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Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning

Skin cancer, a common type of cancer, is generally divided into basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). The incidence of skin cancer has continued to increase worldwide in recent years. Early detection can greatly reduce its morbidity and mortality. Hyp...

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Autores principales: Liu, Lixin, Qi, Meijie, Li, Yanru, Liu, Yujie, Liu, Xing, Zhang, Zhoufeng, Qu, Junle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599813/
https://www.ncbi.nlm.nih.gov/pubmed/36290928
http://dx.doi.org/10.3390/bios12100790
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author Liu, Lixin
Qi, Meijie
Li, Yanru
Liu, Yujie
Liu, Xing
Zhang, Zhoufeng
Qu, Junle
author_facet Liu, Lixin
Qi, Meijie
Li, Yanru
Liu, Yujie
Liu, Xing
Zhang, Zhoufeng
Qu, Junle
author_sort Liu, Lixin
collection PubMed
description Skin cancer, a common type of cancer, is generally divided into basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). The incidence of skin cancer has continued to increase worldwide in recent years. Early detection can greatly reduce its morbidity and mortality. Hyperspectral microscopic imaging (HMI) technology can be used as a powerful tool for skin cancer diagnosis by reflecting the changes in the physical structure and microenvironment of the sample through the differences in the HMI data cube. Based on spectral data, this work studied the staging identification of SCC and the influence of the selected region of interest (ROI) on the staging results. In the SCC staging identification process, the optimal result corresponded to the standard normal variate transformation (SNV) for spectra preprocessing, the partial least squares (PLS) for dimensionality reduction, the hold-out method for dataset partition and the random forest (RF) model for staging identification, with the highest staging accuracy of 0.952 ± 0.014, and a kappa value of 0.928 ± 0.022. By comparing the staging results based on spectral characteristics from the nuclear compartments and peripheral regions, the spectral data of the nuclear compartments were found to contribute more to the accurate staging of SCC.
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spelling pubmed-95998132022-10-27 Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning Liu, Lixin Qi, Meijie Li, Yanru Liu, Yujie Liu, Xing Zhang, Zhoufeng Qu, Junle Biosensors (Basel) Article Skin cancer, a common type of cancer, is generally divided into basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). The incidence of skin cancer has continued to increase worldwide in recent years. Early detection can greatly reduce its morbidity and mortality. Hyperspectral microscopic imaging (HMI) technology can be used as a powerful tool for skin cancer diagnosis by reflecting the changes in the physical structure and microenvironment of the sample through the differences in the HMI data cube. Based on spectral data, this work studied the staging identification of SCC and the influence of the selected region of interest (ROI) on the staging results. In the SCC staging identification process, the optimal result corresponded to the standard normal variate transformation (SNV) for spectra preprocessing, the partial least squares (PLS) for dimensionality reduction, the hold-out method for dataset partition and the random forest (RF) model for staging identification, with the highest staging accuracy of 0.952 ± 0.014, and a kappa value of 0.928 ± 0.022. By comparing the staging results based on spectral characteristics from the nuclear compartments and peripheral regions, the spectral data of the nuclear compartments were found to contribute more to the accurate staging of SCC. MDPI 2022-09-25 /pmc/articles/PMC9599813/ /pubmed/36290928 http://dx.doi.org/10.3390/bios12100790 Text en © 2022 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
Liu, Lixin
Qi, Meijie
Li, Yanru
Liu, Yujie
Liu, Xing
Zhang, Zhoufeng
Qu, Junle
Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning
title Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning
title_full Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning
title_fullStr Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning
title_full_unstemmed Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning
title_short Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning
title_sort staging of skin cancer based on hyperspectral microscopic imaging and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599813/
https://www.ncbi.nlm.nih.gov/pubmed/36290928
http://dx.doi.org/10.3390/bios12100790
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