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Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning

Waterhouse, Dale J; Privitera, Laura; Anderson, John; Stoyanov, Danail; Giuliani, Stefano; (2023) Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning. Journal of Biomedical Optics , 28 (9) , Article 094804. 10.1117/1.JBO.28.9.094804. Green open access

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Abstract

SIGNIFICANCE: Fluorescence-guided surgery (FGS) provides specific real-time visualization of tumors, but intensity-based measurement of fluorescence is prone to errors. Multispectral imaging (MSI) in the short-wave infrared (SWIR) has the potential to improve tumor delineation by enabling machine-learning classification of pixels based on their spectral characteristics. AIM: Determine whether MSI can be applied to FGS and combined with machine learning to provide a robust method for tumor visualization. APPROACH: A multispectral SWIR fluorescence imaging device capable of collecting data from six spectral filters was constructed and deployed on neuroblastoma (NB) subcutaneous xenografts ( n = 6 ) after the injection of a NB-specific NIR-I fluorescent probe (Dinutuximab-IRDye800). We constructed image cubes representing fluorescence collected from ∼ 850 to 1450 nm and compared the performance of seven learning-based methods for pixel-by-pixel classification, including linear discriminant analysis, k -nearest neighbor classification, and a neural network. RESULTS: The spectra of tumor and non-tumor tissue were subtly different and conserved between individuals. In classification, a combine principal component analysis and k -nearest-neighbor approach with area under curve normalization performed best, achieving 97.5% per-pixel classification accuracy (97.1%, 93.5%, and 99.2% for tumor, non-tumor tissue and background, respectively). CONCLUSIONS: The development of dozens of new imaging agents provides a timely opportunity for multispectral SWIR imaging to revolutionize next-generation FGS.

Type: Article
Title: Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/1.JBO.28.9.094804
Publisher version: https://doi.org/10.1117/1.JBO.28.9.094804
Language: English
Additional information: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Keywords: cancer, fluorescence-guided surgery, machine-learning, multispectral, neuroblastoma, short-wave infrared, Humans, Neoplasms, Fluorescent Dyes, Neural Networks, Computer, Optical Imaging, Machine Learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Biology and Cancer Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10167765
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