Zhiming Li1, Lan Yu1, Xin Wang1, Haiyang Yu1, Yuanxiang Gao1, Yande Ren1, Gang Wang1, Xiaoming Zhou2. 1. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. 2. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. Electronic address: 532729569@qq.com.
Abstract
BACKGROUND: The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. PATIENTS AND METHODS: Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. RESULTS: Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). CONCLUSION: Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance.
BACKGROUND: The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. PATIENTS AND METHODS: Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. RESULTS: Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). CONCLUSION: Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance.
Authors: Arnau Panisello-Roselló; Eva Verde; Alexandre Lopez; Marta Flores; Emma Folch-Puy; Anabela Rolo; Carlos Palmeira; Georgina Hotter; Teresa Carbonell; René Adam; Joan Roselló-Catafau Journal: Int J Mol Sci Date: 2018-01-24 Impact factor: 5.923