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Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks

In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to d...

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Autores principales: Tomas, Rock Christian, Sayat, Anthony Jay, Atienza, Andrea Nicole, Danganan, Jannah Lianne, Ramos, Ma. Rollene, Fellizar, Allan, Notarte, Kin Israel, Angeles, Lara Mae, Bangaoil, Ruth, Santillan, Abegail, Albano, Pia Marie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791515/
https://www.ncbi.nlm.nih.gov/pubmed/35081148
http://dx.doi.org/10.1371/journal.pone.0262489
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author Tomas, Rock Christian
Sayat, Anthony Jay
Atienza, Andrea Nicole
Danganan, Jannah Lianne
Ramos, Ma. Rollene
Fellizar, Allan
Notarte, Kin Israel
Angeles, Lara Mae
Bangaoil, Ruth
Santillan, Abegail
Albano, Pia Marie
author_facet Tomas, Rock Christian
Sayat, Anthony Jay
Atienza, Andrea Nicole
Danganan, Jannah Lianne
Ramos, Ma. Rollene
Fellizar, Allan
Notarte, Kin Israel
Angeles, Lara Mae
Bangaoil, Ruth
Santillan, Abegail
Albano, Pia Marie
author_sort Tomas, Rock Christian
collection PubMed
description In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics–area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)–were averaged for comparison. The NN models were compared to six (6) machine learning models–logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)–for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C–OH C–OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample’s spectrum using NN.
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spelling pubmed-87915152022-01-27 Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks Tomas, Rock Christian Sayat, Anthony Jay Atienza, Andrea Nicole Danganan, Jannah Lianne Ramos, Ma. Rollene Fellizar, Allan Notarte, Kin Israel Angeles, Lara Mae Bangaoil, Ruth Santillan, Abegail Albano, Pia Marie PLoS One Research Article In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics–area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)–were averaged for comparison. The NN models were compared to six (6) machine learning models–logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)–for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C–OH C–OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample’s spectrum using NN. Public Library of Science 2022-01-26 /pmc/articles/PMC8791515/ /pubmed/35081148 http://dx.doi.org/10.1371/journal.pone.0262489 Text en © 2022 Tomas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tomas, Rock Christian
Sayat, Anthony Jay
Atienza, Andrea Nicole
Danganan, Jannah Lianne
Ramos, Ma. Rollene
Fellizar, Allan
Notarte, Kin Israel
Angeles, Lara Mae
Bangaoil, Ruth
Santillan, Abegail
Albano, Pia Marie
Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks
title Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks
title_full Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks
title_fullStr Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks
title_full_unstemmed Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks
title_short Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks
title_sort detection of breast cancer by atr-ftir spectroscopy using artificial neural networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791515/
https://www.ncbi.nlm.nih.gov/pubmed/35081148
http://dx.doi.org/10.1371/journal.pone.0262489
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