Predicting CAC using hybrid PET/CT imaging

Unintentional body 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 cancers. However, research has only recently begun to uncover the molecular basis of cachexia. 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. This project, as part of an international consortium, explores novel approaches to better define the onset of cancer-induced cachexia before weight loss occurs in order to implement interventions earlier. In this project, we will employ positron emission tomography (PET) as a method of molecular imaging. PET can detect sugar uptake in all body parts and thus generate a metabolic activity map non- invasively. We propose to develop a computational framework that uses artificial intelligence to support an automated analysis of metabolic interactions between multiple organs involved in the cancer and cachexia. By doing so, we reveal the metabolic connectivity between organs and detect the mobilisation of resources in fat and muscle tissue, before physical 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.