Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carci...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Cells |
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| Online Access: | https://www.mdpi.com/2073-4409/12/22/2645 |
| _version_ | 1797459745085325312 |
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| author | Concetta Esposito Mohammed Janneh Sara Spaziani Vincenzo Calcagno Mario Luca Bernardi Martina Iammarino Chiara Verdone Maria Tagliamonte Luigi Buonaguro Marco Pisco Lerina Aversano Andrea Cusano |
| author_facet | Concetta Esposito Mohammed Janneh Sara Spaziani Vincenzo Calcagno Mario Luca Bernardi Martina Iammarino Chiara Verdone Maria Tagliamonte Luigi Buonaguro Marco Pisco Lerina Aversano Andrea Cusano |
| author_sort | Concetta Esposito |
| collection | DOAJ |
| description | We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum. |
| first_indexed | 2024-03-09T16:55:44Z |
| format | Article |
| id | doaj.art-63cad3ea7059479892dbaeae1c26079a |
| institution | Directory Open Access Journal |
| issn | 2073-4409 |
| language | English |
| last_indexed | 2024-03-09T16:55:44Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cells |
| spelling | doaj.art-63cad3ea7059479892dbaeae1c26079a2023-11-24T14:35:27ZengMDPI AGCells2073-44092023-11-011222264510.3390/cells12222645Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman SpectroscopyConcetta Esposito0Mohammed Janneh1Sara Spaziani2Vincenzo Calcagno3Mario Luca Bernardi4Martina Iammarino5Chiara Verdone6Maria Tagliamonte7Luigi Buonaguro8Marco Pisco9Lerina Aversano10Andrea Cusano11Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, ItalyOptoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, ItalyOptoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, ItalyOptoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, ItalyCentro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, ItalyCentro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, ItalyCentro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, ItalyCentro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, ItalyCentro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, ItalyOptoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, ItalyCentro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, ItalyOptoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, ItalyWe investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.https://www.mdpi.com/2073-4409/12/22/2645liver cancer cellsmachine learningneural networksRaman spectroscopy |
| spellingShingle | Concetta Esposito Mohammed Janneh Sara Spaziani Vincenzo Calcagno Mario Luca Bernardi Martina Iammarino Chiara Verdone Maria Tagliamonte Luigi Buonaguro Marco Pisco Lerina Aversano Andrea Cusano Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy Cells liver cancer cells machine learning neural networks Raman spectroscopy |
| title | Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy |
| title_full | Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy |
| title_fullStr | Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy |
| title_full_unstemmed | Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy |
| title_short | Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy |
| title_sort | assessment of primary human liver cancer cells by artificial intelligence assisted raman spectroscopy |
| topic | liver cancer cells machine learning neural networks Raman spectroscopy |
| url | https://www.mdpi.com/2073-4409/12/22/2645 |
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