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Publications

Journal Article Epublish

PubMed: 39941517 DOI: 10.3390/jcm14030846

Integration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection: A Complementary Diagnostic Tool.

J Clin Med 14(3)

Authors: Luís Ramalhete, Rúben Araújo, Miguel Bigotte Vieira, Emanuel Vigia, Inês Aires, Aníbal Ferreira, Cecília R C Calado

Affiliations

  • Blood and Transplantation Center of Lisbon, Instituto Português do Sangue e da Transplantação, Alameda das Linhas de Torres, No. 117, 1769-001 Lisbon, Portugal.
  • NOVA Medical School, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal.
  • iNOVA4Health-Advancing Precision Medicine, RG11: Reno-Vascular Diseases Group, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal.
  • Nephrology Department, Hospital Curry Cabral, Unidade Local de Saúde São José, 1049-001 Lisbon, Portugal.
  • Centro Hospitalar Universitário de Lisboa Central, Hepatobiliopancreatic and Transplantation Center-Curry Cabral Hospital, 1069-166 Lisbon, Portugal.
  • ISEL-Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, R. Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal.
  • Institute for Bioengineering and Biosciences (iBB), The Associate Laboratory Institute for Health and Bioeconomy-i4HB, Instituto Superior Técnico (IST), Universidade de Lisboa (UL), Av. Rovisco Pais, 1049-001 Lisbon, Portugal.

Abstract

: Kidney 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. : 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 and 2800-3400 cm. Model performance was assessed via area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. : 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. : 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.

MeSH

Serum, Classification, Kidney, Biopsy, Biomarkers, Machine Learning, Transplants, Learning, Allografts