Raman spectroscopic techniques to detect ovarian cancer biomarkers in blood plasma.

Robust diagnosis of ovarian cancer is crucial to improve patient outcomes. The lack of a single and accurate diagnostic approach necessitates the advent of novel methods in the field. In the present study, two spectroscopic techniques, Raman and surface-enhanced Raman spectroscopy (SERS) using silver nanoparticles, have been employed to identify signatures linked to cancer in blood. Blood plasma samples were collected from 27 patients with ovarian cancer and 28 with benign gynecological conditions, the majority of which had a prolapse. Early ovarian cancer cases were also included in the cohort (n = 17). The derived information was processed to account for differences between cancerous and healthy individuals and a support vector machine (SVM) algorithm was applied for classification. A subgroup analysis using CA-125 levels was also conducted to rule out that the observed segregation was due to CA-125 differences between patients and controls. Both techniques provided satisfactory diagnostic accuracy for the detection of ovarian cancer, with spontaneous Raman achieving 94% sensitivity and 96% specificity and SERS 87% sensitivity and 89% specificity. For early ovarian cancer, Raman achieved sensitivity and specificity of 93% and 97%, respectively, while SERS had 80% sensitivity and 94% specificity. Five spectral biomarkers were detected by both techniques and could be utilised as a panel of markers indicating carcinogenesis. CA-125 levels did not seem to undermine the high classification accuracies. This minimally invasive test may provide an alternative diagnostic and screening tool for ovarian cancer that is superior to other established blood-based biomarkers.

Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer.

Currently available screening tests do not deliver the required sensitivity and specificity for accurate diagnosis of ovarian or endometrial cancer. Infrared (IR) spectroscopy of blood plasma or serum is a rapid, versatile, and relatively non-invasive approach which could characterize biomolecular alterations due to cancer and has potential to be utilized as a screening or diagnostic tool. In the past, no such approach has been investigated for its applicability in screening and/or diagnosis of gynaecological cancers. We set out to determine whether attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy coupled with a proposed classification machine could be applied to IR spectra obtained from plasma and serum for accurate class prediction (cancer vs. normal). Plasma and serum samples were obtained from ovarian cancer cases (n = 30), endometrial cancer cases (n = 30) and non-cancer controls (n = 30), and subjected to ATR-FTIR spectroscopy. Four derived datasets were processed to estimate the real-world diagnosis of ovarian and endometrial cancer. Classification results for ovarian cancer were remarkable (up to 96.7%), whereas endometrial cancer was classified with a relatively high accuracy (up to 81.7%). The results from different combinations of feature extraction and classification methods, and also classifier ensembles, were compared. No single classification system performed best for all different datasets. This demonstrates the need for a framework that can accommodate a diverse set of analytical methods in order to be adaptable to different datasets. This pilot study suggests that ATR-FTIR spectroscopy of blood is a robust tool for accurate diagnosis, and carries the potential to be utilized as a screening test for ovarian cancer in primary care settings. The proposed classification machine is a powerful tool which could be applied to classify the vibrational spectroscopy data of different biological systems (e.g., tissue, urine, saliva), with their potential application in clinical practice.