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, PatriciaKarolinska Institutet (author)

Primary hyperparathyroidism, a machine learning approach to identify multiglandular disease in patients with a single adenoma found at preoperative Sestamibi-SPECT/CT

Article/chapterEnglish2022

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Bioscientifica,2022
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LIBRIS-ID:oai:DiVA.org:uu-487237
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-487237URI
https://doi.org/10.1530/EJE-22-0206DOI
http://kipublications.ki.se/Default.aspx?queryparsed=id:150789933URI

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Language:English
Summary in:English

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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.

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Sundin, Anders,1954-Uppsala universitet,Radiologi(Swepub:uu)anderssu (author)
Nilsson, Inga-LenaKarolinska Institutet (author)
Gryback, PerKarolinska Institutet (author)
Sanchez-Crespo, AlejandroKarolinska Institutet (author)
Karolinska InstitutetRadiologi (creator_code:org_t)

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In:European Journal of Endocrinology: Bioscientifica187:2, s. 257-2630804-46431479-683X

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