Machine-learning algorithms tuned to detecting cancer DNA in the blood could pave the way for personalized cancer care.
copyright by www.the-scientist.com
Modern cancer medicine is hampered by two big challenges—detecting cancers when they are small and offering cancer patients personalized, dynamic cancer care. To find solutions, several academic labs and biotech firms are turning to artificial intelligence, working to develop machine-learning algorithms that could help decipher weak signals in the blood that can identify cancers at an early stage and indicate whether a cancer is responding to treatment in real time.
“You have to find this needle in a haystack . . . this very weak signal amongst all of the cacophony of everything else happening in the body,” says Dave Issadore , a bioengineer at Pennsylvania State University and founder of Chip Diagnostics, which is developing a machine-learning method to diagnose disease by sequencing extracellular vesicles’ cargo.
So far, machine-learning algorithms designed to detect minute quantities of tumor DNA in a blood sample—the goal of so-called liquid biopsies—have performed well in clinical validation studies, but no self-learning algorithm has yet been approved for clinical use. These have the potential to outperform imaging and tissue biopsies in detecting and monitoring cancers by looking for mutations in DNA, RNA, and proteins directly from the blood.
“I think the dream of liquid biopsy is to, A), detect cancer when there is very little of it, and, B) detect cancers during treatment,” says Dan Landau, a clinical oncologist and researcher at Weill Cornell Medical School in New York. However, in the both contexts, liquid biopsy techniques struggle to accurately detect cancer among the infinitesimally small quantities of tumor nucleic acids in the blood. Although the technique’s performance varies between cancer types, liquid biopsies so far have been able to detect cancer in around half of early-stage patients diagnosed through imaging, giving it a sensitivity of just 50 percent.
Image Credit: The Scientist
News This Week
Although skin aging has not been related to many health complications, it has aesthetic issues. Some of the common symptoms of skin aging are changes in the skin texture (rough, dry, and itchy), discoloration, [...]
In an article published in the journal Science of the Total Environment, researchers have highlighted the significance and potential risks associated with the release of nanoparticles from coal-fired power plants. Applying the single-particle inductively coupled plasma mass [...]
A paper recently published in the journal ACS Applied Energy Materials demonstrated the feasibility of using a covalent organic framework (COF)-based nanofluidic hybrid membranes (NHMs) to attain enhanced interfacial ion transport for the generation of osmotic [...]
The excess fluoroquinolones (FQs) discharged into the aquatic environment due to human activities must be removed cost-effectively. In an article published in the Journal of Cleaner Production, the authors fabricated an environment-friendly dealkaline lignin-grafted Fe3O4 nanoparticles [...]
Controlling strong electromagnetic fields on nanoparticles is the key to triggering targeted molecular reactions on their surfaces. Such control over strong fields is achieved via laser light. Although laser-induced formation and breaking of molecular [...]
A paper recently published in the journal Nature Communications demonstrated an effective method to realize on-chip nanophotonic topological rainbow devices using the concept of synthetic dimensions. Importance of Synthetic Dimensions for the Construction of Topological Nanophotonics [...]
In a study available in the journal Materials Today: Proceedings, silver nanoparticles (Ag NPs) were fabricated using a green method using Citrus X sinensis. Methylthioninium Chloride (MB) Dyes Threatening the Environment Dye and sewage drainage into [...]