Machine learning for improved clinical decision making ahead of epilepsy surgery

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)

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.