Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

Cals, Froukje L.J.; Koljenović, Senada; Hardillo, José A.; Jong, Robert J. Baatenburg de; Schut, Tom C. Bakker; Puppels, Gerwin J.

doi:10.1016/j.oraloncology.2016.06.012

Cited by 42 publications

(24 citation statements)

References 35 publications

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“…Raman spectroscopy (RS), a vibrational spectroscopic method sensitive to subtle biochemical perturbations in biological systems, has been extensively explored in disease and cancer diagnosis , including management of oral cancers . More importantly, RS has also shown potential in identifying subclinical cancer‐field effects (CFEs) or malignancy‐associated‐changes (MACs) in oral cancer patients .…”

Section: Introductionmentioning

confidence: 99%

Raman exfoliative cytology for prognosis prediction in oral cancers: A proof of concept study

Sahu

Gera

Malik

et al. 2019

Journal of Biophotonics

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Oral cancer is associated with high rates of recurrence, attributable to field cancerization. Early detection of advanced field changes that can potentially progress to carcinoma can facilitate timely intervention and can lead to improved prognosis. Previous in vivo studies have successfully detected advanced field effects in oral cancers. Raman exfoliative cytology has previously shown to differentiate normal, oral precancer and cancers. The present study explores Raman-exfoliative-cytology-based detection of field effects. Exfoliated cells were collected from tumor (n = 16) and contralateral-normal appearing mucosa (n = 16) of oral cancer patients, and healthy tobacco habitués (n = 20). After spectral acquisition, specimens were Papstained for cytological evaluation. Data analysis, by Principal Component Analysis and Principal Component-Linear Discriminant Analysis, indicate several spectralmisclassifications between contralateral normal and tumor, which were investigated and correlated with spectral, cytological and clinical outcomes. A qualitative analysis by grouping patients with number of misclassifications with tumor (Group 1: 0, Group 2: 1 and Group 3: >1) was explored. Group 3 with highest misclassifications showed spectral and cytological similarity to tumor groupone patient was a case of early inoperable residual disease, despite clear margins on histopathology.Thus, these misclassifications could be indicative of cancer field changes, and can prospectively help to identify patients susceptible to recurrences. K E Y W O R D Sfield cancerization, oral cancers, pap staining, Raman exfoliative cytology, Raman spectroscopy

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Section: Introductionmentioning

confidence: 99%

Raman exfoliative cytology for prognosis prediction in oral cancers: A proof of concept study

Sahu

Gera

Malik

et al. 2019

Journal of Biophotonics

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“…But Raman spectroscopy is able to establish a detailed spectral histopathology analysis of cancerous and dysplastic tissue after screening with multimodal nonlinear microscopy . Furthermore, also another working group has already established a linear discriminant analysis‐based approach to classify Raman spectra from tissue samples of patients tongue cancer into cancer vs nontumorous tissue …”

Section: Discussionmentioning

confidence: 99%

Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study

et al. 2018

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Background: A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed. Methods: Head and neck cancer sections were used for standard histopathology and co-registered with multimodal images from the same sections using the combination of coherent anti-Stokes Raman scattering, two-photon excited fluorescence, and second harmonic generation microscopy. The images analyzed with semantic segmentation using a FCN for four classes: cancer, normal epithelium, background, and other tissue types. Results: A total of 114 images of 12 patients were analyzed. Using a patch score aggregation, the average recognition rate and an overall recognition rate or the four classes were 88.9% and 86.7%, respectively. A total of 113 seconds were needed to process a whole-slice image in the dataset. Conclusion: Multimodal nonlinear microscopy in combination with automated image analysis using FCN seems to be a promising technique for objective differentiation between head and neck cancer and noncancerous epithelium.coherent anti-stokes Raman scattering, convolutional neural networks, diagnostics, digital pathology, head and neck cancer, image analysis, second-harmonic generation, semantic segmentation, spectral histopathology, two-photon excited fluorescence

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“…Although progress has been made in research and treatment, the 5-year survival rate of TSCC patients is still low. Nowadays, biomarkers have become a research hotspot in tumor treatment and prognosis [6]. However, there is still no specific biomarker for recurrence and metastasis of tongue cancer.…”

Section: Introductionmentioning

confidence: 99%

NIT2 overexpression predicts poor prognosis in tongue squamous cell carcinoma patients

2020

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There is disputable on the role of nitrilase-like 2 (NIT2) in cancer. Its expression and its relationship with clinicopathological features in tongue squamous cell carcinoma (TSCC) are not yet clear. The purpose of this study is to investigate the expression of NIT2 in TSCC and its correlation with clinicopathological characteristics in TSCC patients. Through proteomic identification, we found that the protein NIT2 was related to the development of TSCC. q-PCR, western blot and immunohistochemistry techniques were applied to detect the expression of NIT2 in TSCC. The relationship between the expression of NIT2 and clinicopathological features was analyzed by Chi square tests. The results showed the expression of NIT2 in TSCC was significantly higher than that in normal tongue tissues (p < 0.05). Univariate and multivariate analysis showed that the positive expression of NIT2 and N classification were associated with decreased disease-free survival rate (DFS) and overall survival (OS) (p < 0.05). The results suggested that NIT2 is overexpressed in TSCC and NIT2 may be a potential therapeutic target for TSCC.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

Cited by 42 publications

References 35 publications

Raman exfoliative cytology for prognosis prediction in oral cancers: A proof of concept study

Raman exfoliative cytology for prognosis prediction in oral cancers: A proof of concept study

Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study

NIT2 overexpression predicts poor prognosis in tongue squamous cell carcinoma patients

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