Raman spectroscopy is a rapid technique for the identification of cancers. Its coupling with a hypodermic needle provides a minimally invasive instrument with the potential to aid real time assessment of suspicious lesions in vivo and guide surgery. A fibre optic Raman needle probe was utilised in this study to evaluate the classification ability of the instrument as a diagnostic tool together with multivariate analysis, through measurements of tissues from different animal species as well as various different porcine tissue types. Cross validation was performed and preliminary classification accuracies were calculated as 100% for the identification of tissue type and 97.5% for the identification of animal species. A lymph node sample was also measured using the needle probe to assess the use of the technique for human tissue and hence its efficiency as a clinical instrument. This needle probe has been demonstrated to have the capabilities to classify tissue samples based on their biochemical components. The Raman needle probe also has the potential to act as a diagnostic and surgical tool to delineate cancerous from non-cancerous cells in real time, thus assisting complete removal of a tumour.
Pathologists find it notoriously difficult to provide both inter- and intra-observer agreement on a diagnosis of early gastrointestinal cancers. Vibrational spectroscopic approaches have shown their value in providing molecular compositional data from tissue samples and therefore enabling the identification of disease specific changes, when combined with multivariate techniques. Mid-infrared microscopic imaging is undergoing rapid developments in sources, detectors and spectrometers. Here we explore the use of high magnification FTIR for GI cancers and consider how the MINERVA (MId- to NEaR infrared spectroscopy for improVed medical diAgnostics) project, which is developing discrete frequency IR imaging tools will enable histopathologists to obtain rapid molecular images form unstained tissue sections.
Despite the demonstrated potential as an accurate cancer diagnostic tool, Raman spectroscopy (RS) is yet to be adopted
by the clinic for histopathology reviews. The Stratified Medicine through Advanced Raman Technologies (SMART)
consortium has begun to address some of the hurdles in its adoption for cancer diagnosis. These hurdles include
awareness and acceptance of the technology, practicality of integration into the histopathology workflow, data
reproducibility and availability of transferrable models. We have formed a consortium, in joint efforts, to develop
optimised protocols for tissue sample preparation, data collection and analysis. These protocols will be supported by
provision of suitable hardware and software tools to allow statistically sound classification models to be built and
transferred for use on different systems. In addition, we are building a validated gastrointestinal (GI) cancers model,
which can be trialled as part of the histopathology workflow at hospitals, and a classification tool. At the end of the
project, we aim to deliver a robust Raman based diagnostic platform to enable clinical researchers to stage cancer, define
tumour margin, build cancer diagnostic models and discover novel disease bio markers.
We evaluate the potential of a custom-built fiber-optic Raman probe, suitable for in vivo use, to differentiate between benign, metaplastic (Barrett's oesophagus), and neoplastic (dysplastic and malignant) oesophageal tissue ex vivo on short timescales. We measured 337 Raman spectra (λ ex =830 nm ; P ex =60 mW ; t=1 s ) using a confocal probe from fresh (298) and snap-frozen (39) oesophageal tissue collected during surgery or endoscopy from 28 patients. Spectra were correlated with histopathology and used to construct a multivariate classification model which was tested using leave one tissue site out cross-validation in order to evaluate the diagnostic accuracy of the probe system. The Raman probe system was able to differentiate, when tested with leave one site out cross-validation, between normal squamous oesophagus, Barrett's oesophagus and neoplasia with sensitivities of (838% to 6%) and specificities of (89% to 99%). Analysis of a two group model to differentiate Barrett's oesophagus and neoplasia demonstrated a sensitivity of 88% and a specificity of 87% for classification of neoplastic disease. This fiber-optic Raman system can provide rapid, objective, and accurate diagnosis of oesophageal pathology ex vivo. The confocal design of this probe enables superficial mucosal abnormalities (metaplasia and dysplasia) to be classified in clinically applicable timescales paving the way for an in vivo trial.
