ArticleEuropean radiology2025
Machine learning-derived clinical decision algorithm for the diagnosis of hyperfunctioning parathyroid glands in patients with primary hyperparathyroidism.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Two-Stage Prediction Strategy for Primary Hyperparathyroidism.Diagnostics (Basel, Switzerland) · 2026Article
- Slice Level Classification of Parathyroid Adenoma Using Arterial Phase CT Images with Hybrid Light Attention Mechanism Based Residual Framework.Bioengineering (Basel, Switzerland) · 2026Article
- An explainable radiomics-based machine learning model for preoperative differentiation of parathyroid carcinoma and atypical tumors on ultrasound: a retrospective diagnostic study.Frontiers in endocrinology · 2025Article
- Precision stratified treatment of primary hyperparathyroidism based on multilevel emerging biomarkers.Frontiers in endocrinology · 2025Review
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9 authors.
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Abstract
purposeTo train and validate machine learning-derived clinical decision algorithm (
methodsThis 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 (
resultsOf 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
conclusionMachine 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. KEY POINTS: Question Can an
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