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Primary hyperparath...
Primary hyperparathyroidism, a machine learning approach to identify multiglandular disease in patients with a single adenoma found at preoperative Sestamibi-SPECT/CT
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- Sandqvist, Patricia (författare)
- Karolinska Institutet
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- Sundin, Anders, 1954- (författare)
- Uppsala universitet,Radiologi
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- Nilsson, Inga-Lena (författare)
- Karolinska Institutet
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- Gryback, Per (författare)
- Karolinska Institutet
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- Sanchez-Crespo, Alejandro (författare)
- Karolinska Institutet
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(creator_code:org_t)
- Bioscientifica, 2022
- 2022
- Engelska.
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Ingår i: European Journal of Endocrinology. - : Bioscientifica. - 0804-4643 .- 1479-683X. ; 187:2, s. 257-263
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Objective: Successful preoperative image localisation of all parathyroid adenomas (PTA) in patients with primary hyperparathyroidism (pHPT) and multiglandular disease (MGD) remains challenging. We investigate whether a machine learning classifier (MLC) could predict the presence of overlooked PTA at preoperative localisation with Tc-99m-Sestamibi-SPECT/CT in MGD patients.Design: This study is a retrospective study from a single tertiary referral hospital initially including 349 patients with biochemically confirmed pHPT and cured after surgical parathyroidectomy.Methods: A classification ensemble of decision trees with Bayesian hyperparameter optimisation and five-fold cross-validation was trained with six predictor variables: the preoperative plasma concentrations of parathyroid hormone, total calcium and thyroid-stimulating hormone, the serum concentration of ionised calcium, the 24-h urine calcium and the histopathological weight of the localised PTA at imaging. Two response classes were defined: patients with single-gland disease (SGD) correctly localised at imaging and MGD patients in whom only one PTA was localised on imaging. The data set was split into 70% for training and 30% for testing. The MLC was also tested on a subset of the original data based on CT image-derived PTA weights.Results: The MLC achieved an overall accuracy at validation of 90% with an area under the cross-validation receiver operating characteristic curve of 0.9. On test data, the MLC reached a 72% true-positive prediction rate for MGD patients and a misclassification rate of 6% for SGD patients. Similar results were obtained in the testing set with image-derived PTA weight.Conclusions: Artificial intelligence can aid in identifying patients with MGD for whom Tc-99m-Sestamibi-SPECT/CT failed to visualise all PTAs.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Endokrinologi och diabetes (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Endocrinology and Diabetes (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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