CT-based Radiomics Signature for Differentiating Borrmann Type IV Gastric Cancer from Primary Gastric Lymphoma

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 featu...

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Published in:European journal of radiology 2017-06, Vol.91, p.142-147
Main Authors: Ma, Zelan, Fang, Mengjie, Huang, Yanqi, He, Lan, Chen, Xin, Liang, Cuishan, Huang, Xiaomei, Cheng, Zixuan, Dong, Di, Liang, Changhong, Xie, Jiajun, Tian, Jie, Liu, Zaiyi
Format: Article
Language:English
Subjects:
Borrmann type IV gastric cancer
Computed tomography
Humans
Lymphoma, Non-Hodgkin - diagnostic imaging
Lymphoma, Non-Hodgkin - pathology
Multivariate Analysis
Primary gastric lymphoma
Radiology
Radiomics signature
Retrospective Studies
ROC Curve
Stomach Neoplasms - diagnostic imaging
Stomach Neoplasms - pathology
Subjective CT findings
Tomography, X-Ray Computed - methods
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Summary: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.
ISSN:0720-048X
1872-7727
DOI:10.1016/j.ejrad.2017.04.007