Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data

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Li, Fuqiang and Zhang, Yao and Wen, Yijing and Wang, Xiaomei and Yang, Hao (2025) Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data. Intelligent Oncology, 1 (4). pp. 299-307. ISSN 2950-2616 (https://doi.org/10.1016/j.intonc.2025.09.004)

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Abstract

Introduction Accurate preoperative localization of parathyroid glands (PGs) is crucial for patients with secondary hyperparathyroidism (sHPT) scheduled for parathyroidectomy (PTx). However, despite its importance, localization remains challenging since current preoperative imaging modalities for PG localization vary in sensitivity and accessibility. Materials and Methods In this study, we developed a deep learning model for PG identification using a dual-phase computed tomography (CT) dataset, including unenhanced CT and contrast-enhanced (CE) CT data, and validated the model’s sensitivity in clinical application. A retrospective study was conducted using 94 CT images taken from 47 patients (one plain CT image and one CE CT image per patient). The data were randomly assigned to a training set (38 cases, 76 CT images) and a test set (9 cases, 18 CT images) based on per-patient splits. A three-dimensional U-Net model was trained using the training set and then validated using the test set. An analysis was conducted to compare the model’s and clinicians’ sensitivity in detecting PGs based on various imaging modalities. An error analysis and an intermodal imaging complementarity analysis were performed to provide references for subsequent model enhancement and application. Results The dual-phase CT model identified PGs with a diagnostic sensitivity of 94.44%. This was significantly higher than the sensitivity achieved by clinicians using ultrasonography (61.11%, P = 0.0013) and CT (72.22%, P = 0.0238). Additionally, the sensitivity achieved using the dual-phase CT model was comparable to that achieved using single-photon emission computed tomography/CT with 99mTc-labeled methoxyisobutylisonitrile scintigraphy (Tc-MIBI SPECT/CT) (86.11%, P = 0.429). We also found that combining predictions from this model with other imaging modalities further improved the PG detection rates. Conclusions The study findings suggest that using a deep learning model with plain and CE CT data could improve PG identification prior to thyroidectomy or parathyroidectomy.

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