In vivo Raman spectroscopic characterization of papillary thyroid carcinoma

Medeiros-Neto, L.P.; Soto, Claudio A. Telléz; Chagas, M.J.; Carvalho, Luís Felipe C. S.; Rajasekaran, R.; Martin, Aírton Abrahão

doi:10.1016/j.vibspec.2018.12.008

Cited by 6 publications

(3 citation statements)

References 60 publications

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“…* email: armida.sodo@uniroma3.it bacteria, and in particular to differentiate between healthy and cancer tissues [9][10][11][12] . Preliminary studies on thyroid tissues by RS suggest that this technique may have high impact in thyroid cancer diagnosis [13][14][15][16][17][18] .…”

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confidence: 99%

Thyroid cancer diagnosis by Raman spectroscopy

Sbroscia

Gioacchino

Ascenzi

et al. 2020

Sci Rep

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Over the last 50 years, the incidence of human thyroid cancer disease has seen a significative increment. This comes along with an even higher increment of surgery, since, according to the international guidelines, patients are sometimes addressed to surgery also when the fine needle aspiration gives undetermined cytological diagnosis. As a matter of fact, only 30% of the thyroid glands removed for diagnostic purpose have a post surgical histological report of malignancy: this implies that about 70% of the patients have suffered an unnecessary thyroid removal. Here we show that Raman spectroscopy investigation of thyroid tissues provides reliable cancer diagnosis. Healthy tissues are consistently distinguished from cancerous ones with an accuracy of ∼ 90%, and the three cancer typology with highest incidence are clearly identified. More importantly, Raman investigation has evidenced alterations suggesting an early stage of transition of adenoma tissues into cancerous ones. These results suggest that Raman spectroscopy may overcome the limits of current diagnostic tools.

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confidence: 99%

Thyroid cancer diagnosis by Raman spectroscopy

Sbroscia

Gioacchino

Ascenzi

et al. 2020

Sci Rep

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“…As shown in Figure 6, the Raman spectrum of the HGIN tissues exhibits significant peaks at 826 cm -1 , 838 cm -1 (amine deformation vibrations), 849 cm -1 , 861 cm -1 , 875 cm -1 (tryptophan), 889 cm -1 , 987 cm -1 , 1101 cm -1 , 1171 cm -1 , 1209 cm -1 , 1235 cm -1 , 1253 cm -1 (C-O4 aromatic stretching), 1324 cm -1 , 1413 cm -1 , and 1562 cm -1 , while the Raman spectrum of the adenocarcinoma tissues displays significant peaks at 820 cm -1 (structural protein modes of tumors), 849 cm -1 , 861 cm -1 , 875 cm -1 , 887 cm -1 , 948 cm -1 (singlebond stretching vibrations of the amino acids proline and 3 Gastroenterology Research and Practice valine and polysaccharides), 977 cm -1 , 1102 cm -1 , 1171 cm -1 , 1209 cm -1 , 1236 cm -1 , 1293 cm -1 , 1323 cm -1 , 1413 cm -1 , 1517 cm -1 , 1531 cm -1 (carotenoid, absent from the normal tissue spectrum), and 1562 cm -1 . The assignments for the peaks are given in Table 1 [14][15][16][17][18][19][20].…”

Section: Resultsmentioning

confidence: 99%

Identification of Lesional Tissues and Nonlesional Tissues in Early Gastric Cancer Endoscopic Submucosal Dissection Specimens Using a Fiber Optic Raman System

Luan

Qin

Dai

et al. 2020

Gastroenterology Research and Practice

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Aim. To identify lesional and nonlesional tissues from early gastric cancer (EGC) patients by Raman spectroscopy to build a diagnostic model and effectively diagnose EGC. Method. Specimens were collected by endoscopic submucosal dissection from 13 patients with EGC, and 55 sets of standard Raman spectral data (each integrated 10 times) were obtained using the fiber optic Raman system; there were 33 sets of lesional tissue data, including 18 sets of high-grade intraepithelial neoplasia (HGIN) data and 15 sets of adenocarcinoma data, and 22 sets of nonlesional tissue data. After the preprocessing steps, the average Raman spectrum was obtained. Results. The nonlesional tissues showed peaks at 891 cm-1, 1103 cm-1, 1417 cm-1, 1206 cm-1, 1234 cm-1, 1479 cm-1, 1560 cm-1, and 1678 cm-1. Compared with the peaks corresponding to nonlesional tissues, the peaks of the lesional tissues shifted by different magnitudes, and a new characteristic peak at 1324 cm-1 was observed. Comparing the peak intensity ratio and the integral energy ratio of the lesional tissues with those of the nonlesional tissues revealed a significant difference between the two groups (independent-samplest-test, P<0.05). Considering the peak intensity ratio of I1560 cm-1/I1103 cm-1 as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 98.8%, 93.9%, and 91.9%, respectively. Considering the integral energy ratio (noncontinuous frequency band and continuous frequency band) as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 99.2-99.6%, 93.9-97.0%, and 95.5%, respectively. Conclusions. The integral energy ratio of the Raman spectrum could be considered an effective indicator for the diagnosis of EGC.

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“…For all the aforementioned reasons, a non-invasive, real-time point-of-care method to detect and accurately diagnose cancer and premalignancies at early stages could benefit patients as well as decrease the risk of oral cancer incidence and mortality. One of the most cost-effective methods to diagnose cancer is the optical biopsy [1,3,[10][11][12][13][14][15][16][17][18]. The term optical biopsy is widely used in optical spectroscopy [6,14,[19][20][21][22][23][24], which assists in the diagnostic process and analyzes optical properties [19,[25][26][27] associated with tissue biochemistry [3][4][5][6][7][8][9]14,16,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Biochemical Profiles of In Vivo Oral Mucosa by Using a Portable Raman Spectroscopy System

Nogueira

Ribeiro

Pires

et al. 2021

Optics

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Most oral injuries are diagnosed by histopathological analysis of invasive and time-consuming biopsies. This analysis and conventional clinical observation cannot identify biochemically altered tissues predisposed to malignancy if no microstructural changes are detectable. With this in mind, detailed biochemical characterization of normal tissues and their differentiation features on healthy individuals is important in order to recognize biomolecular changes associated with early tissue predisposition to malignant transformation. Raman spectroscopy is a label-free method for characterization of tissue structure and specific composition. In this study, we used Raman spectroscopy to characterize the biochemistry of in vivo oral tissues of healthy individuals. We investigated this biochemistry based on the vibrational modes related to Raman spectra of four oral subsites (buccal, gingiva, lip and tongue) of ten volunteers as well as with principal component (PC) loadings for the difference between the four types of oral subsites. Therefore, we determined the biochemical characteristics of each type of healthy oral subsite and those corresponding to differentiation of the four types of subsites. In addition, we developed a spectral reference of oral healthy tissues of individuals in the Brazilian population for future diagnosis of early pathological conditions using real-time, noninvasive and label-free techniques such as Raman spectroscopy.

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In vivo Raman spectroscopic characterization of papillary thyroid carcinoma

Cited by 6 publications

References 60 publications

Thyroid cancer diagnosis by Raman spectroscopy

Thyroid cancer diagnosis by Raman spectroscopy

Identification of Lesional Tissues and Nonlesional Tissues in Early Gastric Cancer Endoscopic Submucosal Dissection Specimens Using a Fiber Optic Raman System

Biochemical Profiles of In Vivo Oral Mucosa by Using a Portable Raman Spectroscopy System

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