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

in European Journal of Endocrinology
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  • 1 Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
  • | 2 Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
  • | 3 Department of Radiology, Section for Molecular Imaging, Institution for Surgical Sciences, Uppsala University Hospital, Uppsala, Sweden
  • | 4 Department of Breast, Endocrine Tumours and Sarcoma, Karolinska University Hospital, Stockholm, Sweden
  • | 5 Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden

Correspondence should be addressed to A Sanchez-Crespo; Email: alejandro.sanchez-crespo@ki.se
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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 99mTc-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 99mTc-Sestamibi-SPECT/CT failed to visualise all PTAs.

 

     European Society of Endocrinology

Sept 2018 onwards Past Year Past 30 Days
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