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Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it h...

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Autores principales: Ji, Mingmei, Zhong, Jiahui, Xue, Runzhe, Su, Wenhua, Kong, Yawei, Fei, Yiyan, Ma, Jiong, Wang, Yulan, Mi, Lan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570424/
https://www.ncbi.nlm.nih.gov/pubmed/36232778
http://dx.doi.org/10.3390/ijms231911476
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author Ji, Mingmei
Zhong, Jiahui
Xue, Runzhe
Su, Wenhua
Kong, Yawei
Fei, Yiyan
Ma, Jiong
Wang, Yulan
Mi, Lan
author_facet Ji, Mingmei
Zhong, Jiahui
Xue, Runzhe
Su, Wenhua
Kong, Yawei
Fei, Yiyan
Ma, Jiong
Wang, Yulan
Mi, Lan
author_sort Ji, Mingmei
collection PubMed
description Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.
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spelling pubmed-95704242022-10-17 Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning Ji, Mingmei Zhong, Jiahui Xue, Runzhe Su, Wenhua Kong, Yawei Fei, Yiyan Ma, Jiong Wang, Yulan Mi, Lan Int J Mol Sci Article Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care. MDPI 2022-09-29 /pmc/articles/PMC9570424/ /pubmed/36232778 http://dx.doi.org/10.3390/ijms231911476 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
Ji, Mingmei
Zhong, Jiahui
Xue, Runzhe
Su, Wenhua
Kong, Yawei
Fei, Yiyan
Ma, Jiong
Wang, Yulan
Mi, Lan
Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning
title Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning
title_full Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning
title_fullStr Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning
title_full_unstemmed Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning
title_short Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning
title_sort early detection of cervical cancer by fluorescence lifetime imaging microscopy combined with unsupervised machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570424/
https://www.ncbi.nlm.nih.gov/pubmed/36232778
http://dx.doi.org/10.3390/ijms231911476
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