Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us

Download Mobile App




Machine Learning Approach Detects Cancer by Analyzing DNA in Blood Samples

By LabMedica International staff writers
Posted on 10 Jun 2019
Researchers have described a proof-of-principle approach for the screening, early detection, and monitoring of human cancer based on a machine learning approach that evaluates fragmentation patterns of cell-free DNA across the genome.

While cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer, characteristics of the origins and molecular features of cell-free DNA are poorly understood. More...
To correct this lack, investigators at Johns Hopkins University (Baltimore, MD, USA) developed a machine learning-based approach to identify abnormal patterns of DNA fragments in the blood of patients with cancer.

They used this DELFI (DNA evaluation of fragments for early interception) method to analyze the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancer and 245 healthy individuals.

The machine-learning model incorporated genome-wide fragmentation features with sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining this approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer.

"For various reasons, a cancer genome is disorganized in the way it is packaged, which means that when cancer cells die they release their DNA in a chaotic manner into the bloodstream," said first author Dr. Jillian Phallen, a postdoctoral research fellow at Johns Hopkins University. "By examining this cell-free DNA (cfDNA), DELFI helps identify the presence of cancer by detecting abnormalities in the size and amount of DNA in different regions of the genome based on how it is packaged."

"We are encouraged about the potential of DELFI because it looks at a completely independent set of cell-free DNA characteristics from those that have posed difficulties over the years, and we look forward to working with our collaborators worldwide to make this test available to patients," said senior author Dr. Victor E. Velculescu, professor of oncology at Johns Hopkins University.

The DELFI method was described in the May 29, 2019, online edition of the journal Nature.

Related Links:
Johns Hopkins University


New
Gold Member
Hematology Analyzer
Medonic M32B
POC Helicobacter Pylori Test Kit
Hepy Urease Test
New
Gel Cards
DG Gel Cards
New
Specimen Radiography System
TrueView 200 Pro
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

Hematology

view channel
Image: New research points to protecting blood during radiation therapy (Photo courtesy of 123RF)

Pioneering Model Measures Radiation Exposure in Blood for Precise Cancer Treatments

Scientists have long focused on protecting organs near tumors during radiotherapy, but blood — a vital, circulating tissue — has largely been excluded from dose calculations. Each blood cell passing through... Read more

Immunology

view channel
Image: The test could streamline clinical decision-making by identifying ideal candidates for immunotherapy upfront (Xiao, Y. et al. Cancer Biology & Medicine July 2025, 20250038)

Blood Test Predicts Immunotherapy Efficacy in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking targeted therapies, making immunotherapy a promising yet unpredictable option. Current biomarkers such as PD-L1 expression or tumor... Read more

Microbiology

view channel
Image: New diagnostics could predict a woman’s risk of a common sexually transmitted infection (Photo courtesy of 123RF)

New Markers Could Predict Risk of Severe Chlamydia Infection

Chlamydia trachomatis is a common sexually transmitted infection that can cause pelvic inflammatory disease, infertility, and other reproductive complications when it spreads to the upper genital tract.... Read more

Technology

view channel
Image: The sensor can help diagnose diabetes and prediabetes on-site in a few minutes using just a breath sample (Photo courtesy of Larry Cheng/Penn State)

Graphene-Based Sensor Uses Breath Sample to Identify Diabetes and Prediabetes in Minutes

About 37 million U.S. adults live with diabetes, and one in five is unaware of their condition. Diagnosing diabetes often requires blood draws or lab visits, which are costly and inconvenient.... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.