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Machine Learning-Enhanced Fiber-Optic Raman Spectroscopy for In Situ Oral Cancer Classification.

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Τίτλος: Machine Learning-Enhanced Fiber-Optic Raman Spectroscopy for In Situ Oral Cancer Classification.
Συγγραφείς: Li X; Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, China., Zhang S; Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang, China., Yang H; The Affiliated Anyang Tumor Hospital, Henan University of Science and Technology, Anyang, China., Wu H; Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, China., Yang K; Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, China., Zhu X; Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, China., Zhou S; Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, China., Zhang L; Department of Electrical and Electronic Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan., Hu J; College of Chemistry and Molecular Sciences, Wuhan University, Wuhan, China.
Πηγή: Journal of biophotonics [J Biophotonics] 2026 Oct; Vol. 19 (10), pp. e70373.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101318567 Publication Model: Print Cited Medium: Internet ISSN: 1864-0648 (Electronic) Linking ISSN: 1864063X NLM ISO Abbreviation: J Biophotonics Subsets: MEDLINE
Imprint Name(s): Original Publication: Weinheim : Wiley-VCH
Ιατρικοί όροι (MeSH): Mouth Neoplasms*/diagnostic imaging , Mouth Neoplasms*/classification , Mouth Neoplasms*/diagnosis , Spectrum Analysis, Raman* , Machine Learning* , Fiber Optic Technology*, Carcinoma, Squamous Cell/diagnostic imaging ; Humans ; Classification Algorithms ; Support Vector Machine ; Discriminant Analysis
Περίληψη: A portable Raman spectroscopy system was developed for in vivo diagnosis of oral squamous cell carcinoma (OSCC) in clinical settings. 46 patients with histopathologically confirmed OSCC were involved. The preprocessed dataset was used to train four supervised machine learning classifiers, support vector machine (SVM), k-nearest neighbors (KNN), ensemble algorithms (EA), and linear discriminant analysis (LDA), under a stratified ten-fold cross-validation scheme. Comparative evaluation demonstrated that SVM achieved perfect classification accuracy (100%), outperforming LDA (96.6%), EA (90.7%), and KNN (83.3%). SVM performance was evaluated against retained PC count to assess robustness and generalizability. The area under the receiver operating characteristic curve (AUC-ROC) remained above 80% with two PCs and increased to 96% when eight PCs were retained. These findings underscore the clinical translational potential of machine learning driven Raman spectroscopy, with high discriminative power and generalizability for oral cancer.
(© 2026 Wiley‐VCH GmbH.)
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Grant Information: 52102310015 the Scientific and Technological Development in Henan Province; 16A140001 the Key Scientific Research Universities Program Foundation of Henan Province; 2023GZ55 the Science and Technology Plan Project of Huzhou
Contributed Indexing: Keywords: fiber Raman spectroscopy; in vivo diagnosis; machine learning; oral cancer
Entry Date(s): Date Created: 20261009 Date Completed: 20261009 Latest Revision: 20261024
Update Code: 20261024
DOI: 10.1002/jbio.70373
PMID: 42852622
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1864-0648
DOI:10.1002/jbio.70373