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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8774306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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