Background: The objective of this study is to determine the morphological computed tomography features of the tumor and texture analysis parameters, which may be a useful diagnostic tool for the preoperative prediction of high-risk gastrointestinal stromal tumors (HR GISTs). Methods: This is a prospective cohort study that was carried out in the period from 2019 to 2022. The study included 79 patients who underwent CT examination, texture analysis, surgical resection of a lesion that was suspicious for GIST as well as pathohistological and immunohistochemical analysis. Results: Textural analysis pointed out min norm (p= 0.032) as a histogram parameter that significantly differed between HR and LR GISTs, while min norm (p= 0.007), skewness (p= 0.035) and kurtosis (p= 0.003) showed significant differences between high-grade and low-grade tumors. Univariate regression analysis identified tumor diameter, margin appearance, growth pattern, lesion shape, structure, mucosal continuity, enlarged peri- and intra-tumoral feeding or draining vessel (EFDV) and max norm as significant predictive factors for HR GISTs. Interrupted mucosa (p< 0.001) and presence of EFDV (p< 0.001) were obtained by multivariate regression analysis as independent predictive factors of high-risk GISTs with an AUC of 0.878 (CI: 0.797–0.959), sensitivity of 94%, specificity of 77% and accuracy of 88%. Conclusion: This result shows that morphological CT features of GIST are of great importance in the prediction of non-invasive preoperative metastatic risk. The incorporation of texture analysis into basic imaging protocols may further improve the preoperative assessment of risk stratification.
背景：本研究旨在确定肿瘤的形态学计算机断层扫描特征及纹理分析参数，这些指标可能成为术前预测高风险胃肠道间质瘤（HR GISTs）的有效诊断工具。方法：本研究为2019年至2022年间开展的前瞻性队列研究，共纳入79例患者，所有患者均接受CT检查、纹理分析、可疑GIST病灶的手术切除以及病理组织学和免疫组化分析。结果：纹理分析显示最小归一化值（p=0.032）作为直方图参数在HR与LR GISTs间存在显著差异，而最小归一化值（p=0.007）、偏度（p=0.035）和峰度（p=0.003）在高危与低危肿瘤间呈现显著差异。单变量回归分析确定肿瘤直径、边缘形态、生长模式、病灶形状、结构、黏膜连续性、瘤周及瘤内供血或引流血管增粗（EFDV）以及最大归一化值是HR GISTs的显著预测因素。多变量回归分析得出黏膜中断（p<0.001）和EFDV存在（p<0.001）是高风险GISTs的独立预测因素，其曲线下面积为0.878（置信区间：0.797–0.959），敏感性94%，特异性77%，准确率88%。结论：该结果表明GIST的形态学CT特征对术前无创性转移风险预测具有重要意义。将纹理分析纳入基础影像学方案可进一步提升术前风险分层的评估效能。
肿瘤(癌症)患者之家