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Circulating Tumor Cell Identification Based on Deep Learning

As a major reason for tumor metastasis, circulating tumor cell (CTC) is one of the critical biomarkers for cancer diagnosis and prognosis. On the one hand, CTC count is closely related to the prognosis of tumor patients; on the other hand, as a simple blood test with the advantages of safety, low co...

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Autores principales: Guo, Zhifeng, Lin, Xiaoxi, Hui, Yan, Wang, Jingchun, Zhang, Qiuli, Kong, Fanlong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889528/
https://www.ncbi.nlm.nih.gov/pubmed/35252012
http://dx.doi.org/10.3389/fonc.2022.843879
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author Guo, Zhifeng
Lin, Xiaoxi
Hui, Yan
Wang, Jingchun
Zhang, Qiuli
Kong, Fanlong
author_facet Guo, Zhifeng
Lin, Xiaoxi
Hui, Yan
Wang, Jingchun
Zhang, Qiuli
Kong, Fanlong
author_sort Guo, Zhifeng
collection PubMed
description As a major reason for tumor metastasis, circulating tumor cell (CTC) is one of the critical biomarkers for cancer diagnosis and prognosis. On the one hand, CTC count is closely related to the prognosis of tumor patients; on the other hand, as a simple blood test with the advantages of safety, low cost and repeatability, CTC test has an important reference value in determining clinical results and studying the mechanism of drug resistance. However, the determination of CTC usually requires a big effort from pathologist and is also error-prone due to inexperience and fatigue. In this study, we developed a novel convolutional neural network (CNN) method to automatically detect CTCs in patients’ peripheral blood based on immunofluorescence in situ hybridization (imFISH) images. We collected the peripheral blood of 776 patients from Chifeng Municipal Hospital in China, and then used Cyttel to delete leukocytes and enrich CTCs. CTCs were identified by imFISH with CD45+, DAPI+ immunofluorescence staining and chromosome 8 centromeric probe (CEP8+). The sensitivity and specificity based on traditional CNN prediction were 95.3% and 91.7% respectively, and the sensitivity and specificity based on transfer learning were 97.2% and 94.0% respectively. The traditional CNN model and transfer learning method introduced in this paper can detect CTCs with high sensitivity, which has a certain clinical reference value for judging prognosis and diagnosing metastasis.
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spelling pubmed-88895282022-03-03 Circulating Tumor Cell Identification Based on Deep Learning Guo, Zhifeng Lin, Xiaoxi Hui, Yan Wang, Jingchun Zhang, Qiuli Kong, Fanlong Front Oncol Oncology As a major reason for tumor metastasis, circulating tumor cell (CTC) is one of the critical biomarkers for cancer diagnosis and prognosis. On the one hand, CTC count is closely related to the prognosis of tumor patients; on the other hand, as a simple blood test with the advantages of safety, low cost and repeatability, CTC test has an important reference value in determining clinical results and studying the mechanism of drug resistance. However, the determination of CTC usually requires a big effort from pathologist and is also error-prone due to inexperience and fatigue. In this study, we developed a novel convolutional neural network (CNN) method to automatically detect CTCs in patients’ peripheral blood based on immunofluorescence in situ hybridization (imFISH) images. We collected the peripheral blood of 776 patients from Chifeng Municipal Hospital in China, and then used Cyttel to delete leukocytes and enrich CTCs. CTCs were identified by imFISH with CD45+, DAPI+ immunofluorescence staining and chromosome 8 centromeric probe (CEP8+). The sensitivity and specificity based on traditional CNN prediction were 95.3% and 91.7% respectively, and the sensitivity and specificity based on transfer learning were 97.2% and 94.0% respectively. The traditional CNN model and transfer learning method introduced in this paper can detect CTCs with high sensitivity, which has a certain clinical reference value for judging prognosis and diagnosing metastasis. Frontiers Media S.A. 2022-02-16 /pmc/articles/PMC8889528/ /pubmed/35252012 http://dx.doi.org/10.3389/fonc.2022.843879 Text en Copyright © 2022 Guo, Lin, Hui, Wang, Zhang and Kong 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 Oncology
Guo, Zhifeng
Lin, Xiaoxi
Hui, Yan
Wang, Jingchun
Zhang, Qiuli
Kong, Fanlong
Circulating Tumor Cell Identification Based on Deep Learning
title Circulating Tumor Cell Identification Based on Deep Learning
title_full Circulating Tumor Cell Identification Based on Deep Learning
title_fullStr Circulating Tumor Cell Identification Based on Deep Learning
title_full_unstemmed Circulating Tumor Cell Identification Based on Deep Learning
title_short Circulating Tumor Cell Identification Based on Deep Learning
title_sort circulating tumor cell identification based on deep learning
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889528/
https://www.ncbi.nlm.nih.gov/pubmed/35252012
http://dx.doi.org/10.3389/fonc.2022.843879
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AT wangjingchun circulatingtumorcellidentificationbasedondeeplearning
AT zhangqiuli circulatingtumorcellidentificationbasedondeeplearning
AT kongfanlong circulatingtumorcellidentificationbasedondeeplearning