Zelan Ma1, Mengjie Fang2, Yanqi Huang3, Lan He4, Xin Chen5, Cuishan Liang6, Xiaomei Huang7, Zixuan Cheng8, Di Dong9, Changhong Liang10, Jiajun Xie11, Jie Tian12, Zaiyi Liu13. 1. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. Electronic address: zelanma@163.com. 2. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China. Electronic address: fmj5mj@gmail.com. 3. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. Electronic address: yikiann@126.com. 4. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China. Electronic address: helan0811@126.com. 5. Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China. Electronic address: wolfchenxin@sina.com. 6. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Southern Medical University, Guangzhou, Guangdong, 510515, China. Electronic address: liangcs2015@163.com. 7. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Southern Medical University, Guangzhou, Guangdong, 510515, China. Electronic address: m18565253960@163.com. 8. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China. Electronic address: czxtlbb@126.com. 9. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China. Electronic address: di.dong@ia.ac.cn. 10. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. Electronic address: cjr.lchh@vip.163.com. 11. Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China. Electronic address: 904660014@qq.com. 12. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China; Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. Electronic address: jie.tian@ia.ac.cn. 13. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. Electronic address: zyliu@163.com.
Abstract
PURPOSE: To evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL). MATERIALS AND METHODS: 40 patients with Borrmann type IV GC and 30 patients with PGL were retrospectively recruited. 485 radiomics features were extracted and selected from the portal venous CT images to build a radiomics signature. Subjective CT findings, including gastric wall peristalsis, perigastric fat infiltration, lymphadenopathy below the renal hila and enhancement pattern, were assessed to construct a subjective findings model. The radiomics signature, subjective CT findings, age and gender were integrated into a combined model by multivariate analysis. The diagnostic performance of these three models was assessed with receiver operating characteristics curves (ROC) and were compared using DeLong test. RESULTS: The subjective findings model, the radiomics signature and the combined model showed a diagnostic accuracy of 81.43% (AUC [area under the curve], 0.806; 95% CI [confidence interval]: 0.696-0.917; sensitivity, 63.33%; specificity, 95.00%), 84.29% (AUC, 0.886 [95% CI: 0.809-0.963]; sensitivity, 86.67%; specificity, 82.50%), 87.14% (AUC, 0.903 [95%CI: 0.831-0.975]; sensitivity, 70.00%; specificity, 100%), respectively. There were no significant differences in AUC among these three models (P=0.051-0.422). CONCLUSION: Radiomics analysis has the potential to accurately differentiate Borrmann type IV GC from PGL.
PURPOSE: To evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL). MATERIALS AND METHODS: 40 patients with Borrmann type IV GC and 30 patients with PGL were retrospectively recruited. 485 radiomics features were extracted and selected from the portal venous CT images to build a radiomics signature. Subjective CT findings, including gastric wall peristalsis, perigastric fat infiltration, lymphadenopathy below the renal hila and enhancement pattern, were assessed to construct a subjective findings model. The radiomics signature, subjective CT findings, age and gender were integrated into a combined model by multivariate analysis. The diagnostic performance of these three models was assessed with receiver operating characteristics curves (ROC) and were compared using DeLong test. RESULTS: The subjective findings model, the radiomics signature and the combined model showed a diagnostic accuracy of 81.43% (AUC [area under the curve], 0.806; 95% CI [confidence interval]: 0.696-0.917; sensitivity, 63.33%; specificity, 95.00%), 84.29% (AUC, 0.886 [95% CI: 0.809-0.963]; sensitivity, 86.67%; specificity, 82.50%), 87.14% (AUC, 0.903 [95%CI: 0.831-0.975]; sensitivity, 70.00%; specificity, 100%), respectively. There were no significant differences in AUC among these three models (P=0.051-0.422). CONCLUSION: Radiomics analysis has the potential to accurately differentiate Borrmann type IV GC from PGL.