Literature DB >> 30659473

In vivo detection of oral precancer using a fluorescence-based, in-house-fabricated device: a Mahalanobis distance-based classification.

Pavan Kumar1, Surendra Kumar Kanaujia2, Ashutosh Singh2, Asima Pradhan3,4.   

Abstract

In vivo detection of oral precancer has been carried out by a fluorescence-based, in-house-developed handheld probe on three groups: oral squamous cell carcinoma (OSCC), dysplastic (precancer), and control (normal). Measurements have been performed on a total of 141 patients and volunteers of different age groups. Excitation wavelength of 405 nm was used and fluorescence emission spectra were recorded in the scan range of 450.14 to 763.41 nm at very low incident power (122 μW) from different oral sites buccal mucosa (BM), lateral boarder of tongue (LBT), and dorsal surface of tongue (DST). Spectral profiles are found to vary among the three groups as well as among the different oral sites. Major and minor bands of flavin adenine dinucleotide (FAD) and porphyrins near 500, 634, 676, 689, and 703 nm have been obtained. Porphyrin contribution is found to be more dominant than the FAD in OSCC and dysplastic groups as compared to the control group. A better classification has been observed using the entire spectral range rather than restricting to individual bands, by application of principal component analysis (PCA), Mahalanobis distance model, and receiver operating characteristic analysis (ROC). ROC on Mahalanobis distance differentiates OSCC to normal, dysplastic to normal, and OSCC to dysplastic with sensitivities from 71% to 98%, 92% to 94% and 81% to 93% and specificities 91% to 100%, 86% to 100% and 79% to 97% for oral sites BM, LBT and DST. LBT and DST appear to be more sensitive to dysplasia detection as compared to BM.

Entities:  

Keywords:  Fluorescence spectroscopy; Mahalanobis distance model; Oral cancer; Oral sites; Principle component analysis; Receiver operating characteristic analysis

Mesh:

Year:  2019        PMID: 30659473     DOI: 10.1007/s10103-019-02720-9

Source DB:  PubMed          Journal:  Lasers Med Sci        ISSN: 0268-8921            Impact factor:   3.161


  4 in total

1.  Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy.

Authors:  Elvis Duran-Sierra; Shuna Cheng; Rodrigo Cuenca; Beena Ahmed; Jim Ji; Vladislav V Yakovlev; Mathias Martinez; Moustafa Al-Khalil; Hussain Al-Enazi; Yi-Shing Lisa Cheng; John Wright; Carlos Busso; Javier A Jo
Journal:  Cancers (Basel)       Date:  2021-09-23       Impact factor: 6.575

2.  Detection of inaccessible head and neck lesions using human saliva and fluorescence spectroscopy.

Authors:  Pavan Kumar
Journal:  Lasers Med Sci       Date:  2021-10-12       Impact factor: 2.555

3.  Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis.

Authors:  Ashley Ferro; Sanjeev Kotecha; Kathleen Fan
Journal:  Sci Rep       Date:  2022-08-13       Impact factor: 4.996

4.  Classification of oral salivary gland tumors based on texture features in optical coherence tomography images.

Authors:  Zihan Yang; Jianwei Shang; Chenlu Liu; Jun Zhang; Yanmei Liang
Journal:  Lasers Med Sci       Date:  2021-06-29       Impact factor: 3.161

  4 in total

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