دورية أكاديمية
Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics.
| العنوان: | Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics. |
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| المؤلفون: | Oliveira MF; Metabonomics and Chemometrics Laboratory, Fundamental Chemistry Department, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife, Pernambuco, Brazil. marcio.felipe@ufpe.br.; Fundamental Chemistry Department, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife, Pernambuco, Brazil. marcio.felipe@ufpe.br., de Albuquerque Neto MC; Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil., Leite TS; Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil., Alves PAA; Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil., Lima SVC; Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil., Silva RO; Metabonomics and Chemometrics Laboratory, Fundamental Chemistry Department, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife, Pernambuco, Brazil. |
| المصدر: | Metabolomics : Official journal of the Metabolomic Society [Metabolomics] 2023 Dec 21; Vol. 20 (1), pp. 8. Date of Electronic Publication: 2023 Dec 21. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Country of Publication: United States NLM ID: 101274889 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-3890 (Electronic) Linking ISSN: 15733882 NLM ISO Abbreviation: Metabolomics Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York : Springer, c2006- |
| مواضيع طبية MeSH: | Chemometrics* , Prostatic Neoplasms*/diagnosis, Male ; Humans ; Metabolomics ; Magnetic Resonance Imaging ; Algorithms |
| مستخلص: | Introduction: In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue. Objectives: Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis. Methods: We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled. Results: Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy. Conclusion: The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1 H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis. (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.) |
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| فهرسة مساهمة: | Keywords: Biomarkers; Feature selection; Metabonomics; Overfitting; Prostatic neoplasms; Proton magnetic resonance spectroscopy |
| تواريخ الأحداث: | Date Created: 20231221 Date Completed: 20231222 Latest Revision: 20240229 |
| رمز التحديث: | 20240301 |
| DOI: | 10.1007/s11306-023-02067-x |
| PMID: | 38127222 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1573-3890 |
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| DOI: | 10.1007/s11306-023-02067-x |