Raman spectroscopic study of cervical precancerous lesions and cervical cancer.

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.

Diagnosis of cervical squamous cell carcinoma and cervical adenocarcinoma based on Raman spectroscopy and support vector machine.

In this report, we collected the Raman spectrum of cervical adenocarcinoma and cervical squamous cell carcinoma tissues by a micro-Raman spectroscopy system. We analysed, compared and summarized the characteristics and differences of the normalized mean Raman spectra of the two tissues and pointed out the major differences in the biochemical composition between the two tissues. The PCA-SVM model that was used to distinguish the two types of cervical cancer tissues was established. The accuracy of the model in differentiating cervical adenocarcinoma from cervical squamous cell carcinoma was 93.125%. The results of this study indicate that Raman spectroscopy of cervical adenocarcinoma and cervical squamous cell carcinoma tissue in combination with SVM (support vector analysis) and PCA (principal component analysis) can be useful for the classification of cervical adenocarcinoma and cervical squamous cell carcinoma tissues and for the exploration of the differences in biochemical compositions between the two types of cervical tissue. This study lays a foundation to further study Raman spectroscopy as a clinical diagnostic method for cervical cancer.