Multivariate classifiers (such as Linear Discriminant Analysis, Support Vector Machines etc) are known to be useful
tools for making diagnostic decisions based on spectroscopic data. However, robust techniques for assessing their
performance (e.g. by sensitivity and specificity) are vital if the application of these methods is to be successful in the
clinic. In this work the application of repeated cross-validation for estimating confidence intervals for sensitivity and
specificity of multivariate classifiers is presented. Furthermore, permutation testing is presented as a suitable technique
for estimating the probability of obtaining the observed sensitivity and specificity by chance. Both approaches are
demonstrated through their application to a Raman spectroscopic model of gastrointestinal cancer.
Raman spectroscopy is an inelastic scattering technique capable of probing the biochemical changes associated with
neoplastic progression in oesophageal tissue. Custom-built fibre-optic Raman probes could potentially provide
opportunities for in vivo endoscopic diagnosis of pre-cancerous oesophageal lesions and targeted early therapy.
However, prior to commencing a clinical trial convincing ex vivo work must demonstrate multi-operator, multi-centre
and multi-system reliability. We report spectral consistency between two operators who independently evaluated two
optically identical probes ex vivo. In addition, we demonstrate compatibility with high-definition white light endoscopes
and narrow band imaging systems highlighting the potential for future endoscopic multi-modality imaging in the
oesophagus.
Histopathology provides the gold standard assessment of colonoscopic biopsies. Infrared spectroscopy can potentially
map biochemical changes across a tissue section identifying disease. The purpose of this study was to determine if
infrared spectroscopy could classify different colorectal pathologies and to investigate biochemical composition.
Colonoscopic tissue biopsies were snap frozen at colonoscopy. 10 micron thick sections were mounted on CaF2 slides. 3-
D spectral datasets (2 spatial dimensions and one spectral) were measured from thawed specimens using a Perkin Elmer
infrared imaging system in transmission mode. Contiguous tissue sections stained with H&E were reviewed by a
specialist gastrointestinal pathologist for comparison. Tissue spectra from epithelial tissues were classified using
principal components fed linear discriminant analysis with leave one out cross validation. Reference spectra from
purchased biochemicals (Sigma-Aldrich) were measured. Ordinary least squares analysis estimated the relative
biochemical signal contribution from epithelial regions. Spectra from tissue epithelia measured from normal tissue,
hyperplastic polyps, adenomatous polyps, cancer and ulcerative colitis samples were classified with accuracies in excess
of 90%. Ordinary least squares analysis demonstrated a higher DNA to cytoplasm ratio in cancer compared to normal
tissue. FTIR spectra from epithelia can be used to classify colorectal pathologies with high accuracy. Ordinary least
squares analysis shows promise for extraction of useful biochemical information. These techniques could aid the
histopathologist and ultimately lead to automated histopathological processing.
Rapid Raman mapping has the potential to be used for automated histopathology diagnosis, providing an adjunct technique to histology diagnosis. The aim of this work is to evaluate the feasibility of automated and objective pathology classification of Raman maps using linear discriminant analysis. Raman maps of esophageal tissue sections are acquired. Principal component (PC)-fed linear discriminant analysis (LDA) is carried out using subsets of the Raman map data (6483 spectra). An overall (validated) training classification model performance of 97.7% (sensitivity 95.0 to 100% and specificity 98.6 to 100%) is obtained. The remainder of the map spectra (131,672 spectra) are projected onto the classification model resulting in Raman images, demonstrating good correlation with contiguous hematoxylin and eosin (HE) sections. Initial results suggest that LDA has the potential to automate pathology diagnosis of esophageal Raman images, but since the classification of test spectra is forced into existing training groups, further work is required to optimize the training model. A small pixel size is advantageous for developing the training datasets using mapping data, despite lengthy mapping times, due to additional morphological information gained, and could facilitate differentiation of further tissue groups, such as the basal cells/lamina propria, in the future, but larger pixels sizes (and faster mapping) may be more feasible for clinical application.
Ultra-low spatial resolution Raman (ULSRR) mapping using fibre probes has been performed on mammalian and human
tissues. This will provide an understanding of the potential for in vivo surveillance of the lining of organs using such a
technique and for identifying abnormal tissues such as residual tumours within a surgical field.
The aim of the study was to create Raman probe map images of excised oesophageal specimens following radical and
palliative oesophagectomy procedures. A reproducible mapping grid was placed over the excised tissue surface and
Raman mapping at 830nm performed at regular intervals to provide images of 200 pixels over the region of interest.
