PURPOSE To test the feasibility of differentiate gastric cancer from gastric stromal tumor using a radiomics study based on contrast-enhanced CT images. MATERIALS AND METHODS The contrast-enhanced CT image data of 60 patients with gastric cancer and 40 patients with gastric stromal tumor confirmed by postoperative pathology were retrospectively analyzed. First, CT images were read by two senior radiologists to acquire subjective CT signs model, including perigastric fatty infiltration, perigastric enlarged lymph nodes, the enhancement and growth modes of gastric tumors. Second, the manual segmentation of gastric tumors from the CT images was performed by the two radiologists to extract radiomics features via ITK-SNAP software, and to construct radiomics signature model. Finally, a diagnostic model integrated with subjective CT signs and radiomics signatures was constructed. The diagnostic efficacy of three models in differentiating gastric cancer from gastric stromal tumor was compared by using receiver operating characteristic curves (ROC). RESULTS There are statistically significant differences between the gastric cancer and gastric stromal tumor in the perigastric enlarged lymph nodes, growth mode and radiomics signature (p < 0.05). The area under ROC curve (AUC), sensitivity and accuracy of subjective CT signs model were the lowest among the three models. While the combined model yields the highest AUC value (0.903), specificity (93.33%) and accuracy (86.00%) among the three models (p = 0.03). CONCLUSION The diagnostic model integrating subjective CT signs and radiomics signature can improve the diagnostic accuracy of gastric tumors.

中文翻译：

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基于对比增强CT图像的放射线学研究用于区分胃癌和胃间质瘤。

目的使用基于对比增强CT图像的放射学研究来测试将胃癌与胃间质瘤区分开的可行性。材料与方法回顾性分析经术后病理证实的60例胃癌和40例胃间质瘤的CT增强扫描数据。首先，由两名高级放射科医生读取CT图像以获取主观CT征象模型，包括胃周脂肪浸润，胃周淋巴结肿大，胃肿瘤的增强和生长方式。其次，由两名放射科医生从CT图像中手动分割胃肿瘤，以通过ITK-SNAP软件提取放射学特征，并构建放射学特征模型。最后，建立了一个具有主观CT征象和放射学特征的诊断模型。通过使用受试者工作特征曲线（ROC），比较了三种模型在区分胃癌和胃间质瘤中的诊断功效。结果胃癌和胃间质瘤在胃周肿大淋巴结，生长方式和放射学特征上有统计学差异（p <0.05）。在三种模型中，ROC曲线下面积（AUC），主观CT征象模型的灵敏度和准确性最低。在三个模型中，组合模型的AUC值最高（0.903），特异性最高（93.33％），准确性（86.00％）（p = 0.03）。