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
ZeptoMetrix an Antylia scientific company

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
Serological Pipet Controller
PIPETBOY GENIUS
New
Gold Member
Thyroid-Stimulating Hormone Test
ULTRA-TSH
New
Gold Member
Thyroxin Test
T3 TOTAL
New
UHF RFID Tag & Inlay
AD-327 U9 ETSI Pure 95
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 POC device rapidly predicts neonatal respiratory disease at birth in the NICU (Photo courtesy of SIME Diagnostics)

AI-Powered Lung Maturity Test Identifies Newborns at Higher Risk of Respiratory Distress

Each year, approximately 300,000 babies in the United States are born between 32 and 36 weeks' gestation, according to national health data. This group is at an elevated risk for respiratory distress,... Read more

Molecular Diagnostics

view channel
Image: The new study findings emphasize the need for incorporating disease heterogeneity into type 2 diabetes care strategies (Photo courtesy of Cell 2025; doi.org/10.1016/j.cell.2025.05.00)

Molecular Fingerprint for Insulin Sensitivity Could Diagnose Diabetes Before Disease Develops

Insulin is a hormone essential for regulating blood sugar levels, and its dysfunction is a key factor in the development of diabetes. Insulin resistance, a condition where the body's cells do not respond... Read more

Hematology

view channel
Image: CitoCBC is the world first cartridge-based CBC to be granted CLIA Waived status by FDA (Photo courtesy of CytoChip)

Disposable Cartridge-Based Test Delivers Rapid and Accurate CBC Results

Complete Blood Count (CBC) is one of the most commonly ordered lab tests, crucial for diagnosing diseases, monitoring therapies, and conducting routine health screenings. However, more than 90% of physician... Read more

Immunology

view channel
Image: Custom hardware and software for the real-time detection of immune cell biophysical signatures in NICU (Photo courtesy of Pediatric Research, DOI:10.1038/s41390-025-03952-y)

First-Of-Its-Kind Device Profiles Newborns' Immune Function Using Single Blood Drop

Premature infants are highly susceptible to severe and life-threatening conditions, such as sepsis and necrotizing enterocolitis (NEC). Newborn sepsis, which is a bloodstream infection occurring in the... Read more

Pathology

view channel
Image: Results of AI-based 3D virtual H&E staining and quantitative analysis of pathological tissue (Photo courtesy of Nature Communications, DOI:10.1038/s41467-025-59820-0)

Virtual Staining Technology Paves Way for Non-Invasive Pathological Diagnosis

For more than 200 years, traditional pathology has depended on the technique of examining cancer tissues under a microscope, a method that provides only limited, specific cross-sections of the 3D structure... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.