Quantum Cascade Laser (QCL) spectroscopic imaging is a novel technique with many potential applications to
histopathology. Like traditional Fourier Transform Infrared (FT-IR) imaging, QCL spectroscopic imaging derives
biochemical data coupled to the spatial information of a tissue sample, and can be used to improve the diagnostic and
prognostic value of assessment of a tissue biopsy. This technique also offers advantages over traditional FT-IR imaging,
specifically the capacity for discrete frequency and real-time imaging. In this work we present applications of QCL
spectroscopic imaging to tissue samples, including discrete frequency imaging, to compare with FT-IR and its potential
value to pathology.
Current methods for cancer detection rely on clinical stains, often using immunohistochemistry techniques. Pathologists then evaluate the stained tissue in order to determine cancer stage treatment options. These methods are commonly used, however they are non-quantitative and it is difficult to control for staining quality. In this paper, we propose the use of mid-infrared spectroscopic imaging to classify tissue types in tumor biopsy samples. Our goal is to augment the data available to pathologists by providing them with quantitative chemical information to aid diagnostic activities in clinical and research activities related to breast cancer.
Hari Sreedhar, Mamta Pant, Nemencio Ronquillo, Bennett Davidson, Peter Nguyen, Rohini Chennuri, Jacqueline Choi, Joaquin Herrera, Ana Hinojosa, Ming Jin, Andre Kajdacsy-Balla, Grace Guzman, Michael Walsh
Hepatocellular carcinoma (HCC) is the most common form of primary hepatic carcinoma. HCC ranks the fourth most
prevalent malignant tumor and the third leading cause of cancer related death in the world. Hepatocellular carcinoma
develops in the context of chronic liver disease and its evolution is characterized by progression through intermediate
stages to advanced disease and possibly even death. The primary sequence of hepatocarcinogenesis includes the
development of cirrhosis, followed by dysplasia, and hepatocellular carcinoma.1 We addressed the utility of Fourier
Transform Infrared (FT-IR) spectroscopic imaging, both as a diagnostic tool of the different stages of the disease and to
gain insight into the biochemical process associated with disease progression. Tissue microarrays were obtained from the
University of Illinois at Chicago tissue bank consisting of liver explants from 12 transplant patients. Tissue core
biopsies were obtained from each explant targeting regions of normal, liver cell dysplasia including large cell change
and small cell change, and hepatocellular carcinoma. We obtained FT-IR images of these tissues using a modified FT-IR
system with high definition capabilities. Firstly, a supervised spectral classifier was built to discriminate between normal
and cancerous hepatocytes. Secondly, an expanded classifier was built to discriminate small cell and large cell changes
in liver disease. With the emerging advances in FT-IR instrumentation and computation there is a strong drive to develop
this technology as a powerful adjunct to current histopathology approaches to improve disease diagnosis and prognosis.
Vishal Varma, Samuel Ohlander, Peter Nguyen, Christopher Vendryes, Sujeeth Parthiban, Blake Hamilton, M. Chad Wallis, Andre Kajdacsy-Balla, Blake Hannaford, Thomas Lendvay, James Hotaling, Michael Walsh
Fourier Transform Infrared (FT-IR) spectroscopic imaging can allow for the rapid imaging of tissue biochemistry in a
label-free and non-perturbing fashion. With the rapid adoption of new minimally invasive surgery (MIS) technologies
over the last 20 years, adequate skill to safely and effectively use these technologies may not be achieved and risk of
undue physical pressure being placed on tissues is a concern. Previous work has demonstrated that a number of
histological stains can detect tissue damage, however, this process requires the initiation and progression of a signaling
cascade that results in the epitope of interest being expressed. We proposed to identify the early biochemical markers
associated with physical tissue damage from applied forces, thus not requiring transcriptional and translational protein
synthesis as traditional immunohistochemistry does. To demonstrate that FT-IR can measure biochemical changes in
tissues that have undergone physical force, we took ex-vivo lamb’s liver that had been freshly excised and applied
varying levels of physical pressure (0kPa to 30kPa). Tissues were then formalin-fixed, paraffin-embedded, and sectioned
on to glass for H and E staining to identify damage and on to an IR slide for FT-IR imaging. Regions of interest containing
hepatocytes were identified and average FT-IR spectra were extracted from the damaged and undamaged livers. FT-IR
spectra showed clear biochemical changes associated with tissue damage. In addition, chemical changes could be
observed proceeding histological changes observed when using conventional staining approaches.
Fourier transform infrared (FT-IR) spectroscopic imaging is a powerful tool to obtain chemical information from
images of heterogeneous, chemically diverse samples. Significant advances in instrumentation and data processing
in the recent past have led to improved instrument design and relatively widespread use of FT-IR imaging, in a
variety of systems ranging from biomedical tissue to polymer composites. Various techniques for improving signal
to noise ratio (SNR), data collection time and spatial resolution have been proposed previously. In this paper
we present an integrated framework that addresses all these factors comprehensively. We utilize the low-rank
nature of the data and model the instrument point spread function to denoise data, and then simultaneously
deblurr and estimate unknown information from images, using a Bayesian variational approach. We show that
more spatial detail and improved image quality can be obtained using the proposed framework. The proposed
technique is validated through experiments on a standard USAF target and on prostate tissue specimens.
Histopathology is the gold standard for disease diagnosis; however it is subject to a number of limitations. Fourier
Transform infrared (FT-IR) spectroscopic imaging can be used to derive chemical images from tissues based on their
inherent molecular composition, thereby eliminating the use of dyes and stains. FT-IR imaging represents a novel,
emerging approach that can allow for accurate cell type identification which is competitive with conventional
histopathological approaches and may alleviate a number of the limitations associated with current techniques.
Traditionally, this approach has involved in a loss of image detail due to the sub-optimal and, compared to optical
microscopy, coarse pixel size in instruments. Recent advances in high-resolution FT-IR imaging have allowed for the
identification and chemical characterization of cell types and tissue structures which were previously not discernible.
Here we report on the visualization of several histologic details using high-resolution IR imaging that may be critical for
tissue histology and disease diagnosis.
Histologic diagnosis is the gold standard for evaluating the presence and severity of most cancers. Unfortunately, the
manual nature of histologic recognition leads to low throughput and errors. Here, we report on the evaluation of an
automated means to accurate histologic recognition using mid-infrared spectroscopic imaging. The method does not
need dyes or probes and dispenses with human input but relies on computational approaches to provide decisions.
Hence, the results must be rigorously validated. We present here a validation of two-class models for pixel-level
histologic segmentation and pathologic classification by spatial polling for breast carcinoma. We also discuss
optimization of spectral resolution and instrumentation for clinical translation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.