Evidence map›Paper›PMID 30511528›Full record

ArticleZhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences2018

[Diffusion-weighted imaging texture features in differentiation of malignant from benign nonpalpable breast lesions for patients with microcalcifications-only in mammography].

Shujun Chen, Guoliang Shao, Feng Shao, Minming Zhang

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In one paragraph

Article in Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Shujun ChenDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
Guoliang ShaoDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China.
Feng ShaoDepartment of Gynecologic Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China.
Minming ZhangDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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.

methodsThe 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.

resultsSix 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

conclusionsThe 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.

Indexed as

Breast NeoplasmsDiffusion Magnetic Resonance ImagingMammographyBreastCalcinosisDiagnosis, DifferentialFemaleHumansRetrospective StudiesSensitivity and Specificity

Identifiers

PMID30511528
PMCPMC10393718

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