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Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics.
Oliveira, Márcio Felipe; de Albuquerque Neto, Moacir Cavalcante; Leite, Thiago Siqueira; Alves, Paulo André Araújo; Lima, Salvador Vilar Correia; Silva, Ricardo Oliveira.
Afiliação
  • 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.
  • de Albuquerque Neto MC; Fundamental Chemistry Department, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife, Pernambuco, Brazil. marcio.felipe@ufpe.br.
  • 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; Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil.
Metabolomics ; 20(1): 8, 2023 Dec 21.
Article em En | MEDLINE | ID: mdl-38127222
ABSTRACT

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. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Quimiometria Limite: Humans / Male Idioma: En Revista: Metabolomics Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Quimiometria Limite: Humans / Male Idioma: En Revista: Metabolomics Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos