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Mayo Clinic researchers Yashbir Singh, John E. Eaton, Sudhakar K. Venkatesh, and Bradley J. Erickson have developed an innovative AI tool to predict hepatic decompensation in individuals with primary sclerosing cholangitis (PSC). PSC is a chronic disease that damages the bile ducts and can lead to liver failure.
Hepatic decompensation marks a critical stage of advanced liver disease, and clinicians have long faced challenges in predicting who is at risk. The Mayo Clinic's new AI tool addresses this gap by combining body fat and muscle composition data with insights extracted from computed tomography (CT) scans using computational radiomics. By analyzing these tissues, the AI model identifies patterns linked to an increased risk of liver failure.
The study involved 80 PSC patients, including 30 with hepatic decompensation, 30 without, and 20 patients in an external validation set. The AI model achieved impressive results, correctly identifying at-risk patients with 97% accuracy. By recognizing these risks early, clinicians may be able to intervene sooner and improve patient outcomes.
While the study focused on PSC, the team emphasized the broader implications of their work.
“It may hold promise for the detection of other PSC-related complications, such as cholangiocarcinoma, as well as applications in more prevalent chronic liver diseases like non-alcoholic fatty liver disease (NAFLD).”
This non-invasive, data-driven approach offers a powerful way to assess health risks and provide more tailored treatments. Despite the promising findings, the researchers acknowledge the limitations of the study, which include a limited sample size and a single-center design.
“However, further research is necessary to validate our findings on a large-scale, independent dataset, ensuring the robustness and generalizability of the model.”
In conclusion, this study shows how detailed information from CT scans can help clinicians predict severe liver problems in patients with PSC. By identifying hidden patterns in the images, they can better understand risks and create personalized treatment plans. This approach could improve care for PSC and other long-term liver diseases.
Keywords: cancer, radiomics, body composition, machine learning, primary sclerosing cholangitis, computer tomography Click hereto sign up for freeAltmetric alerts about this article.
About Oncotarget:
Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.
Oncotarget is indexed and archived byPubMed/Medline,PubMed Central,Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).
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