ArticleBiomedical optics express2022
Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine learning-assisted SERS for rapid and accurate early screening of cervical cancer.Analytical and bioanalytical chemistry · 2026Article
- Refining Structural Analysis of Proteins: Automated Methods to Measure Transition Dipole Strength of Single Residues.The journal of physical chemistry. B · 2025Article
- The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence.Molecular biotechnology · 2025Review
- Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.BMC cancer · 2024Article
- HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images.Frontiers in oncology · 2024Article
- Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines.Lasers in medical science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.
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Registered trials
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