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Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer

The diagnosis and treatment of non-melanoma skin cancer remain urgent problems. Histological examination of biopsy material—the gold standard of diagnosis—is an invasive procedure that requires a certain amount of time to perform. The ability to detect abnormal cells using fluorescence spectroscopy...

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Autores principales: Andreeva, Victoriya, Aksamentova, Evgeniia, Muhachev, Andrey, Solovey, Alexey, Litvinov, Igor, Gusarov, Alexey, Shevtsova, Natalia N., Kushkin, Dmitry, Litvinova, Karina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774306/
https://www.ncbi.nlm.nih.gov/pubmed/35054239
http://dx.doi.org/10.3390/diagnostics12010072
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author Andreeva, Victoriya
Aksamentova, Evgeniia
Muhachev, Andrey
Solovey, Alexey
Litvinov, Igor
Gusarov, Alexey
Shevtsova, Natalia N.
Kushkin, Dmitry
Litvinova, Karina
author_facet Andreeva, Victoriya
Aksamentova, Evgeniia
Muhachev, Andrey
Solovey, Alexey
Litvinov, Igor
Gusarov, Alexey
Shevtsova, Natalia N.
Kushkin, Dmitry
Litvinova, Karina
author_sort Andreeva, Victoriya
collection PubMed
description The diagnosis and treatment of non-melanoma skin cancer remain urgent problems. Histological examination of biopsy material—the gold standard of diagnosis—is an invasive procedure that requires a certain amount of time to perform. The ability to detect abnormal cells using fluorescence spectroscopy (FS) has been shown in many studies. This technique is rapidly expanding due to its safety, relative cost-effectiveness, and efficiency. However, skin lesion FS-based diagnosis is challenging due to a number of single overlapping spectra emitted by fluorescent molecules, making it difficult to distinguish changes in the overall spectrum and the molecular basis for it. We applied deep learning (DL) algorithms to quantitatively assess the ability of FS to differentiate between pathologies and normal skin. A total of 137 patients with various forms of primary and recurrent basal cell carcinoma (BCC) were observed by a multispectral laser-based device with a built-in neural network (NN) “DSL-1”. We measured the fluorescence spectra of suspected non-melanoma skin cancers and compared them with “normal” skin spectra. These spectra were input into DL algorithms to determine whether the skin is normal, pigmented normal, benign, or BCC. The preoperative differential AI-driven fluorescence diagnosis method correctly predicted the BCC lesions. We obtained an average sensitivity of 62% and average specificity of 83% in our experiments. Thus, the presented “DSL-1” diagnostic device can be a viable tool for the real-time diagnosis and guidance of non-melanoma skin cancer resection.
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spelling pubmed-87743062022-01-21 Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer Andreeva, Victoriya Aksamentova, Evgeniia Muhachev, Andrey Solovey, Alexey Litvinov, Igor Gusarov, Alexey Shevtsova, Natalia N. Kushkin, Dmitry Litvinova, Karina Diagnostics (Basel) Article The diagnosis and treatment of non-melanoma skin cancer remain urgent problems. Histological examination of biopsy material—the gold standard of diagnosis—is an invasive procedure that requires a certain amount of time to perform. The ability to detect abnormal cells using fluorescence spectroscopy (FS) has been shown in many studies. This technique is rapidly expanding due to its safety, relative cost-effectiveness, and efficiency. However, skin lesion FS-based diagnosis is challenging due to a number of single overlapping spectra emitted by fluorescent molecules, making it difficult to distinguish changes in the overall spectrum and the molecular basis for it. We applied deep learning (DL) algorithms to quantitatively assess the ability of FS to differentiate between pathologies and normal skin. A total of 137 patients with various forms of primary and recurrent basal cell carcinoma (BCC) were observed by a multispectral laser-based device with a built-in neural network (NN) “DSL-1”. We measured the fluorescence spectra of suspected non-melanoma skin cancers and compared them with “normal” skin spectra. These spectra were input into DL algorithms to determine whether the skin is normal, pigmented normal, benign, or BCC. The preoperative differential AI-driven fluorescence diagnosis method correctly predicted the BCC lesions. We obtained an average sensitivity of 62% and average specificity of 83% in our experiments. Thus, the presented “DSL-1” diagnostic device can be a viable tool for the real-time diagnosis and guidance of non-melanoma skin cancer resection. MDPI 2021-12-29 /pmc/articles/PMC8774306/ /pubmed/35054239 http://dx.doi.org/10.3390/diagnostics12010072 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Andreeva, Victoriya
Aksamentova, Evgeniia
Muhachev, Andrey
Solovey, Alexey
Litvinov, Igor
Gusarov, Alexey
Shevtsova, Natalia N.
Kushkin, Dmitry
Litvinova, Karina
Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer
title Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer
title_full Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer
title_fullStr Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer
title_full_unstemmed Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer
title_short Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer
title_sort preoperative ai-driven fluorescence diagnosis of non-melanoma skin cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774306/
https://www.ncbi.nlm.nih.gov/pubmed/35054239
http://dx.doi.org/10.3390/diagnostics12010072
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