Principal component analysis was used to create pseudocolour score images of both porcine phantoms and a human
resected oesophagus.
A principal component fed linear discriminant (LD) classification model of 72 biopsy samples from 35 patients was
created using a novel single fibre Raman probe. A subset of the training dataset was used to populate a matrix of 200
pixels to simulate a Raman probe map. Spectra from the simulated map were then projected onto the LD model and a
pseudocolour LD pathology map created.
Delineation of clinically significant pathology groups was demonstrated therefore this study has shown the feasibility of
in vivo ULSRR for margin assessment using a Raman probe.
Raman spectroscopy has proved to be a highly sensitive tool for differentiating between normal, cancerous or pre-cancerous
tissues. To date, histological application of Raman mapping has been limited due to lengthy mapping times.
StreamlineTM Raman imaging is a novel mapping technique that has reduced total mapping times to a level that is
becoming clinically practicable. Raman Streamline mapping was carried out on a 20μm frozen section of an oesophageal
biopsy. A contiguous 7μm section was stained with haematoxylin and eosin (H&E) with histpathology analysed by a
pathologist. The step size and acquisition times were varied and the resulting spectra, principal component score maps
and loads were compared. The signal to noise for the raw spectra and a relative 'signal to noise' of the principal
component loads were determined. The Streamline mapping technique was also compared to traditional point Raman
mapping. The principal component loads were similar despite varying the acquisition time and number of spectra, with
the fifth load used for comparison of the noise levels. Gross biochemical information was extracted showing good
correlation with the H&E section even for short overall mapping times as low as 30-90 minutes for a biopsy ~2mm in
diameter (0.5s acquisition time per 25.3μm Raman pixel). Streamline mapping was of the order of 3-7 times faster than
traditional point mapping with the greatest improvement made for high resolution maps. Further optimization of the
system is still possible which will reduce this mapping time further making implementation in a clinical environment a
future possibility.
There is no universally accepted grading system for the classification of Ductal Carcinoma in Situ (DCIS) although the diagnosis of DCIS has increased (2-20%) with screening mammography. (1) At present there are more than six different classifications and grading systems. Infrared spectroscopy is a non-invasive, rapid and specific technique used to analyse biological tissue. Spectral analysis of the chemical fingerprint within the duct would reveal spectral differences according to absorption and transmission characteristics of different grades of DCIS. An existing model of histopathological classification which is locally accepted has been tested and evaluated in this study. 19 ducts from different biopsy specimens were marked on H&E stained sections by two breast pathologists, according to the locally accepted classification. A consecutive unstained 20μm section was subjected to infrared analysis (Perkin-Elmer). Principal component analysis was undertaken using Matlab. Pseudocolor maps of the principal component scores delineated morphological features of the ducts. Peaks in the corresponding principal component loads were identified to enable understanding of the biochemical changes associated with different grades of DCIS. A 4-group cross-validated classification model was developed using multivariate statistical analysis with selected spectra from different grades of DCIS. The classification model demonstrated good separation of the different grades of the DCIS with a sensitivity of 80-99% and specificity of 92-98%. Infrared spectroscopy is a highly sensitive and specific technique for the demonstration of biochemical changes within the proliferative duct. It could aid in reclassifying the grades of DCIS in accordance with the biochemical and morphological changes that occur with proliferation. Infrared spectroscopy has potential as an added tool for the pathologist to diagnose in vitro.
Advances in technologies have brought us closer to routine spectroscopic diagnosis of early malignant disease. However, there is still a poor understanding of the carcinogenesis process. For example it is not known whether many cancers follow a logical sequence from dysplasia, to carcinoma in situ, to invasion. Biochemical tissue changes, triggered by genetic mutations, precede morphological and structural changes. These can be probed using Raman or FTIR microspectroscopy and the spectra analysed for biochemical constituents. Local microscopic distribution of various constituents can then be visualised. Raman mapping has been performed on a number of tissues including oesophagus, breast, bladder and prostate. The biochemical constituents have been calculated at each point using basis spectra and least squares analysis. The residual of the least squares fit indicates any unfit spectral components. The biochemical distribution will be compared with the defined histopathological boundaries. The distribution of nucleic acids, glycogen, actin, collagen I, III, IV, lipids and others appear to follow expected patterns.
