梨状肌综合征
医学
磁共振成像
腰椎
腰痛
冠状面
核医学
坐骨神经痛
解剖
外科
放射科
病理
替代医学
作者
Chee Kin Lim,Hyung‐Bok Park,Young Uk Kim
出处
期刊:Medicine
[Ovid Technologies (Wolters Kluwer)]
日期:2025-02-21
卷期号:104 (8): e41689-e41689
被引量:3
标识
DOI:10.1097/md.0000000000041689
摘要
Piriformis syndrome (PS) is a condition in which the piriformis muscle characterized by buttock and hip pain. PS is frequently overlooked in clinical field because its symptoms are similar to that of primary sacral dysfunction, or lumbar radiculopathy. Thus exact diagnosis is very important. The piriformis muscle cross-sectional area (PMCSA) has not yet been proven to be an independent risk factor for the diagnosis of PS. The present study analyze the relationship between the PMCSA and PS. We hypothesized that PMCSA is a key diagnostic parameter in the PS. Both PMCSA and piriformis muscle thickness (PMT) samples were obtained from 30 patients with PS, and from 30 healthy individuals who underwent hip magnetic resonance imaging (H-MRI) with no evidence of PS. T1W H-MRI images were obtained. We investigated the PMCSA and PMT on H-MRI using a PACS system. The PMCSA was measured in coronal sections of the entire image by contour drawing. The PMT was measured primarily based on the hypertrophied piriformis muscle. Both PMCSA and PMT were significantly associated with PS, but PMCSA was measured as a much more sensitive parameter. Therefore, to evaluate patients with PS, physicians should examine PMCSA more carefully than PMT. The average PMCSA was 564.36 ± 121.61 mm 2 in the normal group and 736.88 ± 168.87 mm 2 in the PS group. The average PMT was 13.83 ± 2.61 mm in the control group and 15.99 ± 2.34 mm in the PS group. PS group had significantly higher PMCSA ( P ≤.001) and PMT ( P ≤.001). Regarding the validity of both PMCSA and PMT as predictors of PS, receiver operating characteristic curve analysis showed the best cutoff point for the PMCSA was 611.67 mm 2 , with 75.0% specificity, 75.0% sensitivity, and the AUC of 0.81 (95% CI 0.68–0.94). The best cutoff value of the PMT was 14.24 mm, with 70.0% sensitivity, 70.0% specificity, and the AUC of 0.78 (95% CI 0.63–0.93).
科研通智能强力驱动
Strongly Powered by AbleSci AI