Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

• Published in: European radiology 2017-12, Vol.27 (12), p.4951-4959
• Main Authors: Liu, Shunli, Liu, Song, Ji, Changfeng, Zheng, Huanhuan, Pan, Xia, Zhang, Yujuan, Guan, Wenxian, Chen, Ling, Guan, Yue, Li, Weifeng, He, Jian, Ge, Yun, Zhou, Zhengyang
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Analysis of Variance; Attenuation; Classification; Computed tomography; Contrast Media; Diagnostic Radiology; Differentiation; Entropy; Female; Gastric cancer; Gastrointestinal; Humans; Image classification; Image contrast; Image enhancement; Imaging; Internal Medicine; Interventional Radiology; Male; Medical diagnosis; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Portal Vein - pathology; Radiology; Retrospective Studies; Standard deviation; Stomach Neoplasms - diagnostic imaging; Stomach Neoplasms - pathology; Texture; Tomography; Tomography, X-Ray Computed - methods; Ultrasound; Young Adult
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-017-4881-1

Objectives To explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers. Methods Preoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed. Results Mean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, −0.231 ~ −0.324, 0.228 ~ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = −0.265, −0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion. Conclusion CT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers. Key Points • CT texture analysis is noninvasive and effective for gastric cancer . • Portal venous CT images correlated significantly with differentiation degree and Lauren classification . • Standard deviation, entropy and minimum attenuation in arterial phase reflect vascular invasion .