Mathematical model for preoperative differential diagnosis for the parathyroid neoplasms.

Background And Objective: Preoperative diagnosis of parathyroid carcinoma (PC) is critical for the determination of the scope of surgical intervention. Nowadays, specific diagnostic markers for differentiation of PC and benign tumors are unknown, and less than half of patients with PC undergo necessary surgery. The aim of this study was to develop the instrument for preoperative diagnosis of PC.

Methods: A multi-center retrospective study included 242 patients with primary hyperparathyroidism: 50 patients with PC, 30 with аtypical adenoma (AA), and 162 with adenoma of the parathyroid glands.

Results: Patients with PC and AA had higher levels of PTH, ionized and albumin-corrected calcium, ALP, volume and the largest diameter of neoplasm, and the higher frequency of GFR decrease less than 60 ml/min/1.73 m compared to patients with adenoma. The frequency of low-energy fractures was higher in the carcinoma group versus the adenoma group (32% vs 8%). Heterogeneous structure and indefinite contour of glands detected by US were more typical for PC than for AA and adenomas. The mathematical model was developed using CatBoost gradient boosting algorithm for the noninvasive preoperative differential diagnosis of PC, AA, and adenoma.

Conclusions: Model can predict adenoma with PPV 100% and PC with PPV 81-92%. Using model clinicians could plan extended resection for PC and selective parathyroidectomy for adenoma. If AA is predicted, he has to make a decision on the choice of the necessary volume of PTE based on his experience, because AA are the zone of uncertainty.