction Tool (K-ECAN). Developed using artificial intelligence and electronic health record data, K-ECAN accurately predicts an individual’s risk of developing these cancers at least three years before a formal diagnosis. By identifying high-risk patients and automating notifications to healthcare providers, K-ECAN offers a proactive approach to cancer prevention. The collaborative effort behind this research highlights the power of data analysis, machine learning, and team science in improving cancer prevention strategies. With the successful implementation of K-ECAN, the burden of esophageal and stomach cancer could be significantly reduced, leading to enhanced patient outcomes and potentially saving lives.
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In the United States and other Western countries, a form of esophageal and stomach cancer has increased dramatically in the past five decades. The rates of esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) are highly fatal.
However, Joel Rubenstein, MD, MS, a research scientist at the Veterans Affairs Center for Clinical Management Research Lt. Col. Charles S. Kettles and professor of internal medicine at Michigan Medicine, says preventative measures can be a saving grace.
“Screening tests can identify precancerous changes in patients, Barrett’s esophagus, which is sometimes diagnosed in people who have long-term gastroesophageal reflux disease, or GERD,” he said.
“When early detection occurs, patients can take additional steps to help prevent cancer.”
While current guidelines already consider screening in high-risk patients, Rubenstein notes that many providers are not yet familiar with this recommendation.
“Many people who develop these cancers have never been screened to begin with,” he said.
“But a new automated tool integrated into the electronic medical record has the potential to bridge the gap between awareness among providers and patients who are at increased risk of developing adenocarcinoma of the esophagus and adenocarcinoma of the gastric cardia.”
Rubenstein and a team of researchers used a type of artificial intelligence to examine data on EAC and GCA rates in more than 10 million American veterans.
Their findings were published in gastroenterology.
Rubenstein and his team developed and tested the Kettles Cardiac and Esophageal Adenocarcinoma Prediction Tool, called K-ECAN for short.
“K-ECAN uses basic information already available from the EHR, such as patient demographics, weight, previous diagnoses, and routine laboratory results, to determine an individual’s risk of developing esophageal adenocarcinoma and gastric cardia adenocarcinoma.” said Rubenstein.
“We developed an earlier tool, M-BERET, over a decade ago to identify patients with Barrett’s esophagus. However, that tool requires measuring patients’ hip and waist circumferences, which is not something that happens routinely. Also, providers should remember to use the appropriate website to calculate the risk of their patients when using this tool.”
To ease this burden, Rubenstein said, they “envisioned leveraging the vast amount of data that is already present in the EHR, as well as presenting their patients’ risk to their providers at opportune moments,” such as when an individual is due for an exam. colorectal. or refill a prescription acid-reducing medication.
According to Rubenstein, K-ECAN is more accurate than published guidelines or previously validated prediction tools and can “accurately predict cancer at least three years before a diagnosis.”
“GERD symptoms, such as heartburn, are an important risk factor for esophageal adenocarcinoma,” he said.
“But the majority of people with GERD symptoms will never develop esophageal adenocarcinoma or gastric cardia adenocarcinoma. Furthermore, about half of patients with this form of cancer have never experienced any prior GERD symptoms. This makes K-ECAN particularly useful because it can identify people who are at high risk, regardless of whether they have GERD symptoms or not.”
Akbar Waljee, MD, M.Sc., professor in the Departments of Health Sciences and Internal Medicine and lead author of the study, adds that this research would not be possible without a collaborative effort.
“This publication, which leveraged invaluable data from millions of US Veterans, was made possible through the dedicated efforts of numerous staff members at our VA Health Services Research and Innovation Center, as well as through partnerships collaboration between the VA Research Center for Clinical Management, Michigan Medicine, the University of Michigan Department of Statistics, and members of the UM Institute for Health Policy and Innovation and E-Health and Artificial Intelligence, or e-HAIL, exemplifying the power of team science, data and machine learning to improve cancer prevention.”
Incorporating this artificial intelligence tool into the EHR could alert providers with automated notification about which patients are at higher risk of developing esophageal adenocarcinoma and gastric cardia adenocarcinoma.
And Rubenstein says this can significantly decrease the burden of these cancers.
“Our dedicated team was able to use sophisticated machine learning tools to develop this unique tool, and we are very excited that this can potentially lead to increased detection and decreased preventable deaths. We look forward to further work to validate K-ECAN for use out of the VA.”
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