Predicting Neoplastic Gallbladder Polyps: The Role of Current Surgical Indications and Preoperative Images

预测胆囊息肉肿瘤:当前手术指征和术前影像的作用

阅读:1

Abstract

BACKGROUND/AIMS: Cholecystectomy for gallbladder (GB) polyps is performed primarily based on preoperative images. This study examined the accuracy of surgical indications commonly used in clinical practice for detecting neoplastic polyps and investigated further clues for predicting neoplastic polyps. METHODS: This retrospective study included 385 patients who underwent a cholecystectomy for GB polyps. The predictive performances of seven surgical indications were compared by fitting the receiver operating characteristic curves. Logistic regression analysis was used to identify the candidate variables associated with predicting neoplastic polyps. RESULTS: Neoplastic polyps were identified in 18.9% (n=62) of the 385 patients assessed. The neoplastic group contained more females than males, larger polyps, more frequent solitary lesions, and lower platelet counts than the non-neoplastic group. Current surgical indications revealed an unsatisfactory prediction for neoplastic polyps. The optimal cutoff polyp size for neoplastic polyps by ultrasound (US) was larger than by computed tomography (CT) (12 mm vs. 10 mm). The proportion of pathologic neoplastic polyps was higher when both US and CT images were used than that predicted using a single test. Logistic regression analysis revealed larger polyps, increasing age, female sex, and lower platelet count to be associated with neoplastic polyps. CONCLUSIONS: The current indications for cholecystectomy in GB polyps have a low predictive value for neoplastic lesions that can lead to overtreatment. Combining the polyp size from US and CT images may reduce unnecessary surgery. In addition, knowledge of the patient's age, sex, and platelet count could help make more selective surgical decisions for neoplastic polyps.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。