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

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Molecular. Genetics & Pop Health


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.

Recent advances in interventional pulmonology including the ability to navigate to nodules and perform Time-Resolved Fluorescence Spectroscopy (TRFS) 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.


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.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
MR/N013166/1 30/09/2016 29/09/2025
2442971 Studentship MR/N013166/1 31/08/2020 29/02/2024