Thornton, Andrew; (2024) Quantitative PET/CT imaging biomarkers in Lung Disease. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

It is estimated that up to half of the UK population will be affected by cancer during their lifetime (1, 2). Whilst half of all people diagnosed with cancer will survive their disease for ten years or more – there is a wide variation in survival between different cancer types and only 9.5% of those diagnosed with non-small cell lung cancer will survive for ten years or more (2, 3). Imaging plays a crucial role in the detection of cancer, assessment of suitability for and planning of treatment, and assessment of response to treatment. The Tumour-Node-Metastasis (TNM) cancer staging system is the globally recognised standard used for cancer registration of almost all cancers, in particular non-small cell lung cancer, and it categorises patients into stages by the size and spread of disease (4). These stages predict survival and suitability for treatments but these are coarse qualitative categorical determinations which do not completely or fully explain patient survival and prognostication of individual patient survival is poor. The incompleteness of the staging system assessment of prognosis is further highlighted with the development of treatments specifically tailored to individual tumour characteristics, for example anti-angiogenesis therapies, tyrosine-kinase inhibitors and checkpoint inhibitors. These treatments are only effective in subsets of patients with mutations relating to the expression of cell-receptors. The work in this thesis provides evidence to suggest that quantitative imaging biomarkers can improve prognostication, predict immunohistochemical expression and mutation status, and thereby contributes to developing, and ultimately delivering, non-invasive precision medicine approaches in patients with non-small cell lung caner. In particular cut-offs of SUVmax>7.6 & TBRlung > 26.2 predict prognosis, and more strongly entropy_ct_ssf_2 > 4.81 is an independent predictor of survival in a combined model with staging and survival, it also stratifies survival when there mutations present. In addition, the thesis also demonstrates that the quantitative techniques are helpful beyond cancer, and can provide additional information in interstitial lung disease. In particular, it demonstrates that 18F-FDG uptake in COVID-19 increases with time after infection and correlates with severity, and provides evidence for the utility of steroid treatment in PCLD as this treatment is associated with reduced 18F-FDG uptake in these patients.

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