Genomics, imaging data to be fused in effort to treat liver cancer

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A genomic medicine company is working on developing algorithms to improve the diagnosis of liver cancer from imaging and molecular data.

Lucence Diagnostics announced the effort last week, reporting that it will be working with a biomedical informatics specialist, Olivier Gevaert, assistant professor of medicine and of biomedical data science at the Stanford University School of Medicine to develop algorithms through the use of artificial intelligence.

The project will aim to fuse imaging data—from ultrasound, computed tomography and magnetic resonance imaging—with sequencing data that includes both cancer mutations and viral DNA, in an attempt to find meaningful correlations between the two.

The project will evaluate a dataset of more than 5,000 patients to identify image changes and patterns that are linked to diagnostic and treatment outcomes in liver cancer, considered to be the second-leading cause of avoidable cancer deaths and also the fastest rising cancer type in both men and women in the U.S.

The best chance for survival requires surgery, but good characterization of the extent and type of the disease is crucial in successful surgical planning.

"Lucence is excited to advance the field of medical imaging to guide liver cancer treatment,” says Min-Han Tan, MD, founder and CEO of Lucence Diagnostics. “By combining radiology with our proprietary sequencing technology and track record in liver cancer data modelling, our AI algorithms will assist physicians in making better treatment decisions.''

Singapore-based Lucence Diagnostics invents non-invasive blood tests that improve cancer detection and treatment selection, using proprietary technology and its AI platform.

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