Machine learning-derived clinical decision algorithm for the diagnosis of hyperfunctioning parathyroid glands in patients with primary hyperparathyroidism Journal Article

  

Authors:	Yeh, R.; Kuo, J. H.; Huang, B.; Shobeiri, P.; Lee, J. A.; Tay, Y. K. D.; Tabacco, G.; Bilezikian, J. P.; Dercle, L.
Article Title:	Machine learning-derived clinical decision algorithm for the diagnosis of hyperfunctioning parathyroid glands in patients with primary hyperparathyroidism
Abstract:	PurposeTo train and validate machine learning-derived clinical decision algorithm (MLCDA) for the diagnosis of hyperfunctioning parathyroid glands using preoperative variables to facilitate surgical planning.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 (MLCDA). The MLCDA was trained to predict the probability of a hyperfunctioning vs normal gland for each of the four parathyroid glands in a patient. The reference standard was a four-quadrant location on operative reports and pathology. The accuracy of MLCDA was prospectively validated.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 MLCDA using five probability categories for hyperfunctioning glands. The MLCDA demonstrated excellent accuracy for correct classification in the training (4D-CT + MIBI: 0.91 [95% CI: 0.89-0.92]) and validation sets (4D-CT + MIBI: 0.90 [95% CI: 0.86-0.94].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 PointsQuestionCan anMLCDA use preoperative variables for the diagnosis of hyperfunctioning parathyroid glands to facilitate surgical planning?FindingsThe developedMLCDA demonstrated excellent accuracy for correct classification in the training (0.91 [95% CI: 0.89-0.92]) and validation sets (0.90 [95% CI: 0.86-0.94]).Clinical relevanceUsing standard preoperative variables, anMLCDA for diagnosing hyperfunctioning parathyroid glands can be implemented to improve preoperative parathyroid localization and included in radiology reports for surgical planning.Key PointsQuestionCan anMLCDA use preoperative variables for the diagnosis of hyperfunctioning parathyroid glands to facilitate surgical planning?FindingsThe developedMLCDA demonstrated excellent accuracy for correct classification in the training (0.91 [95% CI: 0.89-0.92]) and validation sets (0.90 [95% CI: 0.86-0.94]).Clinical relevanceUsing standard preoperative variables, anMLCDA for diagnosing hyperfunctioning parathyroid glands can be implemented to improve preoperative parathyroid localization and included in radiology reports for surgical planning.Key PointsQuestionCan anMLCDA use preoperative variables for the diagnosis of hyperfunctioning parathyroid glands to facilitate surgical planning?FindingsThe developedMLCDA demonstrated excellent accuracy for correct classification in the training (0.91 [95% CI: 0.89-0.92]) and validation sets (0.90 [95% CI: 0.86-0.94]).Clinical relevanceUsing standard preoperative variables, anMLCDA for diagnosing hyperfunctioning parathyroid glands can be implemented to improve preoperative parathyroid localization and included in radiology reports for surgical planning.
Keywords:	hyperparathyroidism; surgery; localization; 4-dimensional computed-tomography; preoperative; single; sestamibi; machine learning; parathyroid; 4d-ct; sestamibi spect; parathyroid 4d-ct; multigland disease
Journal Title:	European Radiology
Volume:	35
Issue:	3
ISSN:	0938-7994
Publisher:	Springer
Date Published:	2025-03-01
Start Page:	1325
End Page:	1336
Language:	English
ACCESSION:	WOS:001344982300001
DOI:	10.1007/s00330-024-11159-8
PROVIDER:	wos
PUBMED:	39476058
Notes:	Article -- Source: Wos

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