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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9599813 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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