Machine learning-derived clinical decision algorithm for the diagnosis of hyperfunctioning parathyroid glands in patients with primary hyperparathyroidism.
Hyperparathyroidism
Machine learning
Parathyroid
Parathyroid 4D-CT
Sestamibi SPECT
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
06
05
2024
accepted:
11
09
2024
revised:
23
07
2024
medline:
30
10
2024
pubmed:
30
10
2024
entrez:
30
10
2024
Statut:
aheadofprint
Résumé
To train and validate machine learning-derived clinical decision algorithm ( This retrospective study included 458 consecutive primary hyperparathyroidism (PHPT) patients who underwent combined 4D-CT and sestamibi SPECT/CT (MIBI) with subsequent parathyroidectomy from February 2013 to September 2016. The study cohort was divided into training (first 400 patients) and validation sets (remaining 58 patients). Sixteen clinical, laboratory, and imaging variables were evaluated. A random forest algorithm selected the best predictor variables and generated a clinical decision algorithm with the highest performance ( Of 16 variables, the algorithm selected 3 variables for optimal prediction: combined 4D-CT and MIBI using (1) sensitive reading, (2) specific reading, and (3) cross-product of serum calcium and parathyroid hormone levels and outputted an Machine learning generated a clinical decision algorithm that accurately diagnosed hyperfunctioning parathyroid glands through classification into probability categories, which can be implemented for improved preoperative planning and convey diagnostic certainty. Question Can an
Identifiants
pubmed: 39476058
doi: 10.1007/s00330-024-11159-8
pii: 10.1007/s00330-024-11159-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to European Society of Radiology.
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