Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, 318 Chao-Wang Road, Hangzhou, 310005, China.
Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, NO. 88 Jie-Fang Road, Hangzhou, 310009, China.
Eur J Radiol. 2021 Jan;134:109395. doi: 10.1016/j.ejrad.2020.109395. Epub 2020 Nov 5.
To investigate CT findings and develop a diagnostic score model to differentiate GLMs from GISTs.
This retrospective study included 109 patients with pathologically confirmed GLMs (n = 46) and GISTs (n = 63) from January 2013 to August 2018 who received CE-CT before surgery. Demographic and radiological features was collected, including lesion location, contour, presence or absence of intralesional necrosis and ulceration, growth pattern, whether the tumor involved EGJ, the long diameter (LD) /the short diameter (SD) ratio, pattern and degree of lesion enhancement. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors and establish a predictive model. Independent predictors for GLMs were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the model. Overall score distribution was divided into four groups to show differentiating probability of GLMs from GISTs.
Five CT features were the independent predictors for GLMs diagnosis in multivariate logistic regression analysis, including esophagogastric junction (EGJ) involvement (OR, 367.9; 95 % CI, 5.8-23302.8; P = 0.005), absence of necrosis (OR, 11.9; 95 % CI, 1.0-138. 1; P = 0.048) and ulceration (OR, 151.9; 95 % CI, 1.4-16899.6; P = 0.037), degree of enhancement (OR, 9.3; 95 % CI, 3.2-27.4; P < 0.001), and long diameter/ short diameter (LD/SD) ratio (OR,170.9; 95 % CI, 8.4-3493.4; P = 0.001). At a cutoff of 9 points, AUC for this score model was 0.95, with 95.65 % sensitivity, 79.37 % specificity, 77.19 % PPV, 96.15 % NPV and 86.24 % diagnostic accuracy. An increasing trend was showed in diagnostic probability of GLMs among four groups based on the score (P < 0.001).
The newly designed scoring system is reliable and easy-to-use for GLMs diagnosis by distinguishing from GISTs, including EGJ involvement, absence of ulceration and necrosis, mild enhancement and high LD/SD ratio. The overall score of model ranged from 1 to 17 points, which was divided into 4 groups: 1-7 points, 7-10 points, 10-13 points and 13-17 points, with a diagnostic probability of GLMs 0%, 45 %, 83 % and 100 %, respectively.
探讨 CT 表现并建立诊断评分模型,以鉴别胃固有肌层肿瘤(GLM)和胃肠道间质瘤(GIST)。
本回顾性研究纳入了 2013 年 1 月至 2018 年 8 月期间经手术病理证实的 109 例 GLM(n=46)和 GIST(n=63)患者,所有患者均在手术前接受了 CE-CT 检查。收集了包括病变位置、轮廓、是否存在瘤内坏死和溃疡、生长模式、肿瘤是否累及食管胃结合部(EGJ)、长径(LD)/短径(SD)比值、病变强化方式和程度等方面的临床和影像学特征。采用单因素分析和多因素 logistic 回归分析确定独立预测因子,并建立预测模型。基于回归系数为 GLM 相关的独立预测因子加权评分。绘制受试者工作特征(ROC)曲线,以评估模型的诊断效能。根据总分分布将患者分为 4 组,以显示不同组间 GLM 的鉴别诊断概率。
多因素 logistic 回归分析显示,5 项 CT 特征是 GLM 诊断的独立预测因子,包括 EGJ 受累(OR,367.9;95 % CI,5.8-23302.8;P=0.005)、无坏死(OR,11.9;95 % CI,1.0-138.1;P=0.048)和溃疡(OR,151.9;95 % CI,1.4-16899.6;P=0.037)、强化程度(OR,9.3;95 % CI,3.2-27.4;P<0.001)和 LD/SD 比值(OR,170.9;95 % CI,8.4-3493.4;P=0.001)。当截断值为 9 分时,该评分模型的 AUC 为 0.95,灵敏度为 95.65 %,特异度为 79.37 %,阳性预测值为 77.19 %,阴性预测值为 96.15 %,诊断准确率为 86.24 %。根据评分,4 组间 GLM 的诊断概率呈递增趋势(P<0.001)。
该新设计的评分系统通过区分 GLM 和 GIST,包括 EGJ 受累、无溃疡和坏死、轻度强化和高 LD/SD 比值,可用于 GLM 的诊断,具有较高的可靠性和易用性。模型的总分范围为 1-17 分,分为 4 组:1-7 分、7-10 分、10-13 分和 13-17 分,对应的 GLM 诊断概率分别为 0%、45%、83%和 100%。