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Precision Medicine DTP - Large-Scale Data-Driven Lung Cancer Diagnostics using Time-Resolved Fluorescence Spectroscopy (TRFS)


Project Description

Background

Pulmonary nodules are common, often incidental, findings on chest CT scans. The investigation of pulmonary nodules is, however, time-consuming and often leads to protracted follow-up with ongoing radiological surveillance. Currently, there is a critical need for clinical calculators that can assess the risk of the nodule being malignant [1].

Recent advances in interventional pulmonology including the ability to navigate to nodules [2] and perform Time-Resolved Fluorescence Spectroscopy (TRFS) [3,4] may enable the immediate bed-side diagnosis of lung cancer and help in the stratification of patients. TRFS investigates the fluorescence (the emission of light) of a sample as a function of time when irradiated with light, and the team have collected over 30 Gigabytes of data which is growing at the rate of 3 Gigabytes per week.

We hypothesize that TRFS can be used to assess the malignancy of the nodule. We aim to develop state-of-the-art signal processing and machine learning tools for TFRS data to estimate key parameters from the raw signal and classify annotated clinical TRFS data obtained from cancerous/non-cancerous tissue samples.

Aims

We are interested in

1) robust statistical estimation of fluorescence decay rate, peak intensity etc. from noisy measurements,

2) multi-way analysis to extract fluorescence spectrum signatures associated with benign and malignant tissues,

3) data driven approaches including deep neural network to classify healthy and malignant tissues,

4) tackling possibly mislabelled data, repeated measurements, and spatial information,

5) learning from potentially multiple views, e.g., Raman spectroscopy, to complement information contained in TRFS

6) building real time algorithm to be used bedside for fast decision making.

Training Outcomes

The project trains the applicant in the field of medical informatics, AI and machine learning, and connects him/her to engineers, scientists, clinicians, and industry with the aim of growing a world-leading interdisciplinary research portfolio. The applicant will benefit from working with clinical collaborators specialised in disruptive optical technologies and medical device innovation (Dhaliwal), medical robotics and image processing (Khadem), and signal processing and machine learning (Seth), who are all ideally placed to support the career development and facilitate the project’s clinical pathways and impact. Moreover, the project offers the applicant an opportunity to collaborate with a leading interventional medical company (Boston Scientific) on clinical product development and testing.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2020

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

References

[1] Seth, S. et al. Assessing the utility of autofluorescene-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans. Scientific Reports 6, 31372; doi: 10.1038/srep31372 (2016).

[2] Seth, S. et al. Estimating Bacterial and Cellular Load in FCFM Imaging. Journal of Imaging 4(1) 11; https://doi.org/10.3390/jimaging4010011 (2018).

[3] Kufcsák et al. Time-resolved spectroscopy at 19,000 lines per second using a CMOS SPAD line array enables advanced biophotonics applications. Optical Express 25, 11103-11123 (2017)

[4] Ehrlich et al. Fibre-based sensing of physiological parameters with time-resolved single photon spectroscopy, Photon (2018)

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