Histology verification demonstrates that biospectroscopy analysis of cervical cytology identifies underlying disease more accurately than conventional screening: removing the confounder of discordance.

BACKGROUND: Subjective visual assessment of cervical cytology is flawed, and this can manifest itself by inter- and intra-observer variability resulting ultimately in the degree of discordance in the grading categorisation of samples in screening vs. representative histology. Biospectroscopy methods have been suggested as sensor-based tools that can deliver objective assessments of cytology. However, studies to date have been apparently flawed by a corresponding lack of diagnostic efficiency when samples have previously been classed using cytology screening. This raises the question as to whether categorisation of cervical cytology based on imperfect conventional screening reduces the diagnostic accuracy of biospectroscopy approaches; are these latter methods more accurate and diagnose underlying disease? The purpose of this study was to compare the objective accuracy of infrared (IR) spectroscopy of cervical cytology samples using conventional cytology vs. histology-based categorisation. METHODS: Within a typical clinical setting, a total of n = 322 liquid-based cytology samples were collected immediately before biopsy. Of these, it was possible to acquire subsequent histology for n = 154. Cytology samples were categorised according to conventional screening methods and subsequently interrogated employing attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. IR spectra were pre-processed and analysed using linear discriminant analysis. Dunn's test was applied to identify the differences in spectra. Within the diagnostic categories, histology allowed us to determine the comparative efficiency of conventional screening vs. biospectroscopy to correctly identify either true atypia or underlying disease. RESULTS: Conventional cytology-based screening results in poor sensitivity and specificity. IR spectra derived from cervical cytology do not appear to discriminate in a diagnostic fashion when categories were based on conventional screening. Scores plots of IR spectra exhibit marked crossover of spectral points between different cytological categories. Although, significant differences between spectral bands in different categories are noted, crossover samples point to the potential for poor specificity and hampers the development of biospectroscopy as a diagnostic tool. However, when histology-based categories are used to conduct analyses, the scores plot of IR spectra exhibit markedly better segregation. CONCLUSIONS: Histology demonstrates that ATR-FTIR spectroscopy of liquid-based cytology identifies the presence of underlying atypia or disease missed in conventional cytology screening. This study points to an urgent need for a future biospectroscopy study where categories are based on such histology. It will allow for the validation of this approach as a screening tool.

Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients.

Despite numerous advances in "omics" research, early detection of ovarian cancer still remains a challenge. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy could characterise alterations in the biomolecular signatures of human blood plasma/serum obtained from ovarian cancer patients compared to non-cancer controls. Blood samples isolated from ovarian cancer patients (n = 30) and healthy controls (n = 30) were analysed using ATR-FTIR spectroscopy. For comparison, a smaller cohort of samples (n = 8) were analysed using an InVia Renishaw Raman spectrometer. Resultant spectra were pre-processed prior to being inputted into principal component analysis (PCA) and linear discriminant analysis (LDA). Statistically significant differences (P < 0.001) were observed between spectra of ovarian cancer versus control subjects for both biospectroscopy methods. Using a support vector machine classifier for Raman spectra of blood plasma, a diagnostic accuracy of 74% was achieved, while the same classifier showed 93.3% accuracy for IR spectra of blood plasma. These observations suggest that a biospectroscopy approach could be applied to identify spectral alterations associated with the presence of insidious ovarian cancer.