Diagnostic Performance of Mammographic Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors.

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.