Raman spectroscopic study of cervical precancerous lesions and cervical cancer

Abstract

Early detection of cervical lesions, accurate diagnosis of cervical lesions, and timely and effective therapy can effectively avoid the occurrence of cervical cancer or improve the survival rate of patients. In this paper, the spectra of tissue sections of cervical inflammation (n = 60), CIN (cervical intraepithelial neoplasia) I (n = 30), CIN II (n = 30), CIN III (n = 30), cervical squamous cell carcinoma (n = 30), and cervical adenocarcinoma (n = 30) were collected by a confocal Raman micro-spectrometer (LabRAM HR Evolution, Horiba France SAS, Villeneuve d’Ascq, France). The Raman spectra of six kinds of cervical tissues were analyzed, the dominant Raman peaks of different kinds of tissues were summarized, and the differences in chemical composition between the six tissue samples were compared. An independent sample t test (p ≤ 0.05) was used to analyze the difference of average relative intensity of Raman spectra of six types of cervical tissues. The difference of relative intensity of Raman spectra of six kinds of tissues can reflect the difference of biochemical components in six kinds of tissues and the characteristic of biochemical components in different kinds of tissues. The classification models of cervical inflammation, CIN I, CIN II, CIN III, cervical squamous cell carcinoma, and cervical adenocarcinoma were established by using a support vector machine (SVM) algorithm. Six types of cervical tissues were classified and identified with an overall diagnostic accuracy of 85.7%. This study laid a foundation for the application of Raman spectroscopy in the clinical diagnosis of cervical precancerous lesions and cervical cancer.

Application of Raman spectroscopy technology based on deep learning algorithm in the rapid diagnosis of glioma

“…In order to avoid errors in the spectrum collection process, 5 spectra were recorded in different positions of each sample to obtain 190 spectra of the glioma patient serum and 225 spectra of the control serum for a total of 415 spectra. [34] In addition, the rubber band correction method in the OPUS software was used to calibrate the baseline of the scanned spectra. [35] 2.3 | Data preprocessing…”

Clinical applications of infrared and Raman spectroscopy in the fields of cancer and infectious diseases

“…Wang, 2021, China [67] Confocal Raman microscopy 60 Colon cancer Petersen, 2017, Germany [53] Fibre-optic Raman…”

“…Using tissue samples and Raman spectroscopy, five different studies demonstrated high diagnostic accuracies in detecting cervical precancer and cancer [67][68][69][70][71] . Wang et al [67] classified and identified six different tissue types (inflammation, CIN1, CIN2, CIN3, squamous cell carcinomas and adenocarcinomas) with 86% overall diagnostic accuracy. Precancerous lesions (CIN1-3) were identified with 80-89% accuracy while squamous cell carcinomas and adenocarcinomas were found with 100% and 86% accuracy.…”

Raman Spectroscopy of Individual Cervical Exfoliated Cells in Premalignant and Malignant Lesions

“…In a study by Wang et al (2021), spectral differences between cervical tissue of different grades of CIN, squamous cell carcinoma, and adenocarcinoma were elucidated, observing Raman bands corresponding to lipids, amino acids, collagen, and cytosine of greater intensity depending on the degree of inflammation of the tissue, being greater in high-grade lesions and cancer [22].…”