Innovative Artificial intelligence disclosing hepatic cancer

Liver cancer – a persistence issues:

“Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate,” reported the researchers. 

“Increased early detection of liver cancer could save lives, but currently available screening tests are underutilized and miss many cancers,” said Victor Velculescu, MD, Ph.D., professor of oncology and co-director of the Cancer Genetics and Epigenetics Program at the Johns Hopkins Kimmel Cancer Center, who co-led the study with Zachariah Foda, MD, Ph.D., gastroenterology fellow, Akshaya Annapragada, MD/Ph.D student, and Amy Kim, MD, assistant professor of medicine at the Johns Hopkins University School of Medicine.

Artificial intelligence – a trustworthy assistance in liver cancer diagnosis:

A recent study conducted by Johns Hopkins Kimmel Cancer Center researchers introduces a groundbreaking artificial intelligence-based blood testing method for the early diagnosis of hepatocellular cancer (HCC), a prevalent form of liver cancer. HCC often arises due to chronic liver diseases, including chronic viral hepatitis and non-alcoholic fatty liver disease, affecting around 400 million people worldwide. The newly developed technology, known as DELFI, focuses on enhancing cancer detection through the innovative utilization of machine learning models in the field of diagnostic techniques.

The DELFI system employs a blood test to scrutinize cell-free DNA fragments within the bloodstream, with a particular focus on liver cancer. This novel approach involves the assessment of how DNA is organized within cell nuclei, which distinguishes healthy cells from cancerous ones. Healthy cells neatly organize their DNA, much like a well-arranged suitcase, carefully compartmentalizing different regions of the genome. In contrast, cancer cell nuclei resemble disorganized suitcases, where genomic elements are scattered haphazardly. When cancer cells perish, they release DNA fragments into the bloodstream in a chaotic manner.

According to their statement in the journal Cancer Discovery, the study involved the analysis of 724 plasma samples from individuals across the U.S., E.U., and Hong Kong, comprising those with HCC and individuals at high risk of developing it. To establish and validate their machine learning model, an artificial intelligence category that leverages data and algorithms to enhance precision, the researchers collected plasma samples from 501 individuals, including 75 with HCC, said Foda. 

The DELFI technology demonstrated remarkable results by successfully identifying early-stage liver cancers, showcasing an impressive overall sensitivity of 88% and an exceptional specificity of 98%. In samples collected from individuals at a high risk of HCC, the test exhibited a sensitivity of 85% and a specificity of 80%. This innovation has the potential to significantly enhance the number of liver cancer cases detected and revolutionize early cancer diagnosis, far surpassing the capabilities of current diagnostic techniques.

Wrap-Up:

With the integration of artificial intelligence and computational biology, this pioneering blood testing technology presents a promising advancement in the field of cancer detection. The researchers are now looking forward to the validation of this approach in more extensive clinical studies to make it accessible for wider clinical use, offering hope for improved diagnostic techniques in hepatocellular cancer and other systemic conditions. According to Kim, co-senior author of the study, “Currently, less than 20% of the high-risk population get screened for liver cancer due to accessibility and suboptimal test performance. This new blood test can double the number of liver cancer cases detected, compared to the standard blood test available, and increase early cancer detection.”

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