Award Title: Machine learning to identify predictors of outcome and survival in the treatment of mUM (metastatic uveal melanoma) with M-PHP (melphalan percutaneous hepatic perfusion).
Funder: The Academy of Medical Sciences

Abstract: Ocular melanoma, the most common primary ocular malignancy in adults, carries a poor prognosis: 50% of patients develop metastatic disease with a 10-25% 1-year survival and no established standard of care treatment. The liver is often the only site of metastatic disease  [1]  and current systemic treatments have poor response rates (<​20%), especially in those with progressive hepatic disease [2]. More recently, melphalan percutaneous hepatic perfusion (M-PHP); a novel treatment delivered by Interventional Radiologists via a minimally invasive approach has shown improved hepatic and overall progression-free survival versus best alternative care whilst exhibiting a reasonable side-effect profile. However, there remains a disparity in survival within this heterogeneous group of patients, with as many as 40% not responding to therapy (progressive disease/stable disease). Currently, we cannot predict who they will be and there is an unmet clinical need to identify which patients are more likely to respond. We aim to use Machine learning and Artificial Intelligence with the combination of pre-procedural imaging and routinely collected clinicopathologic datasets to accurately predict patient outcomes including survival and response to M-PHP therapy. We will develop and validate this ML model to provide a non-invasive decision support tool and thus aid the doctor-patient shared decision-making process to improve patient outcomes, cost-utility and spare patients the risk of ineffective and potentially toxic treatment.

Lay Abstract: Uveal melanoma, the most common primary eye tumour in adults, has a terrible prognosis with half of patients suffering from metastatic or distant disease. Only one out of every ten patients are alive after one year. Immunotherapy (medication), surgery, or, more recently, chemotherapy directly focused at the liver known as melphalan percutaneous hepatic perfusion(M-PHP) are all options for patients but deciding which treatment is best is not clear. The latter has shown promise and has the added benefit of being delivered through a small hole via the groin and neck. However, there is still a large discrepancy in survival, with four out of every ten patients failing to respond to therapy. At the moment, we cannot foresee who they will be, and being able to find which patients are likely to respond and which treatments are likely to be useful will improve decision making and save patients from ineffective treatment and side effects. Machine learning is a type of artificial intelligence that is used to find complicated patterns in big datasets, such as treatment response or survival outcomes. A computerised tomography (CT) scan creates detailed images of the inside of the body using X-rays and a computer. We know that these scans contain information that is currently hidden, and our goal is to employ artificial intelligence to extract and uncover this information to help predict treatment responses. Finally, we plan to develop a tool to aid in shared decision-making for metastatic uveal melanoma patients to improve current treatment paths.

Research Activity Codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health Categories: CANCER AND NEOPLASMS