Machine Learning-derived Clinical Decision Algorithm for the Diagnosis of Hyperfunctioning Parathyroid Glands in Patients with Primary Hyperparathyroidism

Abstract

<title>Abstract</title> Purpose In patients with primary hyperparathyroidism (PHPT), planning for parathyroid surgery currently relies on the synthesis of clinical, laboratory, and imaging data by the clinician. Machine learning may assist in analyzing and integrating data to facilitate surgical decision making. To train and validate a <bold>Machine Learning</bold>-derived <bold>C</bold>linical <bold>D</bold>ecision <bold>A</bold>lgorithm (_MLCDA) for the diagnosis of abnormal hyperfunctioning parathyroid glands using preoperative variables. Methods Four hundred and fifty-eight consecutive patients were evaluated from a single-institution retrospective dataset of PHPT patients who underwent combined 4D-CT and sestamibi SPECT/CT (MIBI) with subsequent parathyroidectomy from February 2013 to September 2016. Study cohort was divided into training (first 400 patients) and validation sets (remaining 58 patients). Sixteen preoperative clinical, laboratory, and imaging variables were evaluated. A random forest algorithm was programmed to select the best predictor variables and output a single clinical decision algorithm with the highest performance (_MLCDA). The _MLCDA was trained to predict the probability of a hyperfunctioning vs. normal gland for each of four parathyroid glands in a patient. Reference standard was 4-quadrant location on operative reports and pathological confirmation of adenoma or hyperplasia. Accuracy of _MLCDA was prospectively validated. Results Of 16 variables, the algorithm selected 3 variables for optimal prediction: combined 4D-CT and MIBI using 1) sensitive reading and 2) specific reading, and 3) crossproduct of serum calcium and parathyroid hormone levels, and outputted a _MLCDA using five probability categories for hyperfunctioning glands. The _MLCDA demonstrated excellent accuracy for correct classification in the training set (4D-CT + MIBI: 0.91 [95%CI 0.89–0.92]), and in the validation set (4D-CT + MIBI: 0.90 [95%CI 0.86–0.94], 4D-CT: 0.88 [95%CI 0.84–0.92], and MIBI: 0.88 [95%CI 0.84–0.92]). Conclusion Machine learning generated a clinical decision algorithm that accurately diagnosed hyperfunctioning parathyroid gland through classification into probability categories, which can be implemented for improved preoperative planning and convey diagnostic certainty.

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