Diffusion-weighted imaging texture features in differentiation of malignant from benign nonpalpable breast lesions for patients with microcalcifications-only in mammography.
- Author:
Shujun CHEN
1
;
Guoliang SHAO
2
;
Feng SHAO
3
;
Minming ZHANG
1
Author Information
1. Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
2. Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China.
3. Department of Gynecologic Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China.
- Publication Type:Journal Article
- MeSH:
Breast;
diagnostic imaging;
Breast Neoplasms;
diagnostic imaging;
Calcinosis;
diagnostic imaging;
Diagnosis, Differential;
Diffusion Magnetic Resonance Imaging;
Female;
Humans;
Mammography;
Retrospective Studies;
Sensitivity and Specificity
- From:
Journal of Zhejiang University. Medical sciences
2018;47(4):400-404
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To evaluate the application of MR diffusion-weighted imaging(DWI) texture features in differentiation of malignant from benign nonpalpable breast lesion for patients with microcalcifications-only in mammography.
METHODS:The clinical and MR-DWI data of 61 patients with microcalcifications, who underwent three-dimensional positioning of breast X-ray wire from October 2012 to December 2015 in Zhejiang Cancer Hospital, were retrospectively analyzed, including 38 patients with malignant lesions and 23 patients with benign lesions. Two radiologists independently drew the regions of interest (ROI) on DWI for image segmentation, and 6 histogram features and 16 grayscale symbiosis matrix (GLCM) texture features were extracted on each ROI. The random forest algorithm was applied to select the features and built the classification model. The leave-one-out cross-validation (LOOCV) was used to validate the classifier, and the performance of the classifier was evaluated by ROC curve.
RESULTS:Six features were selected, including histogram features of mean, variance, skewness, entropy, as well as contrast (0°) and correlation (45°) in GLCM. The histogram features of mean, variance, skewness and entropy were significantly different between the benign and malignant breast lesions (all <0.05). The AUC of the model was 0.76, and the diagnostic accuracy, sensitivity and specificity were 77.05%, 84.21% and 65.21%, respectively.
CONCLUSIONS:The texture feature analysis of DWI can improve the diagnostic accuracy of differentiating benign and malignant breast nonpalpable lesions with microcalcifications-only in mammography. Histogram features of mean, variance, skewness, entropy of DWI may be used as important imaging markers.
