Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study
Abstract Background To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC). Method One hundred four participants were enrolled in this retrospective stu...
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| Format: | Article |
| Language: | English |
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BMC
2020-06-01
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| Series: | BMC Cancer |
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| Online Access: | http://link.springer.com/article/10.1186/s12885-020-07094-8 |
| _version_ | 1818341982290837504 |
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| author | Mengmeng Feng Mengchao Zhang Yuanqing Liu Nan Jiang Qian Meng Jia Wang Ziyun Yao Wenjuan Gan Hui Dai |
| author_facet | Mengmeng Feng Mengchao Zhang Yuanqing Liu Nan Jiang Qian Meng Jia Wang Ziyun Yao Wenjuan Gan Hui Dai |
| author_sort | Mengmeng Feng |
| collection | DOAJ |
| description | Abstract Background To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC). Method One hundred four participants were enrolled in this retrospective study. Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture features were analyzed by MaZda, and B11 program was used for data analysis and classification. The diagnosis efficiencies of texture features and conventional imaging features in identifying the differentiated degree of HCC were assessed by receiver operating characteristic analysis. The relationship between texture features and differentiated degree of HCC was evaluated by Spearman’s correlation coefficient. Results The grey-level co-occurrence matrix -based texture features were most frequently extracted and the nonlinear discriminant analysis was excellent with the misclassification rate ranging from 3.33 to 14.93%. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively, while the AUC of tumor size was 0.649, 0.660 and 0.517 respectively. The tumor size was significantly different between poorly- and moderately-HCC (p = 0.014). The COMBINE AUC values were not increased with tumor size combined. Conclusions Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI was valuable and might be a promising method in identifying the differentiated degree of HCC. The poorly-differentiated HCC was more heterogeneous than well- and moderately-differentiated HCC. |
| first_indexed | 2024-12-13T16:07:26Z |
| format | Article |
| id | doaj.art-fe429b258c4e42869c508418e24d2978 |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-12-13T16:07:26Z |
| publishDate | 2020-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-fe429b258c4e42869c508418e24d29782022-12-21T23:39:01ZengBMCBMC Cancer1471-24072020-06-0120111010.1186/s12885-020-07094-8Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective studyMengmeng Feng0Mengchao Zhang1Yuanqing Liu2Nan Jiang3Qian Meng4Jia Wang5Ziyun Yao6Wenjuan Gan7Hui Dai8Department of Radiology, the First Affiliated Hospital of Soochow UniversityDepartment of Radiology, the China-Japan Union Hospital of Jilin UniversityDepartment of Radiology, the First Affiliated Hospital of Soochow UniversityDepartment of Radiology, the First Affiliated Hospital of Soochow UniversityDepartment of Radiology, the First Affiliated Hospital of Soochow UniversityDepartment of Hepatobiliary Surgery Department, the First Affiliated Hospital of Soochow UniversityDepartment of Pathology Department, the First Affiliated Hospital of Soochow UniversityDepartment of Pathology Department, the First Affiliated Hospital of Soochow UniversityDepartment of Radiology, the First Affiliated Hospital of Soochow UniversityAbstract Background To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC). Method One hundred four participants were enrolled in this retrospective study. Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture features were analyzed by MaZda, and B11 program was used for data analysis and classification. The diagnosis efficiencies of texture features and conventional imaging features in identifying the differentiated degree of HCC were assessed by receiver operating characteristic analysis. The relationship between texture features and differentiated degree of HCC was evaluated by Spearman’s correlation coefficient. Results The grey-level co-occurrence matrix -based texture features were most frequently extracted and the nonlinear discriminant analysis was excellent with the misclassification rate ranging from 3.33 to 14.93%. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively, while the AUC of tumor size was 0.649, 0.660 and 0.517 respectively. The tumor size was significantly different between poorly- and moderately-HCC (p = 0.014). The COMBINE AUC values were not increased with tumor size combined. Conclusions Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI was valuable and might be a promising method in identifying the differentiated degree of HCC. The poorly-differentiated HCC was more heterogeneous than well- and moderately-differentiated HCC.http://link.springer.com/article/10.1186/s12885-020-07094-8Hepatocellular carcinomaDifferentiated degreeTexture feature |
| spellingShingle | Mengmeng Feng Mengchao Zhang Yuanqing Liu Nan Jiang Qian Meng Jia Wang Ziyun Yao Wenjuan Gan Hui Dai Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study BMC Cancer Hepatocellular carcinoma Differentiated degree Texture feature |
| title | Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study |
| title_full | Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study |
| title_fullStr | Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study |
| title_full_unstemmed | Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study |
| title_short | Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study |
| title_sort | texture analysis of mr images to identify the differentiated degree in hepatocellular carcinoma a retrospective study |
| topic | Hepatocellular carcinoma Differentiated degree Texture feature |
| url | http://link.springer.com/article/10.1186/s12885-020-07094-8 |
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