Literature DB >> 30103371

Performance evaluation of breast cancer diagnosis with mammography, ultrasonography and magnetic resonance imaging.

Hang Sun1, Hong Li1, Shuang Si2, Shouliang Qi1, Wei Zhang2, He Ma1, Siqi Liu1, Li Yingxue2, Wei Qian1,3.   

Abstract

OBJECTIVE: Various imaging modalities have been used to diagnose suspicious breast lesions. Purpose of this study is to compare the diagnostic accuracy for breast cancer using mammography, ultrasonography and magnetic resonance imaging (MRI).
METHODS: Total 107 patients aged from 19 to 62 years are included in this retrospective study. Mammography, ultrasonography and MRI scans were performed for each patient detected with suspected breast tumor within a month. In addition, the tumor diversity (10 types of benign and 5 types of malignant) was confirmed by pathological findings of tumor biopsy. To compare the diagnosis performance of the three imaging modalities, the overall fraction correct (accuracy), positive predict value (PPV), negative predict value (NPV), sensitivity and specificity were calculated. Meanwhile, the receiver operating characteristic (ROC) analysis was also performed.
RESULTS: The diagnostic accuracy ranged from 78.5% to 86.9% among three imaging modalities. All modalities yielded a PPV lower than 77.8% and a NPV higher than 90.0% in identifying the presence of malignant tumