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Machine learning analysis of contrast-enhanced ultrasound (CEUS) for the diagnosis of acute graft dysfunction in kidney transplant recipients.
Moisoiu, Tudor; Elec, Alina Daciana; Muntean, Adriana Milena; Badea, Alexandru Florin; Budusan, Anca; Stancu, Bogdan; Iacob, Gheorghița; Oana, Antal; Andries, Alexandra; Zaro, Razvan; Socaciu, Mihai A; Badea, Radu Ion; Oniscu, Gabriel C; Elec, Florin Ioan.
Afiliación
  • Moisoiu T; Clinical Institute of Urology and Renal Transplantation "Iuliu Hațieganu" University of Medicine and Pharmacy, Biomed Data Analytics SRL, Cluj-Napoca.
  • Elec AD; Clinical Institute of Urology and Renal Transplantation, Cluj-Napoca.
  • Muntean AM; Clinical Institute of Urology and Renal Transplantation, Cluj-Napoca.
  • Badea AF; "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca.
  • Budusan A; Children's Clinical Emergency Hospital, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca.
  • Stancu B; Second Surgical Department, Cluj County Emergency Clinical Hospital, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca.
  • Iacob G; Clinical Institute of Urology and Renal Transplantation, Cluj-Napoca.
  • Oana A; Clinical Institute of Urology and Renal Transplantation, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca.
  • Andries A; Department of Radiology, "Ion Chiricuta" Oncology Institute, Cluj-Napoca.
  • Zaro R; Department of Gastroenterology, Clinical Hospital of Infectious Diseases, Cluj-Napoca.
  • Socaciu MA; "Octavian Fodor" Regional Institute of Gastroenterology and Hepatology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca.
  • Badea RI; "Octavian Fodor" Regional Institute of Gastroenterology and Hepatology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca. rbadea@umfcluj.ro.
  • Oniscu GC; Division of Transplantation, CLINTEC, Karolinska Institutet, Stockholm.
  • Elec FI; Clinical Institute of Urology and Renal Transplantation, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca.
Med Ultrason ; 2024 Sep 04.
Article en En | MEDLINE | ID: mdl-39231287
ABSTRACT

AIM:

The aim of the study was to develop machine learning algorithms (MLA) for diagnosing acute graft dysfunction (AGD) in kidney transplant recipients based on contrast-enhanced ultrasound (CEUS) analysis of the graft.Materials and 

methods:

This prospective study involved 71 patients with kidney transplant undergoing CEUS during follow-up. AGD wasdefined as an increase in serum creatinine levels of at least 25% compared to the baseline of the last three months. The control group consisted of patients with stable kidney graft function (SGF). The top five CEUS parameters that achieved the best discrimination between the AGD and SGF groups were selected based on ANOVA testing and then employed as input for training MLA (naïve Bayes (NB), k-nearest neighbors (k-NN), and logistic regression (LR)). The models were validated by leave-one-out cross-validation.

RESULTS:

Among the 111 CEUS analyses, 21 corresponded to the AGD group and 90 to the SGF group. CEUS analyses yielded 44 parameters, from which five were selected the wash out rate in segmental arteries,time to peak in segmental arteries, medullary mean transit time, renal mean transit time, and medullary time to fall. These five parameters were employed as input for MLA, yielding an AUROC of 0.68 for NB and k-NN and 0.72 for LR. The inclusion of graft survival in the MLA significantly improved discrimination accuracy, yielding an AUROC of 0.79 for NB, 0.76 for k-NN,and 0.81 for LR.

CONCLUSIONS:

The use of MLA represents a promising strategy for analyzing CEUS-derived parameters in the setting AGD.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Med Ultrason Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Rumanía
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Med Ultrason Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Rumanía