Machine learning for improved clinical decision making ahead of epilepsy surgery

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Aim of the PhD Project:Automate metabolic lesion detection and segmentation on FDG-PET using normal/abnormal weakly-supervised signal decomposition Differentiating seizure-onset and seizure-spread areas Harness biological and clinical knowledge for optimised detection of relevant lesions Project description:Background: Epilepsy is the most common serious neurological condition, with >600,000 people affected in the UK (https://www.epilepsysociety.org.uk/about-epilepsy). Over 30% of people with epilepsy have medication-resistant seizures. Focal seizures start in one part of the brain, and surgery may be an option if the epileptogenic zone can be identified. Pre-surgical assessment can take >1yr and involves a range of diagnostic procedures. Imaging, particularly MRI, has a central role in this process. [18F]fluorodeoxyglucose position emission tomography (FDG-PET) is much more sensitive than MRI and particularly well developed in our Centre. Current clinical evaluation of imaging relies on time-consuming and subjective visual analysis and is challenging even for experts. Focal cortical dysplasias (FCDs) are small malformations of the brain and one of the most common substrates underlying refractory epilepsy. They can often be detected by MRI alone, but 15-30% of presurgical patients are "MRI-negative", with the proportion likely higher in national referral centres. In those, FDG-PET has become much relied upon over the past five years: it is essential to have very good localization hypotheses prior to implantation of intracranial electrodes, as these are invasive, carry a small risk, and can at best sample ~10% of brain, with seizure onset zones potentially undetected if electrodes are placed just a few mm away. Deep learning methods have gained traction for detecting and characterising abnormalities and lesions [1-3]. As they are fast once trained, they are more suitable than traditional statistical/machine learning methods [4-10] for implementation on clinical workstations. Deep learning work has exclusively focused on MRI analysis, largely ignoring the high yield of FDG-PET, especially in MRI-negative patients. One ML study has investigated FDG-PET, based on asymmetries alone and ignoring MRI [11]; another combined FDG-PET and MRI but used handcrafted features and Support Vector Machines (SVMs) / patch-based classification rather than deep learning [12]. This research will demonstrate the feasibility and usefulness of quantitative joint analysis of FDG PET and MR in the clinical setting. It will support clinicians with patient management and may enable more patients to have surgery, which could represent significant cost savings and a positive impact on patient quality of life. Goals: 1) Produce tools for quantitative analysis of FDG-PET scans of patients with epilepsy. 2) Combine with MR-based tools 3) Obtain prospective validation of the tools developed and obtain clinician feedback 4) Integrate non-imaging information into the image analysis, e.g. depression scores, EEG data, and semiology (via semantic information or ictal video-EEG directly)