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Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images
The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recu...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326547/ https://www.ncbi.nlm.nih.gov/pubmed/37426334 http://dx.doi.org/10.3389/fchem.2023.1213981 |
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author | Sorrentino, Salvatore Manetti, Francesco Bresci, Arianna Vernuccio, Federico Ceconello, Chiara Ghislanzoni, Silvia Bongarzone, Italia Vanna, Renzo Cerullo, Giulio Polli, Dario |
author_facet | Sorrentino, Salvatore Manetti, Francesco Bresci, Arianna Vernuccio, Federico Ceconello, Chiara Ghislanzoni, Silvia Bongarzone, Italia Vanna, Renzo Cerullo, Giulio Polli, Dario |
author_sort | Sorrentino, Salvatore |
collection | PubMed |
description | The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recurrence. Its detection requires multiple assays, and nonlinear optical (NLO) microscopy provides a solution for fast, non-invasive, and label-free detection of therapy-induced senescent cells. Here, we develop several deep learning architectures to perform binary classification between senescent and proliferating human cancer cells using NLO microscopy images and we compare their performances. As a result of our work, we demonstrate that the most performing approach is the one based on an ensemble classifier, that uses seven different pre-trained classification networks, taken from literature, with the addition of fully connected layers on top of their architectures. This approach achieves a classification accuracy of over 90%, showing the possibility of building an automatic, unbiased senescent cells image classifier starting from multimodal NLO microscopy data. Our results open the way to a deeper investigation of senescence classification via deep learning techniques with a potential application in clinical diagnosis. |
format | Online Article Text |
id | pubmed-10326547 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103265472023-07-08 Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images Sorrentino, Salvatore Manetti, Francesco Bresci, Arianna Vernuccio, Federico Ceconello, Chiara Ghislanzoni, Silvia Bongarzone, Italia Vanna, Renzo Cerullo, Giulio Polli, Dario Front Chem Chemistry The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recurrence. Its detection requires multiple assays, and nonlinear optical (NLO) microscopy provides a solution for fast, non-invasive, and label-free detection of therapy-induced senescent cells. Here, we develop several deep learning architectures to perform binary classification between senescent and proliferating human cancer cells using NLO microscopy images and we compare their performances. As a result of our work, we demonstrate that the most performing approach is the one based on an ensemble classifier, that uses seven different pre-trained classification networks, taken from literature, with the addition of fully connected layers on top of their architectures. This approach achieves a classification accuracy of over 90%, showing the possibility of building an automatic, unbiased senescent cells image classifier starting from multimodal NLO microscopy data. Our results open the way to a deeper investigation of senescence classification via deep learning techniques with a potential application in clinical diagnosis. Frontiers Media S.A. 2023-06-23 /pmc/articles/PMC10326547/ /pubmed/37426334 http://dx.doi.org/10.3389/fchem.2023.1213981 Text en Copyright © 2023 Sorrentino, Manetti, Bresci, Vernuccio, Ceconello, Ghislanzoni, Bongarzone, Vanna, Cerullo and Polli. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Chemistry Sorrentino, Salvatore Manetti, Francesco Bresci, Arianna Vernuccio, Federico Ceconello, Chiara Ghislanzoni, Silvia Bongarzone, Italia Vanna, Renzo Cerullo, Giulio Polli, Dario Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images |
title | Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images |
title_full | Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images |
title_fullStr | Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images |
title_full_unstemmed | Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images |
title_short | Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images |
title_sort | deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326547/ https://www.ncbi.nlm.nih.gov/pubmed/37426334 http://dx.doi.org/10.3389/fchem.2023.1213981 |
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