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

Dale J. Waterhouse; Laura Privitera; John Anderson; Danail Stoyanov; Stefano Giuliani

doi:10.1117/1.JBO.28.9.094804

27 March 2023 Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning

Dale J. Waterhouse, Laura Privitera, John Anderson, Danail Stoyanov, Stefano Giuliani

Author Affiliations +

Journal of Biomedical Optics, Vol. 28, Issue 9, 094804 (March 2023). https://doi.org/10.1117/1.JBO.28.9.094804

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.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Dale J. Waterhouse, Laura Privitera, John Anderson, Danail Stoyanov, and Stefano Giuliani "Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning," Journal of Biomedical Optics 28(9), 094804 (27 March 2023). https://doi.org/10.1117/1.JBO.28.9.094804

Received: 6 October 2022; Accepted: 6 March 2023; Published: 27 March 2023

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