Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples

Abstract

Fourier transform infrared (FT-IR) spectroscopic imaging has been widely tested as a tool for stainless digital histology of biomedical specimens, including for the identification of infiltration and fibrosis in endomyocardial biopsy samples to assess transplant rejection. A major barrier in clinical translation has been the slow speed of imaging. To address this need, we tested and report here the viability of using high speed discrete frequency infrared (DFIR) imaging to obtain stain-free biochemical imaging in cardiovascular samples collected from patients. Images obtained by this method were classified with high accuracy by a Bayesian classification algorithm trained on FT-IR imaging data as well as on DFIR data. A single spectral feature correlated with instances of fibrosis, as identified by the pathologist, highlights the advantage of the DFIR imaging approach for rapid detection. The speed of digital pathologic recognition was at least 16 times faster than the fastest FT-IR imaging instrument. These results indicate that a fast, on-site identification of fibrosis using IR imaging has potential for real time assistance during surgeries. Further, the work describes development and applications of supervised classifiers on DFIR imaging data, comparing classifiers developed on FT-IR and DFIR imaging modalities and identifying specific spectral features for accurate identification of fibrosis. This addresses a topic of much debate on the use of training data and cross-modality validity of IR measurements. Together, the work is a step toward addressing a clinical diagnostic need at acquisition time scales that make IR imaging technology practical for medical use.

Infrared spectroscopic imaging: Label-free biochemical analysis of stroma and tissue fibrosis

“…Synchrotrons offer high-intensity broadband sources for FT-IR imaging, but are rendered impractical for clinical applications by size constraints (Nasse et al, 2011). Quantum cascade lasers (QCL) provide a new high-intensity source for IR imaging, and these QCL-IR systems have unique features as compared to FT-IR because of their capacity for discrete-frequency imaging (Bird and Rowlette, 2017; Pilling et al, 2017; Sreedhar et al, 2016; Tiwari et al, 2016). …”

“…One such tissue feature that has recently attracted interest in the IR imaging community is areas of stroma or fibrosis (Bird and Rowlette, 2017; Pilling et al, 2017; Sreedhar et al, 2016; Tiwari et al, 2016). Early IR investigations of tissue samples tended to focus on cancers and other disease processes that centered on epithelia and parenchyma.…”

“…This ubiquitous protein-based structural element of tissue would naturally contribute greatly to the IR absorption signatures of connective tissue regions. Differences in the collagen composition (and collagen types) would be expected to contribute to differences in spectroscopic characteristics, which would translate to pathology-specific differences between areas of stroma and fibrosis (Belbachir et al, 2009; Bird and Rowlette, 2017; Tiwari et al, 2016). …”

“…In a similar capacity, applications of IR imaging toward assessing cardiac transplant rejection (Fig. 2B) have also used the segmentation of fibrosis as part of their approach (Tiwari et al, 2016). The distribution of collagen deposition could also be visualized in cardiac tissue using the 1338cm −1 wavenumber (Cheheltani et al, 2012).…”

“…The same fibrotic regions are identified with QCL-IR discrete frequency imaging by using (III) an image of peak height ratio of 1232 cm −1 to 1336 cm −1 and (IV) a k-Means clustering image. B) Heart transplant (Tiwari et al, 2016) tissue sections with abundant fibrosis showing classification of cardiac tissue into myocardium (green or blue) or fibrosis (red) on the basis of FT-IR spectral data (I, V, IX), QCL-IR discrete frequency spectral data (II, VI, X), and only the peak height ratio of 1236 cm −1 to 1542 cm −1 from QCL IR (III, VII, XI). Corresponding H&E stained images with red arrows identifying prominent areas of fibrosis (IV, VIII, XII).…”