Liver cancer is a serious disease that is often detected late.
This is because it rarely causes symptoms in its early stages, and many individuals are only examined if they already have liver disease.
According to Medical News Today, around one-fifth of cases develop in individuals without known liver disease.
This means these patients are often not identified in time, as they are not included in current screening programs.
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Data and method
A new study published in Cancer Discovery analyzed large volumes of health data from sources including the UK Biobank and the U.S. All of Us program.
Researchers identified several hundred cases of liver cancer and used this information to develop an artificial intelligence–based model.
The model was tested across different groups and demonstrated a strong ability to distinguish between individuals with and without the disease.
It relied, among other factors, on common data such as age, lifestyle, and blood test results.
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Earlier detection
When compared with existing tools, the model was better at identifying the right patients and reducing errors.
Even a simplified version using fewer data points performed better than current methods.
Advanced and costly data, such as genetic information, did not improve the model.
According to the study, standard health data may be sufficient to predict the risk of liver cancer.
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This means that more patients could potentially be detected earlier without the need for more complex examinations.
Sources: Medical News Today and Cancer Discovery.
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