Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy.
Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5)...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0268329 |
| _version_ | 1818047529682468864 |
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| author | Eiron John Lugtu Denise Bernadette Ramos Alliah Jen Agpalza Erika Antoinette Cabral Rian Paolo Carandang Jennica Elia Dee Angelica Martinez Julius Eleazar Jose Abegail Santillan Ruth Bangaoil Pia Marie Albano Rock Christian Tomas |
| author_facet | Eiron John Lugtu Denise Bernadette Ramos Alliah Jen Agpalza Erika Antoinette Cabral Rian Paolo Carandang Jennica Elia Dee Angelica Martinez Julius Eleazar Jose Abegail Santillan Ruth Bangaoil Pia Marie Albano Rock Christian Tomas |
| author_sort | Eiron John Lugtu |
| collection | DOAJ |
| description | Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics-area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues. |
| first_indexed | 2024-12-10T10:07:15Z |
| format | Article |
| id | doaj.art-940b7328dfc7439d9d2e469462b3196d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T10:07:15Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-940b7328dfc7439d9d2e469462b3196d2022-12-22T01:53:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01175e026832910.1371/journal.pone.0268329Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy.Eiron John LugtuDenise Bernadette RamosAlliah Jen AgpalzaErika Antoinette CabralRian Paolo CarandangJennica Elia DeeAngelica MartinezJulius Eleazar JoseAbegail SantillanRuth BangaoilPia Marie AlbanoRock Christian TomasGiven the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics-area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues.https://doi.org/10.1371/journal.pone.0268329 |
| spellingShingle | Eiron John Lugtu Denise Bernadette Ramos Alliah Jen Agpalza Erika Antoinette Cabral Rian Paolo Carandang Jennica Elia Dee Angelica Martinez Julius Eleazar Jose Abegail Santillan Ruth Bangaoil Pia Marie Albano Rock Christian Tomas Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy. PLoS ONE |
| title | Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy. |
| title_full | Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy. |
| title_fullStr | Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy. |
| title_full_unstemmed | Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy. |
| title_short | Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy. |
| title_sort | artificial neural network in the discrimination of lung cancer based on infrared spectroscopy |
| url | https://doi.org/10.1371/journal.pone.0268329 |
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