Title: Towards in-vivo in-situ early lung cancer detection using fluorescence lifetime imaging endomicroscopy: a preliminary study on data-driven characterisation of lung cancer using label-free autofluorescence lifetime images on unstained lung tissue
Funder: Cancer Research UK

Abstract: Background Endoscopic fibre-based fluorescence lifetime imaging microscopy (FLIM) can provide real-time microscopic imaging in the distal lung, with the potential for in-vivo lung cancer discrimination using autofluorescence lifetime contrast between normal/cancerous tissue. However, a fundamental requirement for advanced differentiation, such as diagnostic capability, is the ground truth, e.g., histology images, which are never available for real-time in-vivo in-situ tissue characterisation. This barrier has significantly limited FLIM from in-vivo detection of early lung cancer. To enable our ultimate objective, rapid and accurate early lung cancer characterisation in-vivo with fibre-based FLIM, this project will focus on FLIM-based qualitative and quantitative characterisation of unstained lung tissue. Aims This preliminary project aims to: (1) build a first-of-its-kind large-scale dataset of full-spectral FLIM (FS-FLIM) images on unstained lung tissue covering major subtypes of non-small cell lung cancer (NSCLC), including squamous cell carcinoma, adenocarcinoma, and large cell carcinoma; (2) develop novel deep learning (DL) technologies for real-time synthesis of histology images from the images at arbitrary wavelengths, allowing qualitative characterisation of lung cancer during FLIM imaging; (3) extract cellular-level FLIM fingerprints of lung cancer. Methods We will first expand our existing dataset collected from pairs of unstained normal/cancerous lung tissue using a custom-built FS-FLIM system. Subsequently, the correlation between real histology and FS-FLIM images will be established using our recently-developed co-registration approach, as the ground truth for supervised learning. Later, we will develop novel DL models by improving existing paired image-to-image translation to enable wavelength-independent synthesis. Meanwhile, various cells in the tumour microenvironment will be characterised by absolute lifetime with the correlation of annotated histological findings to the FLIM imaging signatures. How the results of this research will be used The outcomes will allow an insightful understanding of FLIM signatures of NSCLC subtypes with different degrees of differentiation, leading to a greater interpretation of real-time FLIM imaging for advanced discrimination of NSCLC subtypes. This will form the basis of our further investigations on lung biopsies with the system, underpinning future development plans. The data will be utmostly facilitated through a grand challenge for the synthesis of histology images to establish new collaborations with worldwide researchers and engineers for optimal dissemination. Patenting and commercialisation of the technologies will be evaluated during and after the project, with the support from the Healthcare Technology Accelerator Facility at the University of Edinburgh, Edinburgh Innovations and through my participation in the Data-Driven Entrepreneurship Venture Builder Incubator, partnered with Cancer Research UK.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS