2.1.

Raman Instrumentation

The instrumentation (Renishaw, $\mathrm{InVia}+\text{Plus}$, Confocal Microspectrometer) utilized to measure the Raman spectra of tissues mainly consists of a 785 nm diode laser (maximum output: 300 mW, Renishaw Inc., New Mills, UK), a transmissive imaging spectrograph with an NIR-optimized, back-illuminated, deep-depletion charge-coupled device (CCD) detector, and a single $1200\text{lines}/\mathrm{mm}$ dispersion grating. The original Raman spectra of tissues can be displayed on the computer screen in real time and saved for further analysis. The spectral resolution of the system is $1{\mathrm{cm}}^{-1}$ and the laser power used in this study is about 10%. The system acquires each spectrum over the wavenumber range of 700 to $1800{\mathrm{cm}}^{-1}$, which contains the main vibrational signatures of proteins, lipids, carbohydrates, and nucleic acids. The laser power on the surface of the tissue during signal acquisition is $\sim 10\mathrm{mW}$, and the diameter of the laser spot at the focus point is about 5 μm. The spectra are collected in backscattering geometry using a microscope equipped with a Leica $20\times \text{objective}$. In order to obtain a better signal-to-noise ratio, the measurements of all the Raman spectra are accumulated five times within the 5 s of exposure time. The peak frequency of each spectrum and the rapid checking of instrumental performance are calibrated with the silicon phonon line at $520{\mathrm{cm}}^{-1}$. A personal computer controls the Raman spectrometer with a custom-designed software package (Raman Wire 3.2) that triggers on-line data acquisition and analysis.

2.2.

Gastric Tissue Samples

In this study, the samples of ex vivo gastric tissues are collected from 48 gastric patients (29 men and 19 women, with an average age of 61 years). Some of them have undergone the endoscopy biopsies ($n=37$) for clinically suspicious lesions, and the others pathologically diagnosed with gastric adenocarcinoma have received gastrectomy ($n=11$) in the Department of Gastroenterology, Zhujiang Hospital, Guangzhou, China. All of those patients have signed an informed consent form to permit the investigative utilization of the tissues. After biopsies or surgical resections, each one of the fresh tissue samples is immediately divided into two portions, and it is ensured that every portion keeps its histological structure complete; one portion is submitted for histopathologic classification and the other is placed in plastic tubes without any treatment and stored on ice until it is sent to the laboratory for Raman measurements. A senior gastrointestinal pathologist, who is unaware of the features of Raman spectra of various tissue types, carries out the analysis of histopathology. The results show that among the 48 relative homogeneous gastric tissue samples selected from a large series of tissue samples with clearly defined pathologies, 22 tissue samples are normal, 13 are adenomatous polyps, and 13 are adenocarcinomas. Each tissue sample is from a different individual; other pathologic tissues or mixture tissue types are excluded in this study. We measure Raman spectra of all tissue samples in air at room temperature immediately after resection. Each tissue sample (approximately $5\times 4\times 3\mathrm{mm}$ in size) is placed on a glass slide and measured at different regions. After the experiment, each tissue sample is sent to hospital and the tissue diagnosis is confirmed histopathologically. In order to further assess the variability of intrasample, two to three regions of each tissue sample are chosen for multiple measurements ($n>8$) at random. The random selection is accomplished at the xyz three-dimensional motorized and computer-controlled sample stage. The average spectrum of eight repeated Raman measurements on the same tissue region of each tissue sample is used for spectral analysis. The spectra are examined visually for any obvious outliers that deviate gravely from standard deviation (SD) or are caused by instrumental or man-made measurement errors, and then the obviously aberrant spectra are excluded from analysis. Finally, a total of 105 Raman spectra obtained from 48 relatively homogeneous gastric tissue samples, in which 45 Raman spectra are from normal tissues (i.e., confirmed by histopathology), 27 from premalignant lesions (i.e., adenomatous polyp as confirmed by histopathology), and 33 from cancer tissues (i.e., adenocarcinoma as confirmed by histopathology), corresponding to two or three average spectra per tissue sample, are eventually applied to the differentiation of gastric tissues in this study.

2.3.

Data Preprocessing

The original spectra from gastric tissues represent a combination of strong autofluorescence signal, weak Raman signal, and noise. A specially designed software package (Raman Wire 3.2) is used to preprocess these original spectra, including the calibration of wavelength, the calibration of the systematic spectral response, the elimination of cosmic radiation, and the subtraction of the CCD dark-noise. The Raman spectral signal whose noise has been smoothed is collected and removed by a background-correction algorithm named adaptive iteratively reweighted penalized least squares (airPLS), which is implemented in R language.^35,36 Then, each Raman spectrum whose autofluorescence background has been subtracted is also normalized with respect to the total area under the Raman curve from 700 to $1800{\mathrm{cm}}^{-1}$. Thus, the systematic deviation can be minimized and the comparison of the spectral shapes and the relative Raman peak intensities among different types of gastric tissues can be achieved.

2.4.

Multivariate Statistical Analysis

In this study, PCA, LDA, and NBC techniques are performed on all spectra to elucidate the important spectral features of each tissue class and to compare different tissue classes against each other for gastric tissues identification. PCA is usually employed to extract a set of orthogonal principal components (PCs) to simplify the analysis of data. First, PCA is utilized to reduce the dimensionality of the Raman spectral dataset in our study while still retaining the most important information for tissue diagnosis and characterization. Based on standardized data matrices of Raman spectra, PCA can generate the PCs scores. Each loading vector is related to the original spectrum by PCs score, which represents the weight of that particular component against the basis spectrum.^10,24 A supervised analytical technique named LDA could develop diagnostic algorithms using the PCs scores. LDA determines the discriminant function that maximizes the variances in the data among groups while minimizing the variances between members of the same group.²⁸ Based on the utilization of a learning algorithm and a probabilistic determination, Bayesian classification rule can relate a small number of spectral metrics to specific histologic entities. The decision rule is not explicitly specified and Bayesian probability might be estimated directly from the using feature of the data distribution.^37,38 One-way analysis of variance (ANOVA)^31,39 is applied for identification of the most significant PCs ($p<0.05$) in diagnosis for differentiating normal, adenomatous polyp, and adenocarcinoma tissues. These significant PCs scores would be loaded into the model of LDA and NBC to classify those three different kinds of tissues (i.e., normal, adenomatous polyp, and adenocarcinoma tissues). Leave-one-spectrum-out and cross-validation method are used to ensure the validity of the analysis.²⁴ The results of histopathology serve as the golden standard for assessing the sensitivity and specificity of diagnosing different types of gastric tissues through RS.

3.1.

Spectral Features

Figure 1(a) shows the comparison of the average standardized Raman spectra of the normal ($n=45$, dash-dot line), adenomatous polyp ($n=27$, solid line), and adenocarcinoma gastric tissues ($n=33$, short dot line) in the range of 700 to $1800{\mathrm{cm}}^{-1}$. From this figure, it can be seen that the characteristic spectra are similar in shape and intensity among different types of gastric tissues and the spectrum of adenomatous polyp is in the middle of the spectra of normal and adenocarcinoma gastric tissues. Distinct Raman bands are found in those three kinds of tissues at the following peak positions with their respective tentative biomolecular assignments:^{9,26,28,31,40–42} $853{\mathrm{cm}}^{-1}$ (C-C stretching mode of proline), $936{\mathrm{cm}}^{-1}$ (C-C stretching in $\alpha$-helix conformation of proteins), $1003{\mathrm{cm}}^{-1}$ (C-C symmetric stretch ring breathing of phenylalanine), $1032{\mathrm{cm}}^{-1}$ (C-H stretching of phenylalanine), $1174{\mathrm{cm}}^{-1}$ (C-H bending of tyrosine), $1208{\mathrm{cm}}^{-1}$ (${\mathrm{C}\text{-}\mathrm{C}}_6{\mathrm{H}}_5$ stretching mode of phenylalanine and tryptophan), $1323{\mathrm{cm}}^{-1}$ [Guanine (B, Z marker); ${\mathrm{CH}}_3{\mathrm{CH}}_2$ wagging and deforming in collagen and purine bases of nucleic acids], $1335{\mathrm{cm}}^{-1}$ (${\mathrm{CH}}_3{\mathrm{CH}}_2$ twisting mode of proteins and nucleic acids), $1450{\mathrm{cm}}^{-1}$ (${\mathrm{CH}}_2$ bending mode of proteins and lipids), and $1655{\mathrm{cm}}^{-1}$ (amide I band $\mathrm{C}=\mathrm{O}$ stretching mode of proteins, indicating mainly in $\alpha$-helix conformation). Despite the similarities referred to above, remarkable variances in spectra are observed in the three kinds of tissues. First, compared with the normal tissues, adenomatous polyp tissues display lower intensities at 853, 936, 1003, 1032, 1174, and $1208{\mathrm{cm}}^{-1}$, while displaying higher intensities at 1323, 1335, 1450, and $1655{\mathrm{cm}}^{-1}$. Moreover, compared with the adenocarcinoma tissues, adenomatous polyp tissues reveal lower intensities at 1323, 1335, 1450, and $1655{\mathrm{cm}}^{-1}$, but higher intensities at 936, 1032, 1174, and $1208{\mathrm{cm}}^{-1}$. Second, the spectral bandwidths also display slight variability in the spectral ranges of 880 to 960, 1150 to 1220, and 1640 to $1675{\mathrm{cm}}^{-1}$. In addition, the Raman peak shifts of adenomatous polyp and adenocarcinoma tissues have obvious red shift in the spectral range of 1640 to $1675{\mathrm{cm}}^{-1}$ compared with the normal tissues. The average standardized Raman $\text{spectra}\pm \mathrm{SD}$ of the normal, adenomatous polyp, and adenocarcinoma tissues are depicted in Fig. 1(b), respectively. The spectral variabilities of each group are presented in this figure, and the shaded areas in Raman spectra represent the SD of spectral intensities. As shown in Fig. 1(b), spectral variabilities have similar patterns for both classes of normal and adenomatous polyp tissues and appear to be more apparent within the spectral ranges of 790 to 880, 930 to 990, and 1290 to $1400{\mathrm{cm}}^{-1}$, whereas adenocarcinoma tissues have spectral variations in the spectral range of 700 to $860{\mathrm{cm}}^{-1}$.

