Unintentional weight loss is common in patients suffering from advanced cancer. This condition has long been recognised as a frequent and life-threatening complication of many malignancies, but research has only recently begun to uncover its molecular basis. Clinicians refer to cancer associated cachexia (CAC) once weight loss exceeds 5% over 6 months. Therapeutic strategies to revert weight loss after it commenced are ineffective, possibly because they are initiated too late or tackle the wrong pathway. The project explores novel approaches to diagnose CAC before weight loss occurs to implement therapeutic interventions earlier.

We propose to develop a computational framework that uses artificial intelligence (AI) to support an automated analysis of interactions between organs that we can visualise and quantify non-invasively using positron emission tomography (PET). PET imaging of glucose uptake reveals the metabolic connectivity between organs and can conceivably detect the mobilisation of resources in fat and muscle tissue before weight loss weakens the patient. If successful, our computational framework will be developed into a clinical decision-making tool integrating ethical and legal considerations. This tool will determine individual patient risk to develop cachexia and, thus, support personalised therapeutic interventions.