Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning
Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Cells |
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| Online Access: | https://www.mdpi.com/2073-4409/12/14/1909 |
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| author | Uraib Sharaha Daniel Hania Itshak Lapidot Ahmad Salman Mahmoud Huleihel |
| author_facet | Uraib Sharaha Daniel Hania Itshak Lapidot Ahmad Salman Mahmoud Huleihel |
| author_sort | Uraib Sharaha |
| collection | DOAJ |
| description | Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems. |
| first_indexed | 2024-03-11T01:11:55Z |
| format | Article |
| id | doaj.art-a63df672f8f147c7b602c60342a17cc9 |
| institution | Directory Open Access Journal |
| issn | 2073-4409 |
| language | English |
| last_indexed | 2024-03-11T01:11:55Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cells |
| spelling | doaj.art-a63df672f8f147c7b602c60342a17cc92023-11-18T18:46:54ZengMDPI AGCells2073-44092023-07-011214190910.3390/cells12141909Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine LearningUraib Sharaha0Daniel Hania1Itshak Lapidot2Ahmad Salman3Mahmoud Huleihel4Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, IsraelDepartment of Green Engineering, SCE—Shamoon College of Engineering, Beer-Sheva 84100, IsraelDepartment of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv 69107, IsraelDepartment of Physics, SCE—Shamoon College of Engineering, Beer-Sheva 84100, IsraelDepartment of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, IsraelCancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.https://www.mdpi.com/2073-4409/12/14/1909cancerRaman spectroscopymachine learningnormal fibroblast cells (NFC)NIH/3T3MBM (cancerous cells) |
| spellingShingle | Uraib Sharaha Daniel Hania Itshak Lapidot Ahmad Salman Mahmoud Huleihel Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning Cells cancer Raman spectroscopy machine learning normal fibroblast cells (NFC) NIH/3T3 MBM (cancerous cells) |
| title | Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning |
| title_full | Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning |
| title_fullStr | Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning |
| title_full_unstemmed | Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning |
| title_short | Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning |
| title_sort | early detection of pre cancerous and cancerous cells using raman spectroscopy based machine learning |
| topic | cancer Raman spectroscopy machine learning normal fibroblast cells (NFC) NIH/3T3 MBM (cancerous cells) |
| url | https://www.mdpi.com/2073-4409/12/14/1909 |
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