Exploring advanced FDG PET-CT imaging feature analysis for more accurate diagnosis and outcome prediction in suspected large vessel vasculitis

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The aim of this project is to discover and validate predictive parameters extracted from FDG PET-CT images of patients with and without LVV that will allow the development of a radiomic model that can determine the prognosis of LVV patients effectively.Objectives:- Collate outcome data for a large single centre cohort of patients with baseline FDG PET-CT data- Use advanced feature analysis and machine learning to develop putative diagnostic and predictive imaging biomarkers in test and validation cohorts derived from FDG PET-CT images of patients with and without GCA from dataset from a single centre - Correlate outcomes with extracted parameters to determine which parameters have the best performance characteristics- Validate test performance within a multi-centre FDG PET-CT imaging cohort allowing for harmonisation of the process across different scanners. Data pooling facilitated as part of an established GCA multi-centre trial in the UK (TARGET Consortium, MRC)- Explore the potential of this process in (a) other forms of LVV, and (b) MRA and CTA imagingThe facilities used for this project will include:- Deep learning software to build a radiomic model- Image analysis software (LifeX)- Baseline FDG PET-CT data from the 2011-2017 Leeds LVV cohort - FDG PET-CT from patients who were tested but not diagnosed with LVV- FDG PET-CT data from other centres to validate the model - MRA & CTA data taken during the treatment of the 2011-2017 Leeds LVV cohortThe analysis of medical images requires developing an understanding of interactions between modality / modalities and disease. While PET-CT, MRA and CTA imaging of LVV is well established, not all the data provided by these scans will have been analysed; the relevant extracted features could provide valuable insights into the characteristics of the disease that have not been discovered through routine / traditional clinical approaches.The radiomic features (the parameters extracted) relevant to LVV prognosis are currently unknown but could include:- the heterogeneity of the vessel (textural analysis)- size, shape and location of inflammation- relationship with neighbouring tissues (requires statistical analysis considering parameters such as energy and entropy)- intensity of signal- density of tissue- parameters that may be unique to the illness and not established previouslyOnce the parameters are determined, their relationship to the disease and its phenotype may be established. The features will be vulnerable to image quality of the data and image processing methods such as over smoothing in the case of textural analysis. Therefore the methods will require optimisation and will not necessarily be directly transferable from previous work.Once the parameters have been established, we will create a novel radiomic model that can: (a) determine predictive biomarkers in a given image and (b) make patient outcome predictions. For these purposes, we will utilise deep learning with the help of a convolutional artificial neural network. The multiple layers of this network will serve a purpose, ranging from data extraction, through to the evaluation of a given feature. The image will cascade through the layers being weighted by the criteria and the model will make a judgement about the accuracy and precision of the prognosis. The model will need to be trained to recognise the predictive biomarkers with a large data set to minimise incorrect predictions.As the project will be co-supervised by clinicians at the forefront of LVV research, it will be designed to be clinically relevant and useful.