Shunli Liu1, Song Liu1, Changfeng Ji1, Huanhuan Zheng1, Xia Pan1, Yujuan Zhang1, Wenxian Guan2, Ling Chen3, Yue Guan4, Weifeng Li4, Jian He5, Yun Ge6, Zhengyang Zhou7. 1. Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, Jiangsu Province, China, 210008. 2. Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China, 210008. 3. Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, Jiangsu Province, China, 210008. 4. School of Electronic Science and Engineering, Nanjing University, No. 163 Xianlin Avenue, Nanjing, China, 210046. 5. Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, Jiangsu Province, China, 210008. hjxueren@126.com. 6. School of Electronic Science and Engineering, Nanjing University, No. 163 Xianlin Avenue, Nanjing, China, 210046. geyun@nju.edu.cn. 7. Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, Jiangsu Province, China, 210008. zyzhou@nju.edu.cn.
Abstract
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.
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.
Entities:
Keywords:
Diagnosis; Gastric cancer; Medical oncology; Multidetector computed tomography; Pathology
Authors: Balaji Ganeshan; Vicky Goh; Henry C Mandeville; Quan Sing Ng; Peter J Hoskin; Kenneth A Miles Journal: Radiology Date: 2012-11-20 Impact factor: 11.105