Introduction

Liquid biopsy is a revolutionary technique that is opening previously unexpected perspectives. It consists of the detection and isolation of circulating tumor cells, circulating tumor DNA and exosomes, as a source of genomic and proteomic information in patients with cancer.

How does a liquid biopsy work?

Tumors release a variety of biomolecules into the bloodstream that can be collected via a blood test, separated from the plasma, and studied. The circulating tumor DNA (ctDNA) and intact circulating tumor cells (CTCs) are two of the components that are targeted during a liquid biopsy. The following data are analyzed to obtain insight into the tumor:

5. RNA and protein expression
5. DNA and chromosomal abnormalities
5. Amplification, deletions, and translocations
5. Point mutations

Doctors use this data to learn about a patient’s cancer. A liquid biopsy might be used to determine the best cancer therapy, to track how a patient is responding to treatment, or to discover whether a cancer has returned.

Artificial intelligence to improve liquid biopsy

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.

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.

Artificial neural networks, which use thousands of connected nodes to interpret data much like neurons in the brain and form the basis of machine learning, can process vast amounts of data and identify patterns that would likely elude a human doctor. Not only that, machine learning is self-improving; as more data are put into the system, it fine-tunes its own algorithm to improve its diagnostic acumen. “It’s an evolving diagnostic.

With improved power to detect cancer at low levels, “oncology will become a much more iterative field, where doctors are guided in how to treat a tumor in real time.”

made to obtain this accuracy could introduce biases.

The journal invites different types of articles including original research article, review articles, short note communications, case reports, Editorials, letters to the Editors and expert opinions & commentaries from different regions for publication.

A standard editorial manager system is utilized for manuscript submission, review, editorial processing and tracking which can be securely accessed by the authors, reviewers and editors for monitoring and tracking the article processing. Manuscripts can be uploaded online at Editorial Tracking System (https://www.longdom.org/editorial-tracking/publisher.php) or forwarded to the Editorial Office at https://www.longdom.org/swarm-intelligence-evolutionary-computation.html The Journals includes around 150Abstracts and 100 Keynote speakers have given their valuable words. The meet has provided a great scope for interaction of professionals including in addition to clinical experts and top-level pathologists and scientists from around the globe, on a single platform.

Media Contact:

Sarah Rose

Journal Manager

International journal of swarm intelligence and evolutionary computation

Email: evolcomput@journalres.org