Atypical lesions of the breast have potential to turn malignant. The diagnosis of these lesions has increased considerably with screening mammography. A good understanding of their progression to invasive cancer is yet to be proved. Using Raman spectroscopy to study their chemical finger printing at different stages of proliferation a clear picture of whether a progression exists between lesions could be made. At present there is no clear recognition of the biochemical changes that distinguish between the different proliferative lesions of the breast. Our aim is to understand these changes through Raman mapping studies.
Raman spectroscopy is a highly sensitive and specific technique for demonstration of biochemical changes in different atypical proliferative lesions of the breast. The technique could be used to classify the different grades and analyse progression of pathology in the proliferative lesions of the breast.
Breast pathologists carefully marked 50 ducts and classified the different pathology on H and E sections from biopsy samples. Raman spectra were measured, using a Renishaw Raman Spectrometer, on a 20-micron thick consecutive frozen section. Principal component analysis was undertaken using Matlab. Pseudocolor maps of the principal components scores have been generated. The peaks of the corresponding loads were identified enabling visualisation of the biochemical changes associated with proliferative lesions. Proliferative lesions of the duct were grouped according to the existing standard pathological classification and formed four major groups-HUT, ADH, DCIS and IDC.
Spectra of biochemical constituents were fitted to mean spectra from selected regions, taken from maps of each pathology, to identify the relative concentration of the constituents.
The study gave an insight into chemical make up of the ducts in each pathology group and showed similar results to earlier studies in progression but no clear-cut demarcation or continuum of the proliferative disease.
Abstract: laboratory Raman spectroscopy was performed on 59 lymph node sections from breast cancer patients, demonstrating 91% sensitivity and 93% specificity for the correct classification of positive node spectra in a model.
The incidence of both prostate and bladder cancer is high; prostate cancer being the most frequently diagnosed non-cutaneous cancer affecting Western men. At present the gold standard for diagnosis of pathologies within the bladder and the prostate gland is by means of histological examination of biopsies. This is a subjective means of examining tissue and has an element of both inter and intra-observer variability. A large number of specimens have been collected and analysed using both a NIR-Raman spectrometer and histopathology with H&E staining. Multivariate spectral prediction models have been constructed and tested. An evaluation of misclassification cost models and the use of cancer staging data to train the models has been made.
A preliminary investigation of tissue autofluorescence and uptake of the photosensitiser protoporphyrin IX (PpIX) has been investigated using multiphoton imaging of excised oesophageal tissue. The technique has indicated that changes in collagen structure may be a potential marker for high-grade dysplasia and adenocarcinoma. Changes in the localisation of PpIX with the development of malignancy in oesophageal tissue were also visualised.
Raman Spectroscopy is an optical diagnostic technique applied in this study to characterise breast tissue by biochemical signature spectra. In cross-validated results, Raman Spectroscopy identifies invasive breast carcinoma with 75 - 97% agreement with Histology opinion. Axillary lymph nodes from patients with breast carcinoma were mapped with confocal Raman Spectroscopy and colour-weighted principal component analysis (PCA) images were used to identify local biochemical features and correlate these with parallel section slides analysed with routine histology.
The potential of optical spectroscopic detection for the early detection of malignancy is becoming more widely accepted. Many studies have demonstrated the potential of Raman spectroscopy for the identification and classification of malignant changes, with valuable insight into possible applications. However much of the work has been undertaken without clear recognition of the biochemical changes that distinguish between the different stages of malignant progression. Raman mapping experiments have been undertaken in an attempt to increase understanding of the biochemical changes involved in the development of oesophageal malignancy. Pseudocolour maps of the significant principal component scores have been generated. The peaks of the corresponding principal component loads have been identified, by comparison with constituent spectra and published spectral identities. Investigating the measured biochemical changes in greater detail, further demonstrates the potential of near infrared Raman spectroscopy for the analysis of biological tissue.
