Department of Radiotherapy, Fudan University Shanghai Cancer Center, Shanghai, China.
Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Gastric Cancer. 2019 Jul;22(4):769-777. doi: 10.1007/s10120-018-00908-6. Epub 2018 Dec 9.
To determine CT features that can identify gastrointestinal stromal tumors (GISTs) among gastric sub-epithelial tumors (SETs) and to explore a practical scoring method.
Sixty-four patients with gastric SETs (51 GISTs and 13 non-GISTs) from hospital I were included for primary analyses, and 92 (67 GISTs and 25 non-GISTs) from hospital II constituted a validation cohort. Pre-operative CT images were reviewed for imaging features: lesion location, growth pattern, lesion margin, enhancement pattern, dynamic pattern, attenuation at each phasic images and presence of necrosis, superficial ulcer, calcification, and peri-lesion enlarged lymph node (LN). Clinical and CT features were compared between the two groups (GISTs versus non-GISTs) and a GIST-risk scoring method was developed; then, its performance for identifying GISTs was tested in the validation cohort.
Seven clinical and CT features were significantly suggestive of GISTs rather than non-GISTs: older age (> 49 years), non-cardial location, irregular margin, lower attenuation on unenhanced images (≤ 43 HU), heterogeneous enhancement, necrosis, and absence of enlarged LN (p < 0.05). At validation step, the established scoring method with cut-off score dichotomized into ≥ 4 versus < 4 for identifying GISTs revealed an AUC of 0.97 with an accuracy of 92%, a sensitivity of 100% and a negative predictive value (NPV) of 100%.
Gastric GISTs have special CT and clinical features that differ from non-GISTs. With a simple and practical scoring method based on the significant features, GISTs can be accurately differentiated from non-GISTs.
确定能够在胃黏膜下肿瘤(SET)中识别胃肠道间质瘤(GIST)的 CT 特征,并探索一种实用的评分方法。
对 I 医院的 64 例胃 SET 患者(51 例 GIST 和 13 例非 GIST)进行了初步分析,对 II 医院的 92 例(67 例 GIST 和 25 例非 GIST)进行了验证队列分析。对术前 CT 图像进行了影像学特征的回顾:病变位置、生长模式、病变边缘、强化模式、动态模式、各时相图像的衰减以及坏死、浅表溃疡、钙化和病变周围增大的淋巴结(LN)的存在。比较两组(GIST 与非 GIST)之间的临床和 CT 特征,并建立 GIST 风险评分方法;然后在验证队列中测试其识别 GIST 的性能。
有 7 个临床和 CT 特征强烈提示 GIST 而非非 GIST:年龄较大(>49 岁)、非贲门位置、不规则边缘、平扫时的低衰减(≤43 HU)、不均匀强化、坏死和无增大的 LN(p<0.05)。在验证步骤中,采用截断值为≥4 与<4 的建立的评分方法,对识别 GIST 的 AUC 为 0.97,准确率为 92%,敏感度为 100%,阴性预测值(NPV)为 100%。
胃 GIST 具有与非 GIST 不同的特殊 CT 和临床特征。基于显著特征建立的简单实用的评分方法,能够准确地区分 GIST 和非 GIST。