{"id":48348,"date":"2024-06-07T09:22:15","date_gmt":"2024-06-07T08:22:15","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=48348"},"modified":"2024-06-07T09:22:15","modified_gmt":"2024-06-07T08:22:15","slug":"researchers-develop-ai-blood-test-to-diagnose-lung-cancer","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/researchers-develop-ai-blood-test-to-diagnose-lung-cancer\/48348\/","title":{"rendered":"John Hopkins Medicine researchers develop AI blood test to diagnose lung cancer"},"content":{"rendered":"

Scientists from the\u00a0Johns Hopkins Kimmel Cancer Center have developed an AI blood test that accurately identifies lung cancer.<\/h2>\n

The team has performed a prospective study to demonstrate that AI technology<\/a> could effectively identify people who are more likely to have lung cancer based on DNA fragment patterns in blood.<\/p>\n

This new blood test could improve screening and reduce death rates by identifying high-risk patients who would benefit from follow-up CT scans, according to the team’s computer modelling.<\/p>\n

The study’s corresponding author,\u00a0Victor Velculescu, MD, PhD, explained: “We have a simple blood test that could be done in a doctor’s office that would tell patients whether they have potential signs of lung cancer and should get a follow-up CT scan.”<\/p>\n

Limitations of current lung cancer diagnostics<\/h3>\n

Lung cancer is the deadliest cancer in the US and worldwide<\/a>, per the National Cancer Institute and World Health Organization (WHO). Annual CT scans for high-risk patients can detect lung cancer early and reduce deaths.<\/p>\n

The US Preventive Services Task Force recommends screening for 15 million people aged 50-80 with a smoking history, but only 6%-10% comply each year.<\/p>\n

Velculescu notes that reluctance is due to the time taken to perform and the radiation exposure involved in the screenings.<\/p>\n

AI blood test development<\/h3>\n

To address these challenges, Velculescu and his team developed an AI blood test over the past five years.<\/p>\n

This test identifies lung cancer by detecting patterns in DNA fragments in the blood<\/a>. In healthy cells, DNA is neatly packaged, like a rolled-up ball of yarn, but in cancer cells, it is disorganised.<\/p>\n

When cells die, their DNA fragments enter the bloodstream. The test distinguishes between the chaotic DNA fragments from cancer cells and the orderly fragments from healthy cells.<\/p>\n

Excellent accuracy<\/h3>\n

To train the AI blood test, the team performed a study involving around 1,000 patients with and without lung cancer who met traditional screening criteria.<\/p>\n

The researchers trained AI software to identify DNA fragment patterns in the blood of 576 people, with and without lung cancer and confirmed its accuracy in another group of 382.<\/p>\n

The novel blood test has a 99.8% negative predictive value, meaning it would miss only 2 in 1,000 cases.<\/p>\n

Their simulations suggest that increasing lung cancer screening rates to 50% within five years could quadruple detected cases and boost early detection by 10%, potentially preventing 14,000 cancer deaths over that period.<\/p>\n","protected":false},"excerpt":{"rendered":"

Scientists from the\u00a0Johns Hopkins Kimmel Cancer Center have developed an AI blood test that accurately identifies lung cancer.<\/p>\n","protected":false},"author":15,"featured_media":48349,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[10551],"tags":[570,24493,849],"acf":[],"yoast_head":"\nResearchers develop AI blood test to diagnose lung cancer<\/title>\n<meta name=\"description\" content=\"Scientists from the\u00a0Johns Hopkins Kimmel Cancer Center have developed an AI blood test that accurately identifies lung cancer.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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