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.

Identifying functioning and nonfunctioning adrenal tumors based on blood serum surface-enhanced Raman spectroscopy

Adrenal tumors are common tumors in urology and they can be further divided into functioning and nonfunctioning tumors according to whether there is uncommon endocrine function. In clinical practice, the early identification and accurate assessment of adrenal tumors are essential for the guidance of subsequent treatment. However, a nonfunctioning adrenal tumor often lacks obvious clinical symptoms, making it difficult to be timely and precisely diagnosed by conventional examinations. Therefore, a rapid and accurate method for identifying the functioning and nonfunctioning adrenal tumors is urgently required to achieve precise treatment of adrenal tumors. In this study, surface-enhanced Raman spectroscopy was investigated as a diagnostic tool to identify the blood serum samples from healthy volunteers as well as the patients with functioning and nonfunctioning adrenal tumors. Based on the SERS peak analysis, abnormal glycolysis, DNA/RNA, and amino acid metabolites were found to be potential biomarkers for identifying patients with adrenal tumors, while metabolites related to disordered protein catabolism and excessive hormone secretion were expected to further differentiate functioning adrenal tumors from nonfunctioning adrenal tumors. In addition, principal component analysis followed by support vector machine (PCA-SVM) was further applied on those serum SERS measurements, and the classification accuracies of 96.8% and 84.5% were achieved for differentiating healthy group versus adrenal tumor group and functioning adrenal tumor group versus nonfunctioning adrenal tumor group, respectively. The results have demonstrated the prodigious potential of precise adrenal tumor diagnosis by using the blood serum surface-enhanced Raman spectroscopy technique.

Diagnosis and classification of intestinal diseases with urine by surface-enhanced Raman spectroscopy

Intestinal Disease (ID) is often characterized by clinical symptoms such as malabsorption, intestinal dysfunction, and injury. If treatment is not timely, it will increase the risk of cancer. Early diagnosis of ID is the key to cure it. There are certain limitations of the conventional diagnostic methods, such as low sensitivity and specificity. Therefore, development of a highly sensitive, non-invasive diagnostic method for ID is extremely important. Urine samples are easier to collect and more sensitive to changes in biomolecules than other pathological diagnostic samples such as tissue and blood. In this paper, a diagnostic method of ID with urine by surface-enhanced Raman spectroscopy (SERS) is proposed. A classification model between ID patients and healthy controls (HC) and a classification model between different pathological types of ID (i.e., benign intestinal disease (BID) and colorectal cancer (CRC)) are established. Here, 830 urine samples, including 100 HC, 443 BID, and 287 CRC, were investigated by SERS. The ID/HC classification model was developed by analyzing the SERS spectra of 150 ID and 100 HC, while BID/CRC classification model was built with 300 BID and 150 CRC patients by principal component analysis (PCA)-support vector machines (SVM). The two established models were internally verified by leave-one-out-cross-validation (LOOCV). Finally, the BID/CRC classification model was further evaluated by 143 BID and 137 CRC patients as an external test set. It shows that the accuracy of the classification model validated by the LOOCV for ID/HC and BID/CRC is 86.4% and 85.56%, respectively. And the accuracy of the BID/CRC classification model with external test set is 82.14%. It shows that high accuracy can be achieved with these two established classification models. It indicates that ID patients in the general population can be identified and BID and CRC patients can be further classified with measuring urine by SERS. It shows that the proposed diagnostic method and established classification models provide valuable information for clinicians to early diagnose ID patients and analyze different stages of ID.