Literature DB >> 33566431

In vivo diagnosis of skin cancer with a portable Raman spectroscopic device.

Ivan A Bratchenko1, Lyudmila A Bratchenko1, Alexander A Moryatov2,3, Yulia A Khristoforova1, Dmitry N Artemyev1, Oleg O Myakinin1, Andrey E Orlov3, Sergey V Kozlov2,3, Valery P Zakharov1.   

Abstract

In this study, we performed in vivo diagnosis of skin cancer based on implementation of a portable low-cost spectroscopy setup combining analysis of Raman and autofluorescence spectra in the near-infrared region (800-915 nm). We studied 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable setup. The studies considered the patients examined by GPs in local clinics and directed to a specialized Oncology Dispensary with suspected skin cancer. Each sample was histologically examined after excisional biopsy. The spectra were classified with a projection on latent structures and discriminant analysis. To check the classification models stability, a 10-fold cross-validation was performed. We obtained ROC AUCs of 0.75 (0.71-0.79; 95% CI), 0.69 (0.63-0.76; 95% CI) and 0.81 (0.74-0.87; 95% CI) for classification of a) malignant and benign tumors, b) melanomas and pigmented tumors and c) melanomas and seborrhoeic keratosis, respectively. The positive and negative predictive values ranged from 20% to 52% and from 73% to 99%, respectively. The biopsy ratio varied from 0.92:1 to 4.08:1 (at sensitivity levels from 90% to 99%). The accuracy of automatic analysis with the proposed system is higher than the accuracy of GPs and trainees, and is comparable or less to the accuracy of trained dermatologists. The proposed approach may be combined with other optical techniques of skin lesion analysis, such as dermoscopy- and spectroscopy-based computer-assisted diagnosis systems to increase accuracy of neoplasms classification.
© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  Raman spectroscopy; autofluorescence; optical biopsy; skin cancer

Mesh:

Year:  2021        PMID: 33566431     DOI: 10.1111/exd.14301

Source DB:  PubMed          Journal:  Exp Dermatol        ISSN: 0906-6705            Impact factor:   3.960


  4 in total

1.  Comment on "Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks".

Authors:  Ivan A Bratchenko; Lyudmila A Bratchenko
Journal:  Lasers Med Sci       Date:  2022-09-27       Impact factor: 2.555

Review 2.  From Raman to SESORRS: moving deeper into cancer detection and treatment monitoring.

Authors:  Sian Sloan-Dennison; Stacey Laing; Duncan Graham; Karen Faulds
Journal:  Chem Commun (Camb)       Date:  2021-11-23       Impact factor: 6.222

3.  Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images.

Authors:  Marco La Salvia; Emanuele Torti; Raquel Leon; Himar Fabelo; Samuel Ortega; Francisco Balea-Fernandez; Beatriz Martinez-Vega; Irene Castaño; Pablo Almeida; Gregorio Carretero; Javier A Hernandez; Gustavo M Callico; Francesco Leporati
Journal:  Sensors (Basel)       Date:  2022-09-21       Impact factor: 3.847

Review 4.  Novel aspects of Raman spectroscopy in skin research.

Authors:  Dominique Lunter; Victoria Klang; Dorottya Kocsis; Zsófia Varga-Medveczky; Szilvia Berkó; Franciska Erdő
Journal:  Exp Dermatol       Date:  2022-07-25       Impact factor: 4.511

  4 in total

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