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Integration of FTIR spectroscopy and machine learning for kidney allograft rejection: a complementary diagnostic tool

2025-01-27Research article Open access
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authorProfile.emailbiblioteca@isel.pt
datacite.subject.fosEngenharia e Tecnologia::Engenharia Química
dc.contributor.authorRamalhete, Luís
dc.contributor.authorAraújo, Rúben Alexandre Dinis
dc.contributor.authorBigotte Vieira, Miguel
dc.contributor.authorVigia, Emanuel
dc.contributor.authorAires, Inês
dc.contributor.authorFerreira, Aníbal
dc.contributor.authorCalado, Cecília
dc.date.accessioned2025-04-02T08:50:40Z
dc.date.available2025-04-02T08:50:40Z
dc.date.issued2025-01-27
dc.description.abstractKidney transplantation is a life-saving treatment for end-stage kidney disease, but allograft rejection remains a critical challenge, requiring accurate and timely diagnosis. The study aims to evaluate the integration of Fourier Transform Infrared (FTIR) spectroscopy and machine learning algorithms as a minimally invasive method to detect kidney allograft rejection and differentiate between T Cell-Mediated Rejection (TCMR) and Antibody-Mediated Rejection (AMR). Additionally, the goal is to discriminate these rejection types aiming to develop a reliable decision-making support tool. Methods: This retrospective study included 41 kidney transplant recipients and analyzed 81 serum samples matched to corresponding allograft biopsies. FTIR spectroscopy was applied to pre-biopsy serum samples, and Naïve Bayes classification models were developed to distinguish rejection from non-rejection and classify rejection types. Data preprocessing involved, e.g., atmospheric compensation, second derivative, and feature selection using Fast Correlation-Based Filter for spectral regions 600–1900 cm−1 and 2800–3400 cm−1. Model performance was assessed via area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The Naïve Bayes model achieved an AUC-ROC of 0.945 in classifying rejection versus non-rejection and AUC-ROC of 0.989 in distinguishing TCMR from AMR. Feature selection significantly improved model performance, identifying key spectral wavenumbers associated with rejection mechanisms. This approach demonstrated high sensitivity and specificity for both classification tasks. Conclusions: The integration of FTIR spectroscopy with machine learning may provide a promising, minimally invasive method for early detection and precise classification of kidney allograft rejection. Further validation in larger, more diverse populations is needed to confirm these findings’ reliability.eng
dc.description.sponsorshipNeproMD/ISEL/2020 - Instituto Politécnico de Lisboa (IPL)
dc.identifier.citationRamalhete, L., Araújo, R., Vieira, M. B., Vigia, E., Aires, I., Ferreira, A., & Calado, C. R. C. (2025). Integration of FTIR spectroscopy and machine learning for kidney allograft rejection: a complementary diagnostic tool. Journal of Clinical Medicine, 14(3), 846. https://doi.org/10.3390/jcm14030846
dc.identifier.doihttps://doi.org/10.3390/jcm14030846
dc.identifier.eissn2077-0383
dc.identifier.urihttp://hdl.handle.net/10400.21/21730
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationDSAIPA/DS/0117/2020 - Fundação para a Ciência e a Tecnologia (FCT)
dc.relation.hasversionhttps://www.mdpi.com/2077-0383/14/3/846
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectKidney allograft
dc.subjectRejection
dc.subjectBiomarkers
dc.subjectMachine learning
dc.subjectFTIR spectroscopy
dc.titleIntegration of FTIR spectroscopy and machine learning for kidney allograft rejection: a complementary diagnostic tooleng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage19
oaire.citation.issue3
oaire.citation.startPage1
oaire.citation.titleJournal of Clinical Medicine
oaire.citation.volume14
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAraújo
person.familyNameBigotte Vieira
person.familyNameVigia
person.familyNameCalado
person.givenNameRúben Alexandre Dinis
person.givenNameMiguel
person.givenNameEmanuel
person.givenNameCecília
person.identifier591378
person.identifier130332
person.identifier.ciencia-id9A18-BFDC-ED95
person.identifier.ciencia-idFF17-2C76-E2CC
person.identifier.ciencia-idE918-DF3E-1A17
person.identifier.ciencia-id9418-E320-3177
person.identifier.orcid0000-0002-9369-6486
person.identifier.orcid0000-0003-0528-2716
person.identifier.orcid0000-0002-4525-9062
person.identifier.orcid0000-0002-5264-9755
person.identifier.ridV-4629-2018
person.identifier.ridE-2102-2014
person.identifier.scopus-author-id57192837006
person.identifier.scopus-author-id6603163260
relation.isAuthorOfPublication9998e940-5e65-4661-8308-afcb56d5df01
relation.isAuthorOfPublication64821681-4391-45e3-a4da-2ef3869a6ead
relation.isAuthorOfPublicationb5171a62-8862-4ff8-b855-f6b165a95b89
relation.isAuthorOfPublicatione8577257-c64c-4481-9b2b-940fedb360cc
relation.isAuthorOfPublication.latestForDiscovery9998e940-5e65-4661-8308-afcb56d5df01

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