5.1.2. Gastrointestinal cancer

In 2014, Bergholt et al. [36] performed an in vivo diagnostic trial to classify dysplasia in Barrett's esophagus (BE). They reported a diagnostic sensitivity of 87.0% and a specificity of 84.7%, which demonstrate that real-time Raman spectroscopy can be performed prospectively in screening of the patients with suspicious BE in vivo. In a study conducted on mice with colon cancer, Taketani et al. [37] identified alterations in its molecular composition of lipids and collagen type I, along with its advancement. The tumor lesion was discriminated from normal tissues of the control mouse with an accuracy of 86.8%. Stomach cancer diagnosis has been another application in biomedical Raman spectroscopy [38]. Bergholt et al. have also reported a statistically robust study where 450 patients underwent Raman endoscopy for identifying gastric precancer based on PLS-DA [39]. The same group used in vivo Raman spectroscopy to characterize the properties of normal colorectal tissues and to assess distinctive biomolecular variations of different anatomical locations in the colorectum for cancer diagnosis. They conclude that interanatomical Raman spectral variability of normal colorectal tissue is subtle compared to cancer tissue. Their PLS-DA model provided a diagnostic accuracy of 88.8%, a sensitivity of 93.9% and a specificity of 88.3% for colorectal cancer detection [40].

#### 5.1.3. Oral cancer

In a study conducted by Guze et al. [41], Raman spectra of oral diseases from 18 patients were classified into a benign or malignant category using PCA-LDA, and the method provided 100% specificity with 77% sensitivity. Murali Krishna et al. reported the potential for Raman spectroscopy to identify early changes in oral mucosa and the efficacy of this approach in oral cancer applications [42]. Comparing noncancer locations in a smoking and nonsmoking population demonstrated prediction accuracies from 75 to 98%. Another group reported the discrimination of normal oral tissue from different lesion categories with accuracies ranging from 82 to 89% [43]. Recently, Lin et al. [44] reported the utility of fiber-optic–based Raman spectroscopy for real-time in vivo diagnosis of nasopharyngeal carcinoma (NPC) at endoscopy. A total of 3731 in vivo Raman spectra were acquired in real time from 95 subjects. Raman spectra differ significantly between normal and cancerous nasopharyngeal tissues. Using PCA-LDA, their method provided a diagnostic accuracy of 93.1% (sensitivity of 93.6%; specificity of 92.6%) for nasopharyngeal cancer identification. The Raman spectra of the diseased tissue include oral squamous cell carcinoma (OSCC), oral submucosa fibrosis (OSMF), and oral leukoplakia (OLK). The study achieved good diagnostic accuracy for the three diseased groups and the normal group, which were 89, 85, 82, and 85%, respectively.

#### 5.1.4. Skin cancer

hollow organs is the development of fiber-optic Raman probes that can be implemented during

Short et al. designed a Raman probe for in vivo detection of lung cancer during autofluorescence bronchoscopy [34], and they demonstrated the potential of Raman for in vivo diagnosis of lung

In 2014, Bergholt et al. [36] performed an in vivo diagnostic trial to classify dysplasia in Barrett's esophagus (BE). They reported a diagnostic sensitivity of 87.0% and a specificity of 84.7%, which demonstrate that real-time Raman spectroscopy can be performed prospectively in screening of the patients with suspicious BE in vivo. In a study conducted on mice with colon cancer, Taketani et al. [37] identified alterations in its molecular composition of lipids and collagen type I, along with its advancement. The tumor lesion was discriminated from normal tissues of the control mouse with an accuracy of 86.8%. Stomach cancer diagnosis has been another application in biomedical Raman spectroscopy [38]. Bergholt et al. have also reported a statistically robust study where 450 patients underwent Raman endoscopy for identifying gastric precancer based on PLS-DA [39]. The same group used in vivo Raman spectroscopy to characterize the properties of normal colorectal tissues and to assess distinctive biomolecular variations of different anatomical locations in the colorectum for cancer diagnosis. They conclude that interanatomical Raman spectral variability of normal colorectal tissue is subtle compared to cancer tissue. Their PLS-DA model provided a diagnostic accuracy of 88.8%, a

cancer by reducing the false positives of autofluorescence bronchoscopy [35].

sensitivity of 93.9% and a specificity of 88.3% for colorectal cancer detection [40].

and the normal group, which were 89, 85, 82, and 85%, respectively.

In a study conducted by Guze et al. [41], Raman spectra of oral diseases from 18 patients were classified into a benign or malignant category using PCA-LDA, and the method provided 100% specificity with 77% sensitivity. Murali Krishna et al. reported the potential for Raman spectroscopy to identify early changes in oral mucosa and the efficacy of this approach in oral cancer applications [42]. Comparing noncancer locations in a smoking and nonsmoking population demonstrated prediction accuracies from 75 to 98%. Another group reported the discrimination of normal oral tissue from different lesion categories with accuracies ranging from 82 to 89% [43]. Recently, Lin et al. [44] reported the utility of fiber-optic–based Raman spectroscopy for real-time in vivo diagnosis of nasopharyngeal carcinoma (NPC) at endoscopy. A total of 3731 in vivo Raman spectra were acquired in real time from 95 subjects. Raman spectra differ significantly between normal and cancerous nasopharyngeal tissues. Using PCA-LDA, their method provided a diagnostic accuracy of 93.1% (sensitivity of 93.6%; specificity of 92.6%) for nasopharyngeal cancer identification. The Raman spectra of the diseased tissue include oral squamous cell carcinoma (OSCC), oral submucosa fibrosis (OSMF), and oral leukoplakia (OLK). The study achieved good diagnostic accuracy for the three diseased groups

endoscopy [33].

300 Raman Spectroscopy

5.1.1. Lung cancer

5.1.3. Oral cancer

5.1.2. Gastrointestinal cancer

A clinical study of 453 patients to investigate different types of skin cancer was published in 2012 by Lui et al. [45]. The instrument used by the authors allowed an acquisition time of approximately 1s and the software preprocessed the spectra immediately, which allowed to investigate skin lesions in real time. Benign and malignant skin lesions including melanomas, basal cell carcinomas, squamous cell carcinomas, actinic keratoses, atypical nevi, melanocytic nevi, blue nevi, and seborrheic keratosis were investigated and discriminated by multivariate analysis tools with sensitivities between 95 and 99%. Lim et al. determined the diagnostic capability of a multimodal spectral diagnosis for in vivo noninvasive disease diagnosis of melanoma and nonmelanoma skin cancers [46]. They acquired reflectance, fluorescence, and Raman spectra from 137 lesions in 76 patients using optical fiber–based systems. They obtained the best classification for nonmelanoma skin cancers when using multimodal approach. On the other hand, the best melanoma classification occurred when using Raman spectroscopy alone. A Raman probe to detect invasive brain cancer in situ in real time in patients was developed by Jermyn et al. [47]. They demonstrated that Raman spectroscopy can accurately detect grade 2–4 gliomas in vivo during human brain cancer surgery and it was possible to differentiate between cancer cell–invaded brain and normal brain, with sensitivity and specificity greater than 90%. Additionally, this approach can classify in real time, making it an invaluable tool for surgical procedure and decision making.

#### 5.2. Skin diseases

#### 5.2.1. Atopic dermatitis

Several published works have used Raman spectroscopy to analyze the molecular composition of skin and correlate it with history of atopic dermatitis (AD) and filaggrin gene (FLG) mutations; Kezic et al. measured NMFs noninvasively on the skin of 137 Irish children with a history of moderate to severe AD [48]. González et al. detected the presence of the protein filaggrin in the skin of newborns using Raman spectroscopy and PCA as an early detection procedure for filaggrin-related AD [49]. In order to detect the presence of filaggrin in the Raman spectra, the coefficients of the principal components for each of the skin spectra from newborns were calculated. The first and second principal components accounted for 93.86% of all the explained variance of the original data. Figure 3 shows a graph of these two principal components, also known as scores plot. In the figure, the gray solid circles correspond to those infants who developed AD; the rest of the subjects are grouped together around the location of the filaggrin spectrum, represented as a black solid circle. The geometrical distance of each Raman spectra to the spectrum of filaggrin in the principal component plane indicates the amount of filaggrin in the subjects. Lower distances indicate higher amount of filaggrin and higher distances indicate lees amount of filaggrin or a filaggrin with a different molecular structure than the molecule that was taken as a reference spectrum.

This result indicates that this approach can be used to identify the persons who are more susceptible to develop AD, making it possible to use this technique as a method for early detection of AD. González et al. validated the use of Raman spectroscopy as a noninvasive tool to detect filaggrin gene mutations [50]. In this study, the amount of filaggrin was estimated by performing the correlation between the pure filaggrin Raman spectrum and the skin spectra obtained from Mexican patients with AD; the genetic analysis showed that 8 out of the 19 patients (42%) presented an FLG mutation. These 8 patients presented the 2282del4 FLG mutation, 2 of which (10.5%) were homozygous and 6 (31.5%) heterozygous, whereas 1 (5.2%) resulted in a compound heterozygote for the 2282del4 and the R501X mutations. These genetic results were compared to the filaggrin amount estimated; a lower correlation value of the spectra with the filaggrin spectrum indicates a lower filaggrin concentration. Figure 4 shows the results of the correlation for the patients with an FLG mutation (FLG –) and without an FLG mutation (FLG +). The patients with an FLG mutation presented an average correlation of 0.286, while the patients without an FLG mutation showed an average correlation of 0.4. Their results show that the correlation of the filaggrin Raman spectrum with the Raman

spectra of skin can be an indicator of filaggrin gene mutations. In another work, Baclig et al. used a genetic algorithm to demonstrate that strongly reduced Raman spectral information is

Raman Spectroscopy for In Vivo Medical Diagnosis http://dx.doi.org/10.5772/intechopen.72933 303