Fig. 1

(a) Comparison of the average standardized Raman spectra with distinct Raman peaks of the normal ($n=45$, dash-dot line), adenomatous polyp ($n=27$, solid line), and adenocarcinoma ($n=33$, short dot line) gastric tissues. (b) Average standardized Raman $\text{spectra}\pm \text{standard}$ deviation of the normal, adenomatous polyp, and adenocarcinoma gastric tissues, respectively. Note that the Raman spectra are vertically shifted for better visualization. (c) Difference spectra are calculated from the average Raman spectra among the three tissue types.

The difference spectra among the three types of gastric tissues are exhibited more clearly in Fig. 1(c). Overall, the variances of spectral intensities between normal and adenomatous polyps are smaller than those between normal and adenocarcinoma, which illustrates that adenomatous polyp is in the transitional state. From visual inspection, moreover, an inversion in difference spectra is found around $1230{\mathrm{cm}}^{-1}$, which evidences that the intensity of the Raman signal is higher in normal tissues than in pathological tissues (both adenomatous polyp and adenocarcinoma tissues) below $1230{\mathrm{cm}}^{-1}$, and vice versa above $1230{\mathrm{cm}}^{-1}$. In addition, comparison of the Raman signal intensity between adenomatous polyp and adenocarcinoma tissues indicates that the former is notably higher below $1250{\mathrm{cm}}^{-1}$, and vice versa above $1250{\mathrm{cm}}^{-1}$, except for the ranges around 700 to 790, 1000 to 1008, and 1480 to $1640{\mathrm{cm}}^{-1}$ that yield an opposite difference. Specially, nine prominent bands in Raman spectra with statistically significant differences among the three tissue types are identified ($p<0.05$, one-way ANOVA with confidence interval at 95%),³⁹ including 936, 1003, 1032, 1174, 1208, 1323, 1335, 1450, and $1655{\mathrm{cm}}^{-1}$ as revealed in Fig. 1(c), following earlier reported results.^{9,10,26–28,31} Table 1 summarizes the tentative biomolecular assignments^{9,26,28,31,40–42} of the nine significant Raman bands as well as the corresponding $p$-values generated from the post hoc Fisher’s least significant differences (LSD) test^27,31 of all pairwise tissue type comparisons (i.e., normal versus adenomatous polyp, adenomatous polyp versus adenocarcinoma, and normal versus adenocarcinoma). The variances of Raman spectral intensities are probably due to the differences of disease progression. For instance, the intensities of Raman peaks in the vicinity of 853, 936, 1032, 1174, and $1208{\mathrm{cm}}^{-1}$ decrease gradually from normal to cancer tissues [Fig. 1(a)], which may be due to the reductions in the percentage of certain biochemical constituents related to early pathological changes, such as proline, tyrosine, tryptophan, and phenylalanine, relative to the total Raman-active components.^26,28,29,40 On the other hand, when normal tissues develop into the adenomatous polyp and adenocarcinoma tissues, there are progressively increased Raman signals, which attribute to proteins and nucleic acids [e.g., 1323, 1335, 1450, and $1655{\mathrm{cm}}^{-1}$ in Fig. 1(a)]. The Raman intensities of those locations in adenomatous polyp and adenocarcinoma tissues enhance markedly, which may be caused by the increase in nucleic acids concentration, amide I of proteins and nucleo-cytoplasmic ratio. All of those are the major characteristics of tissue carcinogenesis.^{9,10,14,25,26,43} More specifically, compared with the normal tissues, the increased Raman band intensity and the bandwidth broadening of the $1655{\mathrm{cm}}^{-1}$ amide I vibration are highly associated with higher content of $\alpha$-helix proteins (e.g., histone, the main protein component that makes up the chromatin) in adenomatous polyp and adenocarcinoma tissues.^{24–26,28,31} Those discoveries are, in fact, in agreement with the gastric cytological studies for grading malignancy based on the hyperchromatic state of nucleus.^30,43 The alterations of protein-related Raman signals in pathological tissues may suggest the different proteomic activities in the cytoplasm or nucleus and the changes in the extracellular matrix,^{20,24–26,42} whereas the overall changes of Raman signals representing lipids and nucleic acids can be associated with an increase of proliferation activity.^20,22,25 The consistency in finding these significant changes of spectral features reinforces the importance of RS for detecting correlative subtle biochemical changes at the molecular level. This provides evidence that the changes of Raman spectral features, which are related to gradual alterations of biochemical constituents and structures in adenomatous polyp and adenocarcinoma tissues, may reflect the development of neoplasia.

Table 1

Tentative biomolecular assignments9,26,28,31,40–42 and diagnostic significance (p-value) of the prominent Raman bands identified from all pairwise tissue types based on post hoc Fisher’s least significant differences (LSD) test.27,31

3.2.

Diagnosis Potential of RS Using PCA-LDA and PCA-NBC Algorithms

The diagnostically significant PC loadings computed by PCA are shown in Fig. 2, along with the percentage of spectral variance captured by each of the respective PCs (PC1, PC2, PC3, PC9, PC10, $p<0.05$, one-way ANOVA with confidence interval at 95%). Obviously, PC1 accounting for the largest Raman spectral variance ($\sim 64.04\%$ of the total variance) primarily represents the variations of molecule-specific Raman signals in the major Raman peaks (e.g., at 853, 936, 1003, 1174, 1208, 1323, 1335, 1450, and $1655{\mathrm{cm}}^{-1}$), whereas the successive PCs describe the spectral features that contribute progressively smaller variances (i.e., $\mathrm{PC}2\sim 15.36\%$, $\mathrm{PC}3\sim 5.73\%$, $\mathrm{PC}9\sim 0.55\%$, $\mathrm{PC}10\sim 0.43\%$). According to Fig. 2, the positive and negative band features indicate the correlative direction between PCs and Raman bands. For instance, contributions from 853, 1003, 1174, 1208, and $1450{\mathrm{cm}}^{-1}$ are mostly positive correlation to PC1, whereas those from 1335 and $1655{\mathrm{cm}}^{-1}$ are negatively correlated with PC1. PC loadings essentially reveal the significant Raman spectral features pertinent to proteins, lipids, and nucleic acids within the three different types of gastric tissues, and their features are similar to those of tissue Raman spectra in Fig. 1(a), substantiating that the PCA model has the ability to extract the diagnostic information carried by RS from gastric tissues. Figure 3 displays the estimated average values of the five diagnostically significant PCs for identifying the three different types of gastric tissues. The first three PCs (PC1, PC2, and PC3) account for the maximum variance ($\sim 85.13\%$) of the total variance in the whole range of the Raman spectrum (i.e., 700 to $1800{\mathrm{cm}}^{-1}$); thus they contain the most diagnostically significant Raman features for differentiating normal, adenomatous polyp, and adenocarcinoma tissues. From Fig. 3, it can be seen that the average values of PCs tend to increase gradually with the development of neoplasia, but PC1 exhibits an opposite trend, which demonstrates the efficacy of PC scores in identification of the three different types of gastric tissues. The diagnostically significant PCs together with the corresponding $p$-values calculated from the post hoc Fisher’s LSD test of all pairwise tissue type comparisons are listed in Table 2. According to these results, different PC scores have different degrees of diagnostic capacity for classifying the tissue types. For instance, PC1, PC3, and PC10 can be used to differentiate adenocarcinoma from normal tissues; PC2 is optimal in discriminating the three different types of gastric tissues; and PC9 can be utilized to distinguish adenocarcinoma from adenomatous polyp and normal tissues.

Fig. 2

The diagnostically significant PC loadings (PC1, PC2, PC3, PC9, PC10, $p<0.05$, one-way ANOVA) capturing a total variance of 86.1% in the full range of the Raman spectrum calculated from Raman spectra of gastric tissues, revealing the diagnostically significant spectral features for tissue classification.

Fig. 3

Broken line graph of average values of the five diagnostically significant PCs (PC1, PC2, PC3, PC9, and PC10), demonstrating the efficacy of PC scores for classification of the normal, adenomatous polyp, and adenocarcinoma gastric tissues.

Table 2

Five diagnostically significant PCs of all pairwise tissue types as well as the corresponding p-values generated from post hoc Fisher’s LSD test.

In order to further improve the accuracy of the discrimination, we load the diagnostically significant PC scores into the LDA and NBC model to generate effective diagnostic algorithms for tissue groupings. Figure 4 exhibits the two-dimensional scatter plot of the two linear discriminant (LD) functions (LD1 versus LD2) based on the PCA-LDA model, in which obvious clusterings of the tissue types are largely separated into three diagnostic groups [i.e., normal (circle), adenomatous polyp (square), and adenocarcinoma (pentacle)]; the mixed results may be attributed to the similar constituents within tissues. The discrimination results based on NIR RS using PCA-LDA and PCA-NBC algorithms associated with leave-one-out, cross-validation method are summarized in Table 3. Diagnostic sensitivities of 81.5%, 85.3%, and 100%, and specificities of 86.4%, 100%, and 97.4%, respectively, are achieved by using PCA-LDA algorithms to discriminate the normal, adenomatous polyp, and adenocarcinoma gastric tissues, while PCA-NBC algorithms provide the diagnostic sensitivities of 96.3%, 96.9%, and 96.9%, and specificities of 93%, 100%, and 95.2%, respectively, for identifying those three kinds of tissues. The diagnostic algorithms based on PCA-LDA and PCA-NBC techniques have offered accurate information for gastric tissue classification, further demonstrating a considerable value of RS for tissue diagnosis. Compared with PCA-LDA algorithms, PCA-NBC techniques together with leave-one-out, cross-validation method have realized more effective discriminating capacity for differentiating adenomatous polyp and adenocarcinoma from the normal gastric tissues with a higher sensitivity and specificity.

Fig. 4

Scatter plot of the two linear discriminant functions (LD1 versus LD2) based on the PCA-LDA model for discrimination of the normal, adenomatous polyp, and adenocarcinoma gastric tissues.

Table 3

Identification results based on NIR Raman spectra of the normal (n=45), adenomatous polyp (n=27), and adenocarcinoma (n=33) gastric tissues utilizing PCA-LDA and PCA-NBC algorithms together with leave-one-out, cross-validation method.

To find the embodiment of the regularity of pathological development of cancer on Raman spectral characteristic, we explore the Raman spectral properties of normal, adenomatous polyp, and adenocarcinoma gastric tissues in this paper. The results confirm a potential role of RS for revealing the rules of biochemical/biomolecular alterations associated with normal-to-cancer transition in the stomach. However, it is noteworthy that this ex vivo study may not truly reflect in vivo clinical conditions during endoscopic inspection. The in vivo Raman signals may contain a mixture of spectral information from different pathological tissues due to the complexity in the body’s internal environment. Nevertheless, the regularity of biochemical/biomolecular changes is fundamentally similar in both in vivo and ex vivo conditions. For this reason, the results of this ex vivo study will provide important experimental basis to the study of in vivo real-time diagnosis of premalignant lesions and early gastric cancers based on RS during endoscopy. Despite the great advantages that NIR RS could offer, however, in vivo Raman measurements with fiber-optic probes have many technical challenges to overcome because of the weak intensity of the Raman signal from tissue, interference from tissue autofluorescence, and spectral contamination of background fluorescence and Raman signals from the fiber-optic material comprised in the probe.^11,24 Also, the collection times and laser powers are the main limitations for clinical application. Besides, Raman spectral differences are usually subtle between different pathological types of gastric tissues.^10,24 Consequently, it is highly required to develop robust diagnosis algorithms to extract diagnostically important information that is related to biochemical/biomolecular changes associated with neoplastic transformation for effective tissue classification. The effective settlement of these problems will greatly promote the development of an NIR Raman endoscopic system that is a potentially clinically useful tool for the rapid, noninvasive and in vivo early diagnosis of gastric premalignant lesions at the molecular level.