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Research Topic: Developing Artificial Intelligence Solutions in Ovarian Cancer Diagnosis
Supervisors: Dr Nishant Ravikumar, Dr Nic Orsi, Dr Kieran Zucker, Prof Geoff Hall
About Jack: I have a maths integrated masters and a year of experience as a data analyst - essentially, I already know about Machine Learning and I'm beginning to turn that knowledge towards medical research.
Project description: Ovarian cancer (OC) carries the heaviest mortality burden of all gynaecological malignancies, with 7,400 new diagnoses per year in the UK alone. Its diagnosis has been subject to the digital revolution that has taken place in histopathology over the last decade, with clinical departments progressively moving towards working with digital images of histology slides. Whilst this technological advance offers histopathologists advantages in terms of smooth workflow, remote access and low volume storage, the corollary is the generation of petabytes of digital information. The latter has been the focus of a number of endeavours in artificial intelligence (AI) aimed at extracting diagnostic information from digital images, with promising early results in defining grade and molecular profiles across a range of malignancies. The project aims to extend these pioneering approaches to OC by (1) differentiating between ovarian lesions in the progression to invasive carcinoma, (2) diagnosing across the spectrum of ovarian tumours, (3) classifying tumour grade (e.g. low vs. high grade in serous lesions), (4) identifying tumour molecular profiles, and (5) developing models of markers of response to therapy.