Optical spectroscopic detection of early malignancy is becoming more widely accepted in academic circles, however much work remains to be done before full recognition by the medical community is achieved. The majority of published studies to date have demonstrated the potential of optical diagnosis techniques using small sample numbers in a selected patient population. Many are completed without a solid understanding of the shortcomings of histopathology, the 'gold standard' for cancer detection. For the development of a new technique to improve diagnosis it is vital that more rigorous protocols are employed in large-scale clinical trials. The prospect of utilizing NIR-Raman spectroscopy for the analysis of neoplastic gastrointestinal tissue has been thoroughly explored by a multi-disciplinary team including surgeons, pathologists, and spectroscopists. This study demonstrates the need for rigorous experimental protocols and histopathological analysis by a panel of expert pathologists. Measurements of tissue specimens from nine different pathological groups describing the full spectrum of disease in the oesophagus have been made. Only homogeneous samples with consensus pathology opinion were used to construct a training data set of Raman spectra. Models were constructed using multivariate analysis techniques and tested using cross-validation.
The prospect of utilising NIR-Raman spectroscopy for analysis of gastro-intestinal (GI) tissue has been explored both with snap-frozen and formalin fixed samples. In the oesophagus large sample numbers have been employed and the full spectrum of pathology has been studied. Multivariate analysis techniques have been employed to optimally separate the groups and spectral diagnostic models have been constructed and evaluated by employing cross-validation testing. Sensitivities have been shown to vary between 73 and 100 percent and specificities between 91 and 100 percent, depending on pathology group and tissue type.
The incidence of oesophageal and laryngeal cancer has risen over past decades. The early detection of disease is vital for improved prognosis. The current gold standard method of tissue diagnosis is histopathology, which is invasive, costly and somewhat subjective. Raman spectroscopy however has potential for the non-invasive, early, in-vivo diagnosis of the biochemical changes associated with malignancy. Good quality Raman spectra have been measured in vitro using oesophageal and laryngeal tissue. Multivariate analysis has been implemented using principal component and linear discriminant analysis techniques. Sensitivity and specificity of more than 80% has been achieved for the discrimination of dysplasia and cancer, for both oesophageal and laryngeal tissue. On comparison with histopathology these results are seen to be an improvement, since pathology lacks sensitivity and specificity due to the subjective nature of the diagnosis. Thus illustrating that excellent discrimination between normal, dyspalstic and cancerous tissue can be achieved using Raman Spectroscopy.
Early detection of cancer is important because of the improved survival rates when the cancer is treated early. We study the application of NIR Raman spectroscopy for detection of dysplasia because this technique is sensitive to the small changes in molecular invasive in vivo detection using fiber-optic probes. The result of an in vitro study to detect neoplastic progress of esophageal Barrett's esophageal tissue will be presented. Using multivariate statistics, we developed three different linear discriminant analysis classification models to predict tissue type on the basis of the measured spectrum. Spectra of normal, metaplastic and dysplasia tissue could be discriminated with an accuracy of up to 88 percent. Therefore Raman spectroscopy seems to be a very suitable technique to detect dysplasia in Barrett's esophageal tissue.
The incidence of laryngeal cancer has risen progressively over the last 25 years. Early diagnosis and treatment of premalignant lesions of the larynx is vital to prevent progression to invasive squamous cell carcinoma. In the larynx, it has long been recognized that histological evidence of maturation abnormality is associated with a higher risk of transformation to malignancy. Currently, it is extremely difficult if not impossible for the clinician to ascertain the level of abnormality present without removing a biopsy sample and sending it for histopathological analysis. Inherent risks with this technique include damage to vocal chords and loss of speech quality as well as possible selection of unrepresentative biopsy samples. Raman spectroscopy, incorporated into an endoscopic system, has the potential to provide a real-time, non-invasive diagnostic technique able to detect biochemical changes that accompany abnormal pathology. Likely outcomes would be improved biopsy targeting and patient management by providing immediate result of tissue pathology. This paper demonstrates the capacity of near IR Raman spectroscopy combine with statistical data analysis techniques to discriminate between normal, dysplastic and cancerous laryngeal tissue.
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.