Identification of a biomarker of sensory dysfunction for patients who have developed neuropathic pain following cancer chemotherapy using functional Magnetic Resonance Imaging (fMRI) and machine learning.

Aims & Objectives

• High accuracy, sensitivity and specificity of prediction of sensory dysfunction for individual patients with CIPN using machine learning and functional magnetic resonance imaging (fMRI).

Method

Patients will be recruited and clinically assessed by a band 5 research nurse employed part-time 1.5 days/week for 2 years to support the study. Two groups of 20 patients will be identified: i) patients with chronic sensory neuropathy as a prominent feature of post-chemotherapy CIPN and ii) patients who have received chemotherapy but not developed or recovered from neuropathy. Patients with mild-moderate mood and anxiety symptoms will not be excluded; however, patients with serious systemic disease or varying intensity of perceived pain will be excluded. Patients will be characterised using the Brief Pain Inventory, standard mood and anxiety rating scales and Quantitative Sensory Testing (QST) (Scott et al., 2012). A blocked fMRI design will be used with manual brush-evoked allodynia applied according to a previous description for affected and unaffected body sites lasting about 16 min (Schweinhardt et al., 2006). Resting state fMRI will be acquired for 10 min. Machine learning algorithms will then be applied in a within study replication (cross-validation) framework according to previous work in Dundee [see machine learning references] and individual patient accuracy, sensitivity and specificity of the predicted presence of sensory disturbance determined. The student need not participate in patient recruitment and clinical assessment (unless they wish to do this as the nurse will cover these activities).