Department of Ultrasound, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
Abdom Radiol (NY). 2024 Dec;49(12):4189-4197. doi: 10.1007/s00261-024-04341-5. Epub 2024 Jun 8.
Gastrointestinal stromal tumors (GISTs) are difficult to identify the risk level accurately without surgical pathological confirmation. The purpose of our study was to propose a noninvasive prediction method for predicting the malignant potential of GISTs preoperatively by using contrast-enhanced ultrasound (CEUS) with gastric distention.
We reviewed 47 GISTs who underwent CEUS from April 2017 to August 2023 retrospectively, all the lesions were certificated by pathology after surgery. The age of the patient, size of the lesion, shape, necrosis, calcification in the lesion, perfusion parameters including arrival time (AT), peak intensity (PI), time to peak (TTP), and area under the curve (AUC) of the lesion and surrounding normal tissue were analyzed. Logistic regression analyses were performed. Of the 47 GISTs, 26 were high-risk and 21 low-risk tumors respectively.
Compared with low-risk GISTs, high-risk GIST had faster AT (7.7s vs. 11.5s, p < 0.05), higher PI (15.2dB vs. 12.5dB, p < 0.05), and larger size (4.4 cm vs. 2.2 cm, p < 0.001). In multivariate logistic regression, AT, PI, and size were significant features. The corresponding regression equation In (p/(1-p)=-5.9 + 4.5 size + 4.6 PI + 4.0 AT).
The size, AT, and PI of the GISTs on CEUS can be used as parameters for a noninvasive risk level prediction model of GISTs. This model may help identify the different risk levels of GISTs before surgery.
在没有手术病理证实的情况下,胃肠道间质瘤(GIST)很难准确识别其风险水平。本研究旨在通过胃扩张对比增强超声(CEUS)提出一种术前预测 GIST 恶性潜能的非侵入性预测方法。
我们回顾性分析了 2017 年 4 月至 2023 年 8 月期间 47 例接受 CEUS 的 GIST 患者,所有病变均经术后病理证实。分析患者年龄、病变大小、形状、坏死、病变内钙化、病变及周围正常组织的灌注参数,包括到达时间(AT)、峰值强度(PI)、达峰时间(TTP)和曲线下面积(AUC)。进行逻辑回归分析。47 例 GIST 中,高危组 26 例,低危组 21 例。
与低危 GIST 相比,高危 GIST 的 AT 更快(7.7s 比 11.5s,p<0.05),PI 更高(15.2dB 比 12.5dB,p<0.05),且病变较大(4.4cm 比 2.2cm,p<0.001)。多变量逻辑回归分析中,AT、PI 和大小是显著特征。对应的回归方程 In(p/(1-p))=-5.9+4.5 大小+4.6 PI+4.0 AT。
CEUS 上 GIST 的大小、AT 和 PI 可作为 GIST 无创风险水平预测模型的参数。该模型可能有助于在术前识别 GIST 的不同风险水平。