Tfayli et al. reported slight variability in skin lipids upon aging [52]. The Raman spectral features of the skin lipids shifted in lateral packing with increasing age of the volunteers. González et al. differentiated between chronological aging and photoinduced skin damage by

Alda et al. [54] detected biochemical differences in the structure of the skin of subjects with nickel allergy when comparing with healthy subjects. The Raman spectral differences between

Moncada et al. [55] used Raman spectroscopy in melasma patients treated with a triple combination cream (Tretinoin, Fluocinolona, and Hydroquinone) and found that the Raman skin spectra of the melasma patients showed differences in the peaks associated to melanin at 1352 and 1580 cm<sup>1</sup> (Figure 5). The Raman skin spectrum of patients who did not respond to treatment (Figure 1B) showed peaks that are not well defined, which are consistent with molecule degradation and protein breakdown. These results are consistent with the results

In most of the in vivo Raman applications, near infrared (NIR) excitation sources are preferred. NIR wavelengths in the range of 780–1100 nm result in lower fluorescence background in the tissue and simplify the analysis of the Raman bands in comparison to visible or UV excitation. The visible excitation sources have been used in various biomedical Raman applications [57]. However, the use of visible wavelengths has several disadvantages for in vivo biomedical Raman applications such as the decrease of penetration depth, autofluorescence, and heat generation. The UV radiation is not used for in vivo measurements due to the mutagenicity. In Raman imaging [58], a laser spot scans the sample area and acquires Raman spectra at every set point. The intensity of a specific Raman band or bands is used to build an image from cells and tissues. Also the Raman spectra can be discriminated by chemometric analysis and the result is an image of the sample that contains chemical information, also known as Raman chemical image. Other methods of Raman imaging include coherent anti-Stokes Raman spectroscopy (CARS) and stimulated Raman scattering. These methods have been applied to study biochemical interactions in cells and tissues. However, the in vivo applications have been limited to animal models. [59–61]. The Raman imaging has the disadvantage that long integration times are needed, which limit its use for in vivo measurement in humans. Surface-enhanced Raman spectroscopy (SERS)

PCA of in vivo Raman spectra from sun-protected and sun-exposed skin [53].

5.2.5. Other in vivo applications: UV/Vis Raman, Raman imaging, and SERS

sufficient for clinical diagnosis of atopic dermatitis [51].

5.2.2. Skin aging

5.2.3. Nickel allergy

5.2.4. Melasma

groups were classified using PCA.

reported previously by González et al. [56].

Figure 3. Plot of the two first principal components of the Raman spectra for each newborn (white circles) and the Raman spectrum of filaggrin (black circle). The infants identified with the numbers 1, 9, and 11 developed AD (gray circles) [49].

Figure 4. Correlation between the filaggrin Raman spectrum and the skin spectrum of subjects with (FLG ) and without (FLG +) filaggrin gene mutations [50].

spectra of skin can be an indicator of filaggrin gene mutations. In another work, Baclig et al. used a genetic algorithm to demonstrate that strongly reduced Raman spectral information is sufficient for clinical diagnosis of atopic dermatitis [51].

#### 5.2.2. Skin aging

tool to detect filaggrin gene mutations [50]. In this study, the amount of filaggrin was estimated by performing the correlation between the pure filaggrin Raman spectrum and the skin spectra obtained from Mexican patients with AD; the genetic analysis showed that 8 out of the 19 patients (42%) presented an FLG mutation. These 8 patients presented the 2282del4 FLG mutation, 2 of which (10.5%) were homozygous and 6 (31.5%) heterozygous, whereas 1 (5.2%) resulted in a compound heterozygote for the 2282del4 and the R501X mutations. These genetic results were compared to the filaggrin amount estimated; a lower correlation value of the spectra with the filaggrin spectrum indicates a lower filaggrin concentration. Figure 4 shows the results of the correlation for the patients with an FLG mutation (FLG –) and without an FLG mutation (FLG +). The patients with an FLG mutation presented an average correlation of 0.286, while the patients without an FLG mutation showed an average correlation of 0.4. Their results show that the correlation of the filaggrin Raman spectrum with the Raman

Figure 4. Correlation between the filaggrin Raman spectrum and the skin spectrum of subjects with (FLG ) and without

Figure 3. Plot of the two first principal components of the Raman spectra for each newborn (white circles) and the Raman spectrum of filaggrin (black circle). The infants identified with the numbers 1, 9, and 11 developed AD (gray circles) [49].

(FLG +) filaggrin gene mutations [50].

302 Raman Spectroscopy

Tfayli et al. reported slight variability in skin lipids upon aging [52]. The Raman spectral features of the skin lipids shifted in lateral packing with increasing age of the volunteers. González et al. differentiated between chronological aging and photoinduced skin damage by PCA of in vivo Raman spectra from sun-protected and sun-exposed skin [53].

#### 5.2.3. Nickel allergy

Alda et al. [54] detected biochemical differences in the structure of the skin of subjects with nickel allergy when comparing with healthy subjects. The Raman spectral differences between groups were classified using PCA.
