The role of conventional CT examination, CT perfusion and tumor tissue texture analysis in predicting the risk of gastrointestinal stromal tumors

Milica Mitrovic-Jovanovic, Aleksandra Đurić-Stefanović, K. Ebrahimi
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Abstract

As the most common mesenchymal tumors of the digestive tract, gastrointestinal stromal tumors (GIST) can have significant metastatic potential with a high rate of dissemination to the liver and peritoneal cavity. Based on the modified NIH (National Institutes of Health) classification, these tumors are classified into four stages: very low, low, intermediate and high-risk tumors. For low-risk tumors (LRG), minimally invasive surgery with R0 resection is the most common therapeutic option, while for high-risk tumors (HRG), imatinib adjuvant therapy is included post-surgically. Preoperative risk stratification is important for determining the therapeutic treatment and the patient's prognosis. With modern advances in diagnostic modalities, imaging analysis is no longer limited to visual assessment. As a modality of functional multidetector CT diagnostics, CT perfusion enables the quantification of tumor vascularization, while texture analysis provides a quantitative assessment of the degree of tumor heterogeneity, which overall can help in differentiating the metastatic potential of these tumors. The importance of morphological characteristics of tumors obtained by conventional CT examination in risk prediction has already been proven by numerous studies, and it is considered that, together with the advanced CT techniques mentioned above, it can provide a predictive model for the preoperative assessment of GIST risk stratification.
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常规CT检查、CT灌注及肿瘤组织结构分析对胃肠道间质瘤发病风险的预测作用
胃肠道间质瘤(GIST)是消化道最常见的间质肿瘤,具有显著的转移潜力,其向肝脏和腹腔的播散率高。根据修改后的美国国立卫生研究院(NIH)分类,这些肿瘤分为四个阶段:极低、低、中、高风险肿瘤。对于低危肿瘤(LRG),微创手术+ R0切除是最常见的治疗选择,而对于高危肿瘤(HRG),术后包括伊马替尼辅助治疗。术前风险分层对确定治疗方案和患者预后具有重要意义。随着现代诊断方式的进步,影像分析不再局限于视觉评估。作为多功能多检测器CT诊断的一种方式,CT灌注可以量化肿瘤血管化,而纹理分析可以定量评估肿瘤的异质性程度,总体上有助于区分这些肿瘤的转移潜力。常规CT检查所获得的肿瘤形态特征在风险预测中的重要性已被大量研究证实,并认为与上述先进的CT技术相结合,可为术前评估GIST风险分层提供预测模型。
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