Digital Histopathology by Infrared Spectroscopic Imaging

Abstract

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 16 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Phasor Representation Approach for Rapid Exploratory Analysis of Large Infrared Spectroscopic Imaging Data Sets

“…We also caution that the accuracies reported here are not the accuracies for an IR imaging method applied to tissue but simply a test of our approach to understanding spectra against known values. A study that definitively provides diagnostic analyses would have to demonstrate the same on independent samples, samples that are of sufficient diversity and more reflective of clinical practice . Finally, other supervised classification methods like support vector machine or logistic regression can also be applied to phasor quantities.…”

“…A study that definitively provides diagnostic analyses would have to demonstrate the same on independent samples, samples that are of sufficient diversity 25 and more reflective of clinical practice. 38 Finally, other supervised classification methods like support vector machine or logistic regression can also be applied to phasor quantities. The primary motivation for the current study was to achieve fast, automated dimensionality reduction of high-dimensional IR images.…”

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

“…QCL-based IR spectroscopy is mostly adopted in transmission geometry and offers enhanced SNR compared to both interferometer- and spectrometer-based techniques. A summary of the figure of merits of various IR spectroscopy techniques adopted in the analysis of blood derivatives is provided in Table . − …”

Rapid and Label-Free Histopathology of Oral Lesions Using Deep Learning Applied to Optical and Infrared Spectroscopic Imaging Data

“…In this study, we examined the use of label-free infrared (IR) spectroscopic imaging to quantify spatial variations of vibrational modes in oral tissue, which can then be related to histological classifications through deep learning. IR imaging is a powerful tool for studying the spatial variation of the biochemical and molecular structure of tissues without the need for external dyes or reagents that detect molecular patterns [ 15 , 16 ]. The bulk of prior IR spectroscopic imaging data, including for oral cancer [ 17 , 18 , 19 ], has previously been acquired using Fourier transform infrared (FT-IR) microscopy that provides full IR spectral data for all pixels imaged [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”