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原发性甲状旁腺功能亢进症的诊断定位研究。一种建议的算法。

Diagnostic localization studies for primary hyperparathyroidism. A suggested algorithm.

作者信息

Weinberger M S, Robbins K T

机构信息

Division of Otolaryngology-Head and Neck Surgery, University of California-San Diego Medical Center.

出版信息

Arch Otolaryngol Head Neck Surg. 1994 Nov;120(11):1187-9. doi: 10.1001/archotol.1994.01880350005001.

Abstract

OBJECTIVE

To assess the accuracy of preoperative imaging studies in primary hyperparathyroidism.

DESIGN

Retrospective review of all patients treated surgically for primary hyperparathyroidism at The University of California-San Diego Medical Center between January 1990 and May 1992. Results of preoperative imaging studies were compared with surgical and pathologic findings.

SETTING

The University of California-San Diego Medical Center, a primary care and referral center.

PARTICIPANTS

Twenty-eight patients were included in the study, and a total of 41 imaging studies were obtained.

OUTCOME MEASURES

The accuracy of preoperative imaging studies in correctly localizing the site of a parathyroid adenoma.

RESULTS

Ultrasound correctly identified the site of a solitary parathyroid adenoma in 82% of cases, and magnetic resonance imaging in 80% of cases. The adenomas that were not localized by ultrasound were correctly localized by magnetic resonance imaging. These findings are discussed within the context of recent trends in parathyroid surgery.

CONCLUSIONS

Based on the results and a review of the literature, a simple algorithm for the use of these imaging studies is proposed. Use of this algorithm will improve preoperative planning, and potentially reduce operative time and morbidity.

摘要

目的

评估术前影像学检查在原发性甲状旁腺功能亢进症中的准确性。

设计

回顾性分析1990年1月至1992年5月间在加利福尼亚大学圣地亚哥分校医学中心接受原发性甲状旁腺功能亢进症手术治疗的所有患者。将术前影像学检查结果与手术及病理结果进行比较。

地点

加利福尼亚大学圣地亚哥分校医学中心,一个初级保健和转诊中心。

参与者

28例患者纳入研究,共进行了41项影像学检查。

观察指标

术前影像学检查正确定位甲状旁腺腺瘤部位的准确性。

结果

超声在82%的病例中正确识别出孤立性甲状旁腺腺瘤的部位,磁共振成像在80%的病例中正确识别。超声未定位的腺瘤经磁共振成像正确定位。这些发现结合甲状旁腺手术的近期趋势进行了讨论。

结论

基于研究结果和文献回顾,提出了一种使用这些影像学检查的简单算法。使用该算法将改善术前规划,并可能减少手术时间和发病率。

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