New tool accurately predicts liver cancer recurrence
A Singapore team of clinician-scientists and researchers has developed a new tool that accurately predicts which liver cancer patients are likely to experience a recurrence after surgery. The machine-learning tool combines genetic and clinical information and was found to outperform the commonly used TNM staging system, which is based on tumor burden, across independent patient groups.
A team of clinicians and researchers from Singapore developed a machine-learning tool that accurately predicts liver cancer recurrence after surgery, outperforming the commonly used TNM staging system. The study, published in Gut, discovered two distinct ways hepatocellular carcinoma (HCC) can recur and offers personalized approaches to risk prediction and targeted therapies.
Liver cancer is the third leading cause of cancer-related death globally, particularly affecting Asian populations. The tool combines genetic and clinical information, including tumor size, blood markers, cancer stage, and specific genes linked to recurrence, to predict a patient's risk. When validated in independent cohorts, the tool achieved a performance score of 86%, significantly higher than the TNM staging system, which scored between 56% and 68%.
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