Raman spectroscopic detection of high-grade cervical cytology: Using morphologically normal appearing cells

Duraipandian, Shiyamala; Traynor, Damien; Kearney, Padraig; Martin, Cara; O’Leary, John; Lyng, Fiona M.

doi:10.1038/s41598-018-33417-8

Cited by 30 publications

(37 citation statements)

References 32 publications

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“…The mean Raman spectra and LV1 loadings showed that the discrimination was mostly based around increased nucleic acids (727, 781, 826, 1485, and 1580 cm −1 ), decreased glycogen (482, 852, 937, 1082, 1123, 1334, and 1380 cm −1 ), and changes in protein features (1152, 1240, 1450, 1640, and 1670 cm −1 ), indicating increased proliferation and altered protein expression due to the overexpression of E6/E7 viral proteins in the samples with a transcriptionally active HPV infection. These discriminating spectral features are consistent with previous studies on exfoliated cells showing discrimination between samples with negative cytology and HSIL cytology [ 25 , 27 , 28 , 30 , 34 ] and between samples with negative cytology and HPV DNA-positive HSIL samples [ 29 ]. PLSDA classification with LOPOCV achieved a sensitivity of 88% and a specificity of 88% in distinguishing non-transcriptionally active HPV infection from transcriptionally active HPV infection.…”

Section: Discussionsupporting

confidence: 91%

Raman Spectroscopy of Liquid-Based Cervical Smear Samples as a Triage to Stratify Women Who Are HPV-Positive on Screening

Traynor

Martin

White

et al. 2021

Cancers

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The role of persistent high-risk human papillomavirus (HPV) infection in the development of cervical precancer and cancer is now well accepted, and HPV testing has recently been introduced for primary cervical screening. However, the low specificity of HPV DNA testing can result in large numbers of women with an HPV-positive result, and additional triage approaches are needed to avoid over-referral to colposcopy and overtreatment. The aim of this study was to assess Raman spectroscopy as a potential triage test to discriminate between transient and persistent HPV infection. HPV DNA status and mRNA status were confirmed in ThinPrep® cervical samples (n = 60) using the Cobas 4800 and APTIMA HPV test, respectively. Raman spectra were recorded from single-cell nuclei and subjected to partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 14). Sensitivity of 85% and specificity of 92% were achieved for the classification of transient and persistent HPV infection, and this increased to 90% sensitivity and 100% specificity when mean sample spectra were used instead of individual cellular spectra. This study showed that Raman spectroscopy has potential as a triage test for HPV-positive women to identify persistent HPV infection.

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Section: Discussionsupporting

confidence: 91%

Raman Spectroscopy of Liquid-Based Cervical Smear Samples as a Triage to Stratify Women Who Are HPV-Positive on Screening

Traynor

Martin

White

et al. 2021

Cancers

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“…The resulting LD scores matrix “ Z ” represents compactly the original data features, “ ” and differentiates one class from another very efficiently. Detail of LDA can be found in 58 – 60 .…”

Section: Methodsmentioning

confidence: 99%

Developing intelligent medical image modality classification system using deep transfer learning and LDA

Hassan

Ali²,

Alquhayz

et al. 2020

Sci Rep

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Rapid advancement in imaging technology generates an enormous amount of heterogeneous medical data for disease diagnosis and rehabilitation process. Radiologists may require related clinical cases from medical archives for analysis and disease diagnosis. It is challenging to retrieve the associated clinical cases automatically, efficiently and accurately from the substantial medical image archive due to diversity in diseases and imaging modalities. We proposed an efficient and accurate approach for medical image modality classification that can used for retrieval of clinical cases from large medical repositories. The proposed approach is developed using transfer learning concept with pre-trained ResNet50 Deep learning model for optimized features extraction followed by linear discriminant analysis classification (TLRN-LDA). Extensive experiments are performed on challenging standard benchmark ImageCLEF-2012 dataset of 31 classes. The developed approach yields improved average classification accuracy of 87.91%, which is higher up-to 10% compared to the state-of-the-art approaches on the same dataset. Moreover, hand-crafted features are extracted for comparison. Performance of TLRN-LDA system demonstrates the effectiveness over state-of-the-art systems. The developed approach may be deployed to diagnostic centers to assist the practitioners for accurate and efficient clinical case retrieval and disease diagnosis.

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“…The ThinPrep standard, including the use of associated fixatives and glass slides, has previously been shown to be compatible with Raman micro-spectroscopy. [5,7,12,24,30] HeLa cells were selected for this initial investigation due to their well known morphology and were prepared as described in Section 3.1.1 and imaged using an IX81 fluorescence microscope as described in Section 3.2. In Fig.1 (a) the live cells are shown in medium. The image was slightly defocused by displacing the sample approximately 1μm from the focal plane, and oblique illumination was used in order to improve visualisation of cell boundaries.…”

Section: Automationmentioning

confidence: 99%

Automated Raman micro-spectroscopy of epithelial cells for the high-throughput classification

O’Dwyer

Domijan

Dignam

et al. 2021

Preprint

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Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy that is greater than standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility, have prevented the clinical adoption of the technology. Here we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented using the open source Micro-Manager platform, and is made freely available. We believe this code can be readily integrated into existing commercial Raman micro-spectrometers.

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Raman spectroscopic detection of high-grade cervical cytology: Using morphologically normal appearing cells

Cited by 30 publications

References 32 publications

Raman Spectroscopy of Liquid-Based Cervical Smear Samples as a Triage to Stratify Women Who Are HPV-Positive on Screening

Raman Spectroscopy of Liquid-Based Cervical Smear Samples as a Triage to Stratify Women Who Are HPV-Positive on Screening

Developing intelligent medical image modality classification system using deep transfer learning and LDA

Automated Raman micro-spectroscopy of epithelial cells for the high-throughput classification

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