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Rose Collet

Rose Collet

  • Email: scrmc@leeds.ac.uk
  • Thesis title: Models of multiscale fluid transport for characterising treatment response in breast tumours
  • Supervisor: Dr Zeike Taylor, Dr David Buckley

Profile

I joined the Fluid Dynamics CDT in 2020, which offers an integrated MSc and PhD and covers a wide range of topics within the subject.

I was immediately attracted to biomedical applications, and my MSc team project investigated the validity of the Clauser plot method for measuring wall shear stress in preclinical models of cardiovascular disease. 

Prior to this, I studied mathematics at the University of Glasgow and graduated with a first class BSc (Hons) in 2020. During this time I spent a summer at the University of Calabria (2019), where I studied abstract algebra: semi-direct products with application to the classification of extra-special finite p-groups.

In my final year at Glasgow I mainly focussed on applied mathematics and fluid dynamics: my dissertation investigated the thermal stability of a horizontal layer of fluid heated from below, both in the absence of external forces (Rayleigh-Bénard convection) and in the presence of rotation and magnetic fields. 

Research interests

I am broadly interested in biomedical applications of fluid dynamics, and tumour growth modelling in particular. I am carrying out my PhD within CISTIB.

My project aims to develop models of fluid transport in breast tumours, to characterise response to neoadjuvant chemotherapy (NACT) in a patient-specific manner.

Neoadjuvant chemotherapy is the standard-of-care for newly-diagnosed patients with locally advanced breast cancer (stages II-III), but only 39% of patients achieve pathological complete response (pCR) and up to 12% experience no response at all. Non-responsive patients suffer the side-effects of their chemotherapy regimen without reaping any benefit, which also delays the start of alternative treatment.

As such, there is a clear need to accurately identify non-responsive tumours as early as possible. To do this, I use a combination of MR imaging and mathematical models to create digital twins of patient tumours. These can be evolved in time to predict treatment outcome.

Qualifications

  • MSc Fluid Dynamics, University of Leeds
  • BSc (Hons) Mathematics, University of Glasgow