JUN 10, 2021 8:00 AM PDT

Algorithm Flags High Risk of Pancreatic Cancer

WRITTEN BY: Tara Fernandez

In 2021, over 60,000 individuals in the U.S. alone will be diagnosed with pancreatic cancer. Researchers are turning to machine learning (ML) to help identify those most at risk of developing the disease, such that they can access early interventions.

In the study, published in PLOS ONE, researchers describe how they analyzed health records from over 1,000 patients in the UK, up to two years before their pancreatic cancer diagnosis. They used this data to train an ML algorithm to distinguish patients at risk.

The ML system found 41 percent of the patients to be in the high-risk category, up to 20 months before they receive the diagnosis. The test had a 72 percent sensitivity, meaning the ability to successfully identify those who went on to develop pancreatic cancer. 59 percent of people who did not end up getting pancreatic cancer were categorized as low risk by the algorithm. The specificity and sensitivity of the system can be refined and improved with additional work, say the authors.

"Each year, 460,000 people worldwide are diagnosed with pancreatic cancer, and only around 5% of those diagnosed survive for five years or more,” explained co-lead author Ananya Malhotra. “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."

 



Sources: PLOS One, EurekAlert.

About the Author
  • Tara Fernandez has a PhD in Cell Biology and has spent over a decade uncovering the molecular basis of diseases ranging from skin cancer to obesity and diabetes. She currently works on developing and marketing disruptive new technologies in the biotechnology industry. Her areas of interest include innovation in molecular diagnostics, cell therapies, and immunology. She actively participates in various science communication and public engagement initiatives to promote STEM in the community.
You May Also Like
FEB 11, 2021
Clinical & Molecular DX
Seeing if Hormone Therapy Will Work for Breast Cancer Patients
FEB 11, 2021
Seeing if Hormone Therapy Will Work for Breast Cancer Patients
Only around half of women diagnosed with breast cancer will benefit from hormonal therapy, a cancer treatment that adds, ...
MAR 02, 2021
Clinical & Molecular DX
Test Before You Travel: Tiny Smartphone Device Checks for COVID in 60 Minutes
MAR 02, 2021
Test Before You Travel: Tiny Smartphone Device Checks for COVID in 60 Minutes
According to the CDC’s latest travel regulations, all air passengers must have a negative COVID test before boardi ...
APR 08, 2021
Clinical & Molecular DX
Marijuana Versus Tobacco: Which Is Worse for Your Lungs?
APR 08, 2021
Marijuana Versus Tobacco: Which Is Worse for Your Lungs?
Canadian researchers have observed that individuals who smoke marijuana are more at risk than tobacco cigarette smokers ...
APR 13, 2021
Clinical & Molecular DX
A Color-Changing "Invisible Tattoo" for Long-Term Health Monitoring
APR 13, 2021
A Color-Changing "Invisible Tattoo" for Long-Term Health Monitoring
German researchers have developed an innovative method for continuously tracking and monitoring biomarkers and drugs cir ...
APR 12, 2021
Microbiology
New Lyme Test Can ID The DIsease Early
APR 12, 2021
New Lyme Test Can ID The DIsease Early
Lyme disease is a disease that is caused by four main species of bacteria, including Borrelia burgdorferi, which ar ...
APR 29, 2021
Clinical & Molecular DX
Can We Diagnose Disease Based on How "Sticky" Cells Are?
APR 29, 2021
Can We Diagnose Disease Based on How "Sticky" Cells Are?
How “sticky” cells are, or their viscosity, holds a wealth of information about their health and functionali ...
Loading Comments...