Prediction of multiglandular parathyroid disease in primary hyperparathyroidism using ultrasound and clinical features - Luo, Yanwen; Jin, Siqi; He, Yudi; Fang, Song; Wang, Ou; Liao, Quan; Li, Jianchu; Jiang, Yuxin; Zhu, Qingli; Liu, He (Frontiers in Endocrinology, 2023)
Background: Identification of multigland disease (MGD) in primary hyperparathyroidism (PHPT) patients is essential for minimally invasive surgical decision-making. Objective: To develop a nomogram based on US findings and clinical factors to predict MGD in PHPT patients. Materials and Methods: Patients with PHPT who underwent surgery between March 2021 and January 2022 were consecutively enrolled. Biochemical and clinicopathologic data were recorded. US images were analyzed to extract US features. Logistic regression analyses were used to identify the risk factors for MGD. The nomogram was constructed based on the factors. Nomogram performance was evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, the Hosmer–Lemeshow test, and decision curve analysis. Results: A total of 102 PHPT patients were included. 82 (80.4%) had the single-gland disease (SGD) and 20 (19.6%) had MGD. Using multivariate analysis, the MGD was positively correlated with age (OR = 1.033, 95% CI = 0.190-4.047), PTH level (OR = 1.001, 95% CI = 1.000–1.002), MEN-1 (OR = 29.730, 95% CI = 3.089-836.785), US size (OR = 1.198, 95% CI = 0.647–2.088) and US texture (cystic-solid) (OR = 5.357, 95% CI = 0.499–62.912). And negatively correlated with gender (OR = 0.985, 95% CI = 0.190–4.047), calcium level (OR = 0.453, 95% CI = 0.070–2.448), and symptoms(yes) (OR = 0.935, 95% CI = 0.257–13.365). The nomogram showed good discrimination with an AUC of 0.77 (0.68-0.85) and good agreement for predicting MGD in PHPT patients. And 65 points was recommended as a cut-off value with a specificity of 0.94 and a sensitivity of 0.50. Conclusion: US provided useful features for evaluating MGD. Combining the US and clinical features in a nomogram showed good diagnostic performance for predicting MGD.