LAUSR

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 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 optimization and 5-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-hours 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 dataset was split in 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 with identifying patients with MGD for whom 99mTc-Sestamibi-SPECT/CT failed to visualize all PTAs.