A comparative evaluation of diffuse reflectance and Raman spectroscopy in the detection of cervical cancer

Abstract

Optical spectroscopic techniques show improved diagnostic accuracy for non-invasive detection of cervical cancers. In this study, sensitivity and specificity of two in vivo modalities, i.e diffuse reflectance spectroscopy (DRS) and Raman spectroscopy (RS), were compared by utilizing spectra recorded from the same sites (67 tumor (T), 22 normal cervix (C), and 57 normal vagina (V)). Data was analysed using principal component - linear discriminant analysis (PC-LDA), and validated using leave-one-out-cross-validation (LOOCV). Sensitivity, specificity, positive predictive value and negative predictive value for classification between normal (N) and tumor (T) sites were 91%, 96%, 95% and 93%, respectively for RS and 85%, 95%, 93% and 88%, respectively for DRS. Even though DRS revealed slightly lower diagnostic accuracies, owing to its lower cost and portability, it was found to be more suited for cervical cancer screening in low resource settings. On the other hand, RS based devices could be ideal for screening patients with centralised facilities in developing countries.

Highly Accurate Detection of Cancer In Situ with Intraoperative, Label-Free, Multimodal Optical Spectroscopy

“…Amino acid, amide, and protein concentrations were also different, suggesting modifications in cellular functions such as metabolism (46), cell The information associated with average IFS and DRS spectra is not as molecularly specific as RS because of the different physical origin of the light-tissue interaction processes leading to spectra with much less characteristic features. The spectra obtained for IFS and DRS conform to expectations and previous literature (26,28,29), as can be assessed on the basis of the shape of the intrinsic fluorescence spectra and the presence of characteristic hemoglobin peaks in the diffuse reflectance measurements (Fig. 3).…”

“…DRS provides information from tissue absorbers, including oxygenated and deoxygenated hemoglobin, lipids, water, cytochromes, and melanin (23)(24)(25). DRS has been used to detect cervical cancer (26), melanoma and nonmelanoma skin cancers (27), breast cancer (28), and colorectal metastases to liver (29).…”

“…In vivo RS has been shown to be a highly specific and sensitive cancer detection method for multiple pathologies with detection accuracy values usually limited to approximately 90% (7,(15)(16)(17)30), while oncology applications using IFS and/or DRS have been similarly limited (26,28,29,31). We hypothesized that IFS and DRS have the potential to synergistically complement the molecular information provided by RS to maximize cancer detection capability.…”

Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer

“…These characteristic Raman peaks interpret not only information about biological components of the cell but also their quality, quantity, symmetry, and orientation the compound. Recently, a combination of Raman microspectroscopy and machine learning techniques have been used successfully in the identification of various cancers such as lung, breast, cervix, stomach, brain, skin, oral, leukemia, and bladder cancer [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Initial RS studies used near-infrared and visible lasers to identify biochemical profiles of human breast biopsies.…”

Spectroscopy and Machine Learning Based Rapid Point-of-Care Assessment of Core Needle Cancer Biopsies

“…10 These include optical spectroscopy, 11 x-ray imaging, 12 confocal microscopy, [13][14][15] structured illumination microscopy, 16 Fourier transform infrared imaging, 17 fluorescence microscopy, [18][19][20][21] contrast-enhanced micrography, 22 as well as diffuse reflectance, electrical impedance, and Raman spectroscopy. [23][24][25][26] Use of these technologies in a clinical setting has been hampered by factors such as lengthy analytic times, tissue degradation, expense, on-site tissue staining, requirement for interpretive expertise, and challenges to workflow integration. Additionally, many of these technologies analyze tissues in vivo, which may be helpful for determining intraoperative surgical margins but is less informative for ex vivo tissue acquired from a needle biopsy.…”