We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
LGC Clinical Diagnostics

Download Mobile App




Advanced Predictive Algorithms Identify Patients Having Undiagnosed Cancer

By LabMedica International staff writers
Posted on 07 May 2025

Two newly developed advanced predictive algorithms leverage a person’s health conditions and basic blood test results to accurately predict the likelihood of having an undiagnosed cancer, including challenging-to-diagnose liver and oral cancers. More...

These innovative models have the potential to transform cancer detection in primary care, making it easier for patients to receive treatment at much earlier stages.

Currently, the UK's NHS uses prediction tools like the QCancer scores, which integrate various patient data to identify individuals at high risk for undiagnosed cancer, allowing general practitioners and specialists to refer them for further testing. Researchers from Queen Mary University of London (London, UK) and the University of Oxford (Oxford, UK) utilized anonymized electronic health records from over 7.4 million adults in England to develop two new algorithms. These models are more sensitive than existing tools and could lead to improved clinical decision-making and earlier cancer detection. Significantly, the new algorithms incorporate not only patient details like age, family history, medical diagnoses, symptoms, and general health, but also include the results of seven routine blood tests. These blood tests, which measure full blood count and liver function, serve as biomarkers to enhance early cancer diagnosis.

When compared with the current QCancer models, the new algorithms identified four additional medical conditions associated with an elevated risk of 15 different types of cancer, including those affecting the liver, kidneys, and pancreas. The new models also discovered two additional links between family history and lung or blood cancer, along with seven new symptoms—such as itching, bruising, back pain, hoarseness, flatulence, abdominal mass, and dark urine—that were associated with various types of cancer. The findings, published in Nature Communications, show that these new algorithms significantly improve diagnostic capabilities and are currently the only models applicable in primary care settings to assess the likelihood of undiagnosed liver cancer.

“These algorithms are designed to be embedded into clinical systems and used during routine GP consultations,” said Professor Julia Hippisley-Cox, Professor of Clinical Epidemiology and Predictive Medicine at Queen Mary University of London, and lead author of the study. “They offer a substantial improvement over current models, with higher accuracy in identifying cancers — especially at early, more treatable stages. They use existing blood test results which are already in the patients’ records making this an affordable and efficient approach to help the NHS meet its targets to improve its record on diagnosing cancer early by 2028.”


Gold Member
Antipsychotic TDM Assays
Saladax Antipsychotic Assays
Verification Panels for Assay Development & QC
Seroconversion Panels
New
TRAb Immunoassay
Chorus TRAb
New
Ultrasonic Cleaner
UC 300 Series
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to LabMedica.com and get access to news and events that shape the world of Clinical Laboratory Medicine.
  • Free digital version edition of LabMedica International sent by email on regular basis
  • Free print version of LabMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of LabMedica International in digital format
  • Free LabMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Clinical Chemistry

view channel
Image: The GlycoLocate platform uses multi-omics and advanced computational biology algorithms to diagnose early-stage cancers (Photo courtesy of AOA Dx)

AI-Powered Blood Test Accurately Detects Ovarian Cancer

Ovarian cancer ranks as the fifth leading cause of cancer-related deaths in women, largely due to late-stage diagnoses. Although over 90% of women exhibit symptoms in Stage I, only 20% are diagnosed in... Read more

Immunology

view channel
Image: The cancer stem cell test can accurately choose more effective treatments (Photo courtesy of University of Cincinnati)

Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer

Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more

Pathology

view channel
Image: AI-analyzed images from the FDM microscope show platelet clumps in motion (Photo courtesy of Hirose et al CC-BY-ND)

AI Microscope Spots Deadly Blood Clots Before They Strike

Platelets are small blood cells that act as emergency responders in the body, rushing to areas of injury to help stop bleeding by forming clots. However, sometimes platelets can overreact, leading to complications.... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.