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How AI Is Helping Predict Treatment Success in Liver Cancer Patients

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Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma DOI- 10.1016j.jhep.2025.04.017

People with advanced liver cancer (hepatocellular carcinoma, or HCC) are often treated with a combination of two medicines: atezolizumab and bevacizumab. These are part of a group called immunotherapy. But only some patients respond well. This study looked at a new way to predict who might benefit most—using computer analysis of CT scans.

Researchers reviewed CT scans from 152 patients before they started treatment. They used artificial intelligence (AI) and machine learning to measure tiny patterns in the liver that can’t be seen by the human eye. These patterns are called radiomics.

When combined with regular medical data, this new method did better than traditional cancer staging tools. It was able to predict how long patients might live and how likely they were to respond to treatment.

Patients placed in the “low-risk” group by the AI model lived much longer—around 28 months compared to just 5 to 6 months in the “high-risk” group. They also responded better to immunotherapy.

This approach may help doctors choose the right treatment for each person before starting. That could save time, avoid side effects, and improve outcomes.

This research shows how computer tools are being used to make cancer care more personal and precise.

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