Affiliations: [a] Department of Radiology, Affiliated Hospital of Jiangnan University, The Fourth People’s Hospital of Wuxi City, Jiangsu Province, China
| [b] Department of Intervention Affiliated Hospital of Jiangnan University, The Fourth People’s Hospital of Wuxi City, Jiangsu Province, China
| [c] Department of Oncology, Affiliated Hospital of Jiangnan University, The Fourth People’s Hospital of Wuxi City, Jiangsu Province, China
| [d] General Electric Company (GE) Healthcare China, Pudong New Town, Shanghai, China
| [e] Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
Correspondence:
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Corresponding authors: Shu-Dong Hu and Teng Wang, Affiliated Hospital of Jiangnan University,Wuxi, Jiangsu, 214062, China. Tel.: +86 15312238218; (Shu-Dong Hu, E-mail: hsd2001054@163.com) and (Teng Wang, E-mail: drwangteng@163.com).
Note: [1] Zong-Qiong Sun, Jie Li and Jun Wang contributed equally to this work.
Abstract: 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.
