Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy
Abstract This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learning pipeline comprising class-specific principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM)...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-79153-0 |
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| _version_ | 1850127928393728000 |
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| author | Zheng Tang Edward Duckworth Benjamin Mora Bilal Al - Sarireh Matthew Mortimer Debdulal Roy |
| author_facet | Zheng Tang Edward Duckworth Benjamin Mora Bilal Al - Sarireh Matthew Mortimer Debdulal Roy |
| author_sort | Zheng Tang |
| collection | DOAJ |
| description | Abstract This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learning pipeline comprising class-specific principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) in both real patient and synthetic data. By conducting separate PCA on cancerous and non-cancerous samples and integrating the projections prior to LDA and SVM classification, we demonstrate significantly improved diagnostic accuracy compared to traditional methods. This methodology not only enhances predictive performance but also offers deeper insights into the influence of molecular spectra on model efficacy. Our findings, validated on real patient data, suggest a promising avenue for developing non-invasive, accurate diagnostic tools for early-stage pancreatic cancer detection. |
| format | Article |
| id | doaj-art-e5924e719594478ba9a154ce7eb97b92 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e5924e719594478ba9a154ce7eb97b922025-08-20T02:33:31ZengNature PortfolioScientific Reports2045-23222024-11-011411910.1038/s41598-024-79153-0Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopyZheng Tang0Edward Duckworth1Benjamin Mora2Bilal Al - Sarireh3Matthew Mortimer4Debdulal Roy5Department of Computer Science and Mathematics, Swansea UniversityConnectomX LtdDepartment of Computer Science and Mathematics, Swansea UniversityMorriston Hospital, Heol Maes EglwysMorriston Hospital, Heol Maes EglwysDepartment of Chemistry, Swansea UniversityAbstract This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learning pipeline comprising class-specific principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) in both real patient and synthetic data. By conducting separate PCA on cancerous and non-cancerous samples and integrating the projections prior to LDA and SVM classification, we demonstrate significantly improved diagnostic accuracy compared to traditional methods. This methodology not only enhances predictive performance but also offers deeper insights into the influence of molecular spectra on model efficacy. Our findings, validated on real patient data, suggest a promising avenue for developing non-invasive, accurate diagnostic tools for early-stage pancreatic cancer detection.https://doi.org/10.1038/s41598-024-79153-0 |
| spellingShingle | Zheng Tang Edward Duckworth Benjamin Mora Bilal Al - Sarireh Matthew Mortimer Debdulal Roy Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy Scientific Reports |
| title | Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy |
| title_full | Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy |
| title_fullStr | Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy |
| title_full_unstemmed | Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy |
| title_short | Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy |
| title_sort | advancing frontline early pancreatic cancer detection using within class feature extraction in ftir spectroscopy |
| url | https://doi.org/10.1038/s41598-024-79153-0 |
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