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Immunotherapies are a type of cancer treatment that activate the body’s immune system to attack cancer cells. These types of therapies have had some great successes, revolutionising cancer treatment with improved outcomes and survival for patients. However, not all patients respond to immunotherapies and there can be some dangerous side effects. These treatments can also be very expensive, and not all immunotherapies are available on the NHS.
Immunotherapies can be used on liver cancer, specifically hepatocellular carcinoma (HCC). Over 6,500 people are diagnosed with liver cancer each year and only 8% survive for 10 or more years1.
Currently, there aren’t many indicators to predict how someone with liver cancer will respond to immunotherapy treatment and given the severity of potential side effects, can lead to a lot of uncertainty and potentially additional risk to patients.
A team of researchers at Imperial, involving a Clinical Research Fellow from the CRUK Convergence Science Centre, have designed a machine learning pipeline to extract information from pre-treatment scans and images to identify features that could indicate how a patient will respond to immunotherapy. These machine learning models are capable of outperforming current clinical markers in predicting survival and response to immunotherapy in liver cancer patients. This research could go on to guide personalised treatment pathways for patients and ensure that those who receive immunotherapy will benefit from treatment and those who won’t benefit can explore alternative options and don’t need to take unnecessary risks.
