Differentiating gastrointestinal stromal tumors from gastric adenocarcinomas and normal mucosae using confocal Raman microspectroscopy

Abstract

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract, and gastric adenocarcinomas are a common cancer worldwide. To differentiate GISTs from adenocarcinomas is important because the surgical processes for both are different; the former excises the tumor with negative margins, while the latter requires radical gastrectomy with lymph node dissection. Endoscopy with biopsy is used to distinguish GISTs from adenocarcinomas; however, it may cause tumor bleeding in GISTs. We reported here the confocal Raman microspectroscopy as an effective tool to differentiate GISTs, adenocarcinomas, and normal mucosae. Of 119 patients enrolled in this study, 102 patients underwent gastrectomy (40 GISTs and 62 adenocarcinomas), and 17 patients with benign lesions were obtained as normal mucosae. Raman signals were integrated for 100 s for each spot on the specimen, and 5 to 10 spots, depending on the sample size, were chosen for each specimen. There were significant differences among those tissues as evidenced by different Raman signal responding to phospholipids and protein structures. The spectral data were further processed and analyzed by using principal component analysis. A two-dimensional plot demonstrated that GISTs, adenocarcinomas, and normal gastric mucosae could be effectively differentiated from each other.

Confocal Raman microspectroscopy combined with chemometrics as a discrimination method of clostridia and serotypes of Clostridium botulinum strains

“…To enhance the application of confocal Raman microspectroscopy in differentiating, the Raman spectral data were further analyzed with the chemometrics methods as below. [49]…”

“…Orthogonal linear combinations to transform the original data into uncorrelated variables are termed PCs. [49] The PCs correspond to valuable "dimensions" of the data. Order of the PC corresponds to amount of information contained in a database relatively, for example, PC-1 express the highest amount of variation in the data.…”

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

“…A range of other studies also demonstrated the potential of Raman spectroscopy in diagnosing breast cancer in human and mouse breast tissues [89][90][91][92][93][94][95][96]. By exploiting the fingerprint region of the Raman spectrum and various machine learning techniques, Raman spectroscopy showed diagnostic potential for distinguishing cancerous tissues from normal tissues in brain cancer [56,[97][98][99][100], skin cancer [42,[101][102][103][104][105], gastrointestinal cancer [106][107][108][109], and lung cancer [110,111].…”

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

“…Cancer is one of the most common causes of death worldwide and RS makes it possible to diagnose undetected precancerous lesions in various organs, such as breast, skin, brain, gastrointestinal tract, heart, urinary, and reproductive tracts [40]. Hsu et al [41] investigated that confocal Raman microscopy distinguishes intestinal tumours from adenocarcinomas and normal, healthy organs. RS provides simple and immediate tissue identification during surgery, which allows for cancerous organs to be distinguished from healthy tissue.…”