Pterygium and Ocular Surface Squamous Neoplasia: Optical Biopsy Using a Novel Autofluorescence Multispectral Imaging Technique

Abbas Habibalahi; Alexandra Allende; Jesse Michael; Ayad G Anwer; Jared Campbell; Saabah B Mahbub; Chandra Bala; Minas T Coroneo; Ewa M Goldys

doi:10.3390/cancers14061591

Pterygium and Ocular Surface Squamous Neoplasia: Optical Biopsy Using a Novel Autofluorescence Multispectral Imaging Technique

Cancers (Basel). 2022 Mar 21;14(6):1591. doi: 10.3390/cancers14061591.

Authors

Abbas Habibalahi^{1

2}, Alexandra Allende^{3

4}, Jesse Michael^{1

2}, Ayad G Anwer^{1

2}, Jared Campbell^{1

2}, Saabah B Mahbub^{1

2}, Chandra Bala⁵, Minas T Coroneo⁶, Ewa M Goldys^{1

2}

Affiliations

¹ ARC Centre of Excellence for Nanoscale Biophotonics, University of New South Wales, Sydney, NSW 2032, Australia.
² Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2032, Australia.
³ Douglass Hanly Moir Pathology, Macquarie Park, NSW 2113, Australia.
⁴ Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia.
⁵ Department of Ophthalmology, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia.
⁶ Department of Ophthalmology, University of New South Wales at Prince of Wales Hospital, High Street, Randwick, NSW 2031, Australia.

Abstract

In this study, differentiation of pterygium vs. ocular surface squamous neoplasia based on multispectral autofluorescence imaging technique was investigated. Fifty (N = 50) patients with histopathological diagnosis of pterygium (PTG) and/or ocular surface squamous neoplasia (OSSN) were recruited. Fixed unstained biopsy specimens were imaged by multispectral microscopy. Tissue autofluorescence images were obtained with a custom-built fluorescent microscope with 59 spectral channels, each with specific excitation and emission wavelength ranges, suitable for the most abundant tissue fluorophores such as elastin, flavins, porphyrin, and lipofuscin. Images were analyzed using a new classification framework called fused-classification, designed to minimize interpatient variability, as an established support vector machine learning method. Normal, PTG, and OSSN regions were automatically detected and delineated, with accuracy evaluated against expert assessment by a specialist in OSSN pathology. Signals from spectral channels yielding signals from elastin, flavins, porphyrin, and lipofuscin were significantly different between regions classified as normal, PTG, and OSSN (p < 0.01). Differential diagnosis of PTG/OSSN and normal tissue had accuracy, sensitivity, and specificity of 88 ± 6%, 84 ± 10% and 91 ± 6%, respectively. Our automated diagnostic method generated maps of the reasonably well circumscribed normal/PTG and OSSN interface. PTG and OSSN margins identified by our automated analysis were in close agreement with the margins found in the H&E sections. Such a map can be rapidly generated on a real time basis and potentially used for intraoperative assessment.

Keywords: autofluorescence; boundary detection; machine learning; ocular surface squamous neoplasia; pterygium.