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

Fuqiang Li; Yao Zhang; Yijing Wen; Xiaomei Wang; Hao Yang

doi:10.1016/j.intonc.2025.09.004

您当前的位置：

首页 >

文章列表页 >

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

Research Article | 更新时间：2026-06-03

- Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data
- Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data
- 智能肿瘤学 2025年第4期页码：299-307
- 作者机构：
  
  1. Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine,Zhejiang,Hangzhou,China,310003
  2. Hangzhou PathoAI Technology Co., Ltd.,Zhejiang,Hangzhou,China,311100
  3. University of Strathclyde, Glasgow G1 1QN, United Kingdom
  4. Hangzhou Meiaiying Biomedical Technology Co., Ltd.,Zhejiang,Hangzhou,China,311100
- 作者简介：
- 基金信息：
- DOI：10.1016/j.intonc.2025.09.004
  中图分类号：
- 收稿：2025-06-05，
  
  网络首发：2025-09-26，
  
  纸质出版：2025-10-23
- 稿件说明：
移动端阅览
Fuqiang Li, Yao Zhang, Yijing Wen, 等. Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data[J]. 智能肿瘤学, 2025,(4):299-307.

Fuqiang Li, Yao Zhang, Yijing Wen, et al. Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data[J]. Intelligent Oncology, 2025, (4): 299-307.
Fuqiang Li, Yao Zhang, Yijing Wen, 等. Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data[J]. 智能肿瘤学, 2025,(4):299-307. DOI： 10.1016/j.intonc.2025.09.004.

Fuqiang Li, Yao Zhang, Yijing Wen, et al. Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data[J]. Intelligent Oncology, 2025, (4): 299-307. DOI： 10.1016/j.intonc.2025.09.004.

摘要

IntroductionAccurate preoperative localization of parathyroid glands (PGs) is crucial for patients with secondary hyperparathyroidism scheduled for parathyroidectomy. However

despite its importance

localization remains challenging since current preoperative imaging modalities for PG localization vary in sensitivity and accessibility.Materials and methodsIn 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 obtained 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 wa

s 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.ResultsThe 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%

 = 0.0013) and CT (72.22%

 = 0.0238). Additionally

the sensitivity achieved using the dual-phase CT model was comparable to that achieved using technetium-99m-methoxyisobutylisonitrile single-photon emission CT/CT (86.11%

 = 0.429). We also found that combining predictions from this model with other imaging modalities further improved the PG detection rates.ConclusionsThe study findings suggest that using a deep learning model with plain and CE CT data could improve PG identification prior to thyroidectomy or parathyroidectomy.

Abstract

IntroductionAccurate preoperative localization of parathyroid glands (PGs) is crucial for patients with secondary hyperparathyroidism scheduled for parathyroidectomy. However

despite its importance

localization remains challenging since current preoperative imaging modalities for PG localization vary in sensitivity and accessibility.Materials and methodsIn 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

76 CT images) and a test set (9 cases

18 CT imag

es) 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.ResultsThe 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%

 = 0.0013) and CT (72.22%

 = 0.0238). Additionally

the sensitivity achieved using the dual-phase CT model was comparable to that achieved using technetium-99m-methoxyisobutylisonitrile single-photon emission CT/CT (86.11%

关键词

Keywords

references

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

From morphology to function: Advances in multimodal imaging for pulmonary function prediction

AI-based diagnosis of clear-cell renal cell carcinoma based on non-contrast CT

Deep learning-based segmentation of small-volume brain metastases in lung cancer patients

Multimodal medical imaging AI for breast cancer diagnosis: A comprehensive review

Deep learning in abdominal organ segmentation: A review