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

Sandqvist, Patricia; Sundin, Anders; Nilsson, Inga-Lena; Grybäck, Per; Sânchez-Crespo, Alejandro

doi:10.1530/EJE-22-0206

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

Author(s): Patricia Sandqvist , Anders Sundin , Inga-Lena Nilsson , Per Grybäck , Alejandro Sanchez-Crespo

Publication date (Electronic): June 24 2022

Journal: European Journal of Endocrinology

Publisher: Bioscientifica

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Abstract

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.

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Most cited references 22

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SMOTE: Synthetic Minority Over-sampling Technique

N. Chawla, K. W. Bowyer, L Hall … (2002)

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

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Hyperparathyroidism.

John Bilezikian, Leonardo Bandeira, Aliya Khan … (2018)

Primary hyperparathyroidism is a common endocrine disorder of calcium metabolism characterised by hypercalcaemia and elevated or inappropriately normal concentrations of parathyroid hormone. Almost always, primary hyperparathyroidism is due to a benign overgrowth of parathyroid tissue either as a single gland (80% of cases) or as a multiple gland disorder (15-20% of cases). Primary hyperparathyroidism is generally discovered when asymptomatic but the disease always has the potential to become symptomatic, resulting in bone loss and kidney stones. In countries where biochemical screening tests are not common, symptomatic primary hyperparathyroidism tends to predominate. Another variant of primary hyperparathyroidism has been described in which the serum calcium concentration is within normal range but parathyroid hormone is elevated in the absence of any obvious cause. Primary hyperparathyroidism can be cured by removal of the parathyroid gland or glands but identification of patients who are best advised to have surgery requires consideration of the guidelines that are regularly updated. Recommendations for patients who do not undergo parathyroid surgery include monitoring of serum calcium concentrations and bone density.