Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy

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...

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Bibliographic Details
Published in:Scientific reports 2024-11, Vol.14 (1), p.28940-9, Article 28940
Main Authors: Tang, Zheng, Duckworth, Edward, Mora, Benjamin, Al - Sarireh, Bilal, Mortimer, Matthew, Roy, Debdulal
Format: Article
Language:English
Subjects:
639/705/1046
692/4028/67/2322
Accuracy
Biomarkers
Datasets
Discriminant Analysis
Early Detection of Cancer - methods
Fourier transforms
Humanities and Social Sciences
Humans
Machine learning
multidisciplinary
Pancreatic cancer
Pancreatic Neoplasms - diagnosis
Patients
Principal Component Analysis
Principal components analysis
Research methodology
Science
Science (multidisciplinary)
Spectroscopy
Spectroscopy, Fourier Transform Infrared - methods
Support Vector Machine
Support vector machines
Urine
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-79153-0