Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system(CNS) lymphoma. Methods The pre-operative...Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system(CNS) lymphoma. Methods The pre-operative MRI data of 81 patients with glioblastoma and 28 patients with primary CNS lymphoma admitted to the Chinese PLA General Hospital and Hainan Hospital of Chinese PLA General Hospital were retrospectively collected. All patients underwent plain MR imaging and enhanced T1 weighted imaging to visualize imaging features of lesions. Texture analysis of T2 weighted imaging(T2 WI) was performed by use of GLCM texture plugin of ImageJ software, and the texture parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were measured. Independent sample t-test and Mann-Whitney U test were performed for the between-group comparisons, regression model was established by Binary Logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to compare the diagnostic efficacy. Results The conventional imaging features including cystic and necrosis changes(P = 0.000), ‘Rosette' changes(P = 0.000) and ‘incision sign'(P = 0.000), except ‘flame-like edema'(P = 0.635), presented significantly statistical difference between glioblastoma and primary CNS lymphoma. The texture features, ASM, Contrast, Correlation, IDM and Entropy, showed significant differences between glioblastoma and primary CNS lympoma(P = 0.006,0.000, 0.002, 0.000, and 0.015 respectively). The area under the ROC curve was 0.671, 0.752, 0.695, 0.720 and 0.646 respectively, and the area under the ROC curve was 0.917 for the combined texture variables(Contrast, cystic and necrosis, ‘Rosette' changes, and ‘incision sign') in the model of Logistic regression. Binary Logistic regression analysis demonstrated that cystic and necrosis changes, ‘Rosette' changes and ‘incision sign' and texture Contrast could be considered as the specific texture variables for the differential diagnosis of glioblastoma and primary CNS lymphoma. Conclusion The texture features of T2 WI and conventional imaging findings may be used to distinguish glioblastoma from primary CNS lymphoma.展开更多
Objective To investigate the difference in texture features on diffusion weighted imaging(DWI) images between breast benign and malignant tumors.Methods Patients including 56 with mass-like breast cancer, 16 with brea...Objective To investigate the difference in texture features on diffusion weighted imaging(DWI) images between breast benign and malignant tumors.Methods Patients including 56 with mass-like breast cancer, 16 with breast fibroadenoma, and 4 with intraductal papilloma of breast treated in the Hainan Hospital of Chinese PLA General Hospital were retrospectively enrolled in this study, and allocated to the benign group(20 patients) and the malignant group(56 patients) according to the post-surgically pathological results. Texture analysis was performed on axial DWI images, and five characteristic parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were calculated. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. Regression model was established by using Binary Logistic regression analysis, and receiver operating characteristic curve(ROC) analysis was carried out to evaluate the diagnostic efficiency. Results The texture features ASM, Contrast, Correlation and Entropy showed significant differences between the benign and malignant breast tumor groups(PASM= 0.014, Pcontrast= 0.019, Pcorrelation= 0.010, Pentropy= 0.007). The area under the ROC curve was 0.685, 0.681, 0.754, and 0.683 respectively for the positive texture variables mentioned above, and that for the combined variables(ASM, Contrast, and Entropy) was 0.802 in the model of Logistic regression. Binary Logistic regression analysis demonstrated that ASM, Contrast and Entropy were considered as thespecific imaging variables for the differential diagnosis of breast benign and malignant tumors.Conclusion The texture analysis of DWI may be a simple and effective tool in the differential diagnosis between breast benign and malignant tumors.展开更多
文摘Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system(CNS) lymphoma. Methods The pre-operative MRI data of 81 patients with glioblastoma and 28 patients with primary CNS lymphoma admitted to the Chinese PLA General Hospital and Hainan Hospital of Chinese PLA General Hospital were retrospectively collected. All patients underwent plain MR imaging and enhanced T1 weighted imaging to visualize imaging features of lesions. Texture analysis of T2 weighted imaging(T2 WI) was performed by use of GLCM texture plugin of ImageJ software, and the texture parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were measured. Independent sample t-test and Mann-Whitney U test were performed for the between-group comparisons, regression model was established by Binary Logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to compare the diagnostic efficacy. Results The conventional imaging features including cystic and necrosis changes(P = 0.000), ‘Rosette' changes(P = 0.000) and ‘incision sign'(P = 0.000), except ‘flame-like edema'(P = 0.635), presented significantly statistical difference between glioblastoma and primary CNS lymphoma. The texture features, ASM, Contrast, Correlation, IDM and Entropy, showed significant differences between glioblastoma and primary CNS lympoma(P = 0.006,0.000, 0.002, 0.000, and 0.015 respectively). The area under the ROC curve was 0.671, 0.752, 0.695, 0.720 and 0.646 respectively, and the area under the ROC curve was 0.917 for the combined texture variables(Contrast, cystic and necrosis, ‘Rosette' changes, and ‘incision sign') in the model of Logistic regression. Binary Logistic regression analysis demonstrated that cystic and necrosis changes, ‘Rosette' changes and ‘incision sign' and texture Contrast could be considered as the specific texture variables for the differential diagnosis of glioblastoma and primary CNS lymphoma. Conclusion The texture features of T2 WI and conventional imaging findings may be used to distinguish glioblastoma from primary CNS lymphoma.
文摘Objective To investigate the difference in texture features on diffusion weighted imaging(DWI) images between breast benign and malignant tumors.Methods Patients including 56 with mass-like breast cancer, 16 with breast fibroadenoma, and 4 with intraductal papilloma of breast treated in the Hainan Hospital of Chinese PLA General Hospital were retrospectively enrolled in this study, and allocated to the benign group(20 patients) and the malignant group(56 patients) according to the post-surgically pathological results. Texture analysis was performed on axial DWI images, and five characteristic parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were calculated. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. Regression model was established by using Binary Logistic regression analysis, and receiver operating characteristic curve(ROC) analysis was carried out to evaluate the diagnostic efficiency. Results The texture features ASM, Contrast, Correlation and Entropy showed significant differences between the benign and malignant breast tumor groups(PASM= 0.014, Pcontrast= 0.019, Pcorrelation= 0.010, Pentropy= 0.007). The area under the ROC curve was 0.685, 0.681, 0.754, and 0.683 respectively for the positive texture variables mentioned above, and that for the combined variables(ASM, Contrast, and Entropy) was 0.802 in the model of Logistic regression. Binary Logistic regression analysis demonstrated that ASM, Contrast and Entropy were considered as thespecific imaging variables for the differential diagnosis of breast benign and malignant tumors.Conclusion The texture analysis of DWI may be a simple and effective tool in the differential diagnosis between breast benign and malignant tumors.
