Machine learning could aid an earlier pancreatic cancer diagnosis and increase patient survival numbers, new research has found.
A study, led by the London School of Hygiene & Tropical Medicine (LSHTM), found that individuals at higher risk of developing pancreatic cancer could be identified earlier using machine learning (ML), which is a form of artificial intelligence (AI).
The results of the study, funded by Pancreatic Cancer Research Fund (PCRF), have been published in PLOS ONE.
Researchers used UK electronic health records for over 1,000 patients, aged between 15 to 99 years old, who were diagnosed with pancreatic cancer between January 2005 and June 2009. They examined numerous symptoms and health statuses recorded by a GP among patients up to two years before the cancer diagnosis. They then developed an algorithm which ‘learnt’ how to distinguish patients who went on to develop pancreatic cancer from those who did not.
The algorithm was then used to identify those at high risk of developing pancreatic cancer just from GP records.
Using this technique, 41% of patients under the age of 60 were identified as high risk, up to 20 months prior to diagnosis. Over 72% of people who went on to be diagnosed would have been successfully identified as high risk (sensitivity) whilst 59% of people who did not develop cancer were correctly identified as low risk (specificity). Results were similar for patients over 60, with 43% identified at 17 months, displaying 65% sensitivity and 57% specificity.
Earlier diagnosis of pancreatic cancer tumours
The team estimates that combining their algorithm with simple blood and urine tests which could potentially detect pancreatic cancer, currently under investigation, could result in 30 older and 400 younger patients per cancer being identified as ‘potential patients’. This could lead to the earlier diagnosis of around 60% of all pancreatic cancer tumours.
The authors acknowledge that further work is required to confirm, refine, and evaluate the potential use of these findings in practice.
Dr Ananya Malhotra, the co-lead author from the London School of Hygiene & Tropical Medicine, said: “Each year, 460,000 people worldwide are diagnosed with pancreatic cancer, and only around 5% of those diagnosed survive for five years or more. This low survival is because patients are usually diagnosed very late. Recent progress has been made in identifying biomarkers in the blood and urine, but these tests cannot be used for population screening as they would be very expensive and potentially harmful due to the psychological distress of excess testing.
“Although preliminary, this study offers some hope for a new early diagnosis for pancreatic cancer which until now remains elusive.”
Previous research has highlighted conditions associated with pancreatic cancer diagnosis, such as jaundice, abdominal pain, and new-onset diabetes. Whilst these new results are consistent with these findings, this approach is a step-change from these previous studies because the team examined whether it is possible to predict future pancreatic cancer based on the presence of a combination of symptoms or abnormalities more than 12 months before diagnosis, ignoring late-stage symptoms.
The algorithm’s greatest potential is within a multiple-testing model where pancreatic cancer is one of several malignancies of interest. Another important finding was the relative importance of diabetes, over time-varying symptoms, in predicting later pancreatic cancer diagnosis, which is consistent with previous research.
Increased survival chances
Dr Laura Woods, study senior author from the London School of Hygiene & Tropical Medicine, said: “Using machine learning techniques, we developed a risk score for pancreatic cancer diagnosis in order to identify patients for whom biomarkers might detect the disease at an early and treatable stage. After further work, this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease.”
