Lung Cancer Detection Test Applies Advanced Machine Learning to Whole-Genome Sequencing Data

DELFI Diagnostics (Baltimore, MD, USA) is developing an early cancer detection test that leverages sophisticated machine learning algorithms. These algorithms analyze whole-genome sequencing data to compare patterns and attributes of an individual's cell-free DNA (cfDNA) against those found in cancerous and non-cancerous populations. Their scientific approach is grounded in fragmentomics, the study of how cancer cells, which are generally more disorderly than normal cells, leave behind specific cfDNA fragments in the blood when they die. By using machine learning in conjunction with whole-genome sequencing, the DELFI platform can sift through millions of data points to accurately identify individuals who might have cancer, including those in the early stages.

DELFI’s fragmentomics-based methodology deploys machine learning algorithms on whole-genome sequencing data from cfDNA that has undergone low-coverage sequencing. This not only allows for the analysis of fragment lengths that could indicate the aberrant packaging of DNA within cancer cells but also reveals other DNA alterations such as mutations, amplifications, and deletions. The company's machine learning tools are compatible with standard laboratory equipment and procedures, making the test both affordable and feasible for mass screening. By analyzing a significantly larger set of molecular data compared to traditional methods, the system can deliver more comprehensive clinical insights. A blood-based screening test for early detection of lung cancer has the potential to be easily integrated into regular healthcare practices, benefiting populations globally.

"Lung cancer is the leading cause of cancer death globally, yet only six percent of the over 15 million Americans who are eligible to be screened annually do so," said Peter B. Bach, M.D., DELFI Chief Medical Officer. "DELFI is built to solve the highest-burden population health issues, starting with lung cancer."

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