New Blood Test Can Predict Future Breast Cancer
By LabMedica International staff writers Posted on 29 Apr 2015 |
Image: The Avance III 600 spectrometer for proton nuclear magnetic resonance analysis (Photo courtesy of Bruker).
The analysis of a blood sample could predict if a woman will get breast cancer within two to five years which could create a paradigm shift in early diagnosis of this malignant neoplasm as well as other diseases.
The method, called a metabolic blood profile is still in the early stages but over time the experts expect it could be used to predict breast cancer and more generally to predict chronic disease and the new method will lead to better prevention and early treatment of the disease.
Scientists at the University of Copenhagen (Frederiksberg, Denmark) used 20-year-old blood samples and other available data from 400 women who were healthy when they were first examined but who were diagnosed with breast cancer two to seven years after providing the first sample, and from 400 women who did not develop breast cancer. The team analyzed all compounds contained in the blood sample, instead of as is often done in health and medical science, examining what a single biomarker means in relation to a specific disease.
The plasma samples were analyzed by proton Nuclear Magnetic Resonance (1H NMR). NMR analysis was performed with a Bruker Avance III 600 spectrometer (Bruker Biospin Gmbh; Rheinstetten, Germany). The NMR spectra were subjectively evaluated by spectroscopists and data analysts in order to exclude as many noise regions from the data as possible and to include all peaks in the most parsimonious manner. The spectroscopists and data analysts were blinded to the case/control status.
While a mammography can detect newly developed breast cancer with a sensitivity of 75% the new metabolic blood profile is able to predict the likelihood of a woman developing breast cancer within the next two to five years with a sensitivity of 80%. The method was also used to test a different dataset of women examined in 1997. Predictions based on the new set of data matched the first dataset, which indicated the validity of the model.
Lars Ove Dragsted, PhD, a professor and senior author of the study said, “The potential is that we can detect a disease like breast cancer much earlier than today. This is important as it is easier to treat if you discover it early. In the long term, it will probably also be possible to use similar models to predict other diseases.” The study was published on March 10, 2015, in the journal Metabolomics.
Related Links:
University of Copenhagen
Bruker Biospin Gmbh
The method, called a metabolic blood profile is still in the early stages but over time the experts expect it could be used to predict breast cancer and more generally to predict chronic disease and the new method will lead to better prevention and early treatment of the disease.
Scientists at the University of Copenhagen (Frederiksberg, Denmark) used 20-year-old blood samples and other available data from 400 women who were healthy when they were first examined but who were diagnosed with breast cancer two to seven years after providing the first sample, and from 400 women who did not develop breast cancer. The team analyzed all compounds contained in the blood sample, instead of as is often done in health and medical science, examining what a single biomarker means in relation to a specific disease.
The plasma samples were analyzed by proton Nuclear Magnetic Resonance (1H NMR). NMR analysis was performed with a Bruker Avance III 600 spectrometer (Bruker Biospin Gmbh; Rheinstetten, Germany). The NMR spectra were subjectively evaluated by spectroscopists and data analysts in order to exclude as many noise regions from the data as possible and to include all peaks in the most parsimonious manner. The spectroscopists and data analysts were blinded to the case/control status.
While a mammography can detect newly developed breast cancer with a sensitivity of 75% the new metabolic blood profile is able to predict the likelihood of a woman developing breast cancer within the next two to five years with a sensitivity of 80%. The method was also used to test a different dataset of women examined in 1997. Predictions based on the new set of data matched the first dataset, which indicated the validity of the model.
Lars Ove Dragsted, PhD, a professor and senior author of the study said, “The potential is that we can detect a disease like breast cancer much earlier than today. This is important as it is easier to treat if you discover it early. In the long term, it will probably also be possible to use similar models to predict other diseases.” The study was published on March 10, 2015, in the journal Metabolomics.
Related Links:
University of Copenhagen
Bruker Biospin Gmbh
Latest Pathology News
- Hyperspectral Dark-Field Microscopy Enables Rapid and Accurate Identification of Cancerous Tissues
- AI Advancements Enable Leap into 3D Pathology
- New Blood Test Device Modeled on Leeches to Help Diagnose Malaria
- Robotic Blood Drawing Device to Revolutionize Sample Collection for Diagnostic Testing
- Use of DICOM Images for Pathology Diagnostics Marks Significant Step towards Standardization
- First of Its Kind Universal Tool to Revolutionize Sample Collection for Diagnostic Tests
- AI-Powered Digital Imaging System to Revolutionize Cancer Diagnosis
- New Mycobacterium Tuberculosis Panel to Support Real-Time Surveillance and Combat Antimicrobial Resistance
- New Method Offers Sustainable Approach to Universal Metabolic Cancer Diagnosis
- Spatial Tissue Analysis Identifies Patterns Associated With Ovarian Cancer Relapse
- Unique Hand-Warming Technology Supports High-Quality Fingertip Blood Sample Collection
- Image-Based AI Shows Promise for Parasite Detection in Digitized Stool Samples
- Deep Learning Powered AI Algorithms Improve Skin Cancer Diagnostic Accuracy
- Microfluidic Device for Cancer Detection Precisely Separates Tumor Entities
- Virtual Skin Biopsy Determines Presence of Cancerous Cells
- AI Detects Viable Tumor Cells for Accurate Bone Cancer Prognoses Post Chemotherapy