Quantitative, multimodal, imaging-based assessment of hypoxia in Non-Small Cell Lung Cancer

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Lung Cancer is the leading cause of cancer death in the UK and non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Hypoxia, a lack of oxygen, in lung cancer tumours has been shown to lead to worse patient outcomes and hypoxic tumours require up to three times as much radiation therapy as non-hypoxic tumours. A recent study at the University of Oxford showed that the anti-malarial drug atovaquone is effective at reducing hypoxia in tumours of non-small cell lung cancer patients. The drug is already approved, and it would therefore be easy to adopt into clinical practice. Currently hypoxia is measured using [18F]-fluoromisonidazole Positron Emission Tomography and Computed Tomography (FMISO-PET/CT) scans, where the tumour and descending aorta are segmented manually by an expert clinician to calculate the hypoxic volume. This is a time-consuming process and automating the process would speed up the time taken to measure hypoxia during clinical trials. The FMISO PET scans are also expensive and only used in research, therefore, to identify patients with hypoxic tumours during their cancer pathway, hypoxia needs to be measured from routinely collected or cheaper modalities.In this project we aim to automate the process of measuring hypoxia in lung cancer tumours using multimodal imaging data. Using Artificial Intelligence (AI) including deep learning techniques, novel methods for tumour segmentation from multimodal imaging data such as FMISO-PET/CT scans will be developed. Furthermore, deep radiomics will then be investigated to extract features that could measure of hypoxia within the tumour volume. Both deep learning and deep radiomics will then be used to see if any hypoxic features can be found from other modalities that are routinely collected or cheaper that could easily be integrated into a patient's cancer pathway. Deep Learning has been successful in many medical imaging tasks, including image segmentation. Radiomics is an active area of research for extracting textural features from radiological images, and recent studies have shown that convolutional neural networks can capture textural information, therefore advanced deep radiomic techniques could be developed for extracting hypoxic features from different modalities.The project by its nature is high-collaborative venture and collaboration includes Department of Oncology in Oxford, and Oxford University Hospitals NHS Foundation Trust. The collaboration will provide access to the Atovaquone as Tumour HypOxia Modifier (ATOM) dataset, containing non-small cell lung cancer patients, each with many imaging and non-imaging modalities.In summary, automating the time-consuming task of measuring hypoxia from FMISO-PET/CT scans will speed up the process and allow more patients to be included in future clinical trials. Additionally, being able to measure hypoxia from routinely collected or cheaper modalities will allow the measurement of hypoxia to be integrated into the patient cancer pathway. This would mean that patients with hypoxic tumours could be offered alternative treatment to improve patient outcomes.The project falls within the EPSRC Healthcare Technologies research theme and the Medical Imaging and AI Technologies research areas.