CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma |
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Institution: | 1. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan nanli, Chaoyang district, Beijing, China, 100021;2. Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan nanli, Chaoyang district, Beijing, China, 100021;3. Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, HaiDian District, Beijing, China, 100192;1. Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, 266000, Qingdao, Shandong, China;2. Qingdao Medical Colledge, Qingdao University, 266071, Qingdao, Shandong, China;3. Department of Radiology, The Affiliated Hospital of Qingdao University, 266000, Qingdao, Shandong, China;4. Department of Pathology, The Affiliated Hospital of Qingdao University, 266000, Qingdao, Shandong, China;5. Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, 266000, Qingdao, Shandong, China;6. Department of Gastrointestinal Surgery, Lingshui People''s Hospital, 572499, Hainan, China |
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Abstract: | PurposeTo evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL).Materials and methods40 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.ResultsThe 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).ConclusionRadiomics analysis has the potential to accurately differentiate Borrmann type IV GC from PGL. |
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Keywords: | Borrmann type IV gastric cancer Primary gastric lymphoma Computed tomography Radiomics signature Subjective CT findings |
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