{"id":44626,"date":"2024-03-01T09:34:48","date_gmt":"2024-03-01T09:34:48","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=44626"},"modified":"2024-03-01T09:34:48","modified_gmt":"2024-03-01T09:34:48","slug":"machine-learning-model-enhances-prediction-of-liver-cancer-risk","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/machine-learning-model-enhances-prediction-of-liver-cancer-risk\/44626\/","title":{"rendered":"Machine Learning model enhances prediction of liver cancer risk"},"content":{"rendered":"

Groundbreaking research by the UC Davis Health team has pioneered a Machine Learning model that accurately predicts liver cancer risk.<\/h2>\n

The collaboration between clinicians and data scientists at UC Davis Health has yielded a revolutionary Machine Learning model designed to predict the likelihood of hepatocellular carcinoma (HCC),<\/a> a prevalent form of liver cancer.<\/p>\n

The findings demonstrate how AI techniques can assist physicians in providing early risk HHC risk assessment for metabolic dysfunction-associated steatotic liver disease (MASLD) patients, helping to provide more personalised care.<\/p>\n

Study co-author Aniket Alurwar, clinical informatics specialist at the UC Davis\u00a0Center for Precision Medicine and Data Sciences, commented: “MASLD can lead to HCC, but the disease is quite sneaky, and it’s often unclear which patients face that risk.<\/p>\n

“It doesn’t make sense to biopsy every patient with MASLD, but if we can segment for risk, we can track those people more closely and perhaps catch HCC early.”<\/p>\n

Addressing the threat of MASLD<\/h3>\n

MASLD, formerly known as nonalcoholic fatty liver disease (NAFLD), presents a significant health challenge, particularly as it is intricately linked with metabolic disorders such as Type 2 diabetes.<\/p>\n

With approximately a quarter of Americans affected by some form of MASLD<\/a>, the stakes are high. The team’s study represents an exciting method of leveraging the capabilities of Machine Learning algorithms to identify disease risk.<\/p>\n

Unveiling the power of predictive learning<\/h3>\n

Nine open-source algorithms were tested, and five were selected for additional evaluation and model development.<\/p>\n

These chosen algorithms were trained using deidentified health data from 1,561 UC Davis Health MASLD patients, among whom 227 later developed HCC.<\/p>\n

Subsequently, these top five algorithms underwent validation using data from 686 UC San Francisco patients sourced from deidentified medical records.<\/p>\n

Among these patients, 176 were diagnosed with HCC. Ultimately, the Gradient Boosted Trees algorithm emerged as the most accurate prediction model, demonstrating superior statistical accuracy, sensitivity, and specificity.<\/p>\n

Identifying new risk factors<\/h3>\n

While advanced liver fibrosis remains a prominent risk indicator for HCC, typified by elevated Fibrosis-4 Index (FIB-4) scores, the team’s analysis unearthed additional predictors, including high cholesterol, hypertension, bilirubin levels, and alkaline phosphatase (ALP) activity.<\/p>\n

The team discovered various pathways leading to HCC, with high FIB-4 levels being the most evident. Interestingly, some patients with low FIB-4 levels but elevated cholesterol, bilirubin, and hypertension also developed HCC.<\/p>\n

However, according to current guidelines, such patients would not typically receive preventive care measures.<\/p>\n

This multifactorial approach significantly enhanced the predictive accuracy of the Machine Learning model to 92.23%.<\/p>\n

With an impressive accuracy rate, the pilot model stands as a testament to the potential of AI in healthcare.<\/a><\/p>\n

Notably, the model identified ‘low-risk’ MASLD patients who may still face heightened HCC susceptibility, challenging conventional screening protocols.<\/p>\n

A roadmap to future AI advancements<\/h3>\n

Looking ahead, the UC Davis team remains committed to refining their model. By integrating clinical notes and exploring advanced AI techniques<\/a> like natural language processing, they aim to further enhance predictive accuracy.<\/p>\n

Ultimately, their vision extends to seamlessly integrating these advancements into electronic health records, empowering clinicians with real-time risk assessments.<\/p>\n","protected":false},"excerpt":{"rendered":"

UC Davis Health researchers have pioneered a Machine Learning model that accurately predicts liver cancer risk. Find out more.<\/p>\n","protected":false},"author":15,"featured_media":44638,"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":"\nMachine learning model enhances prediction of liver cancer risk<\/title>\n<meta name=\"description\" content=\"UC Davis Health researchers have pioneered a Machine Learning model that accurately predicts liver cancer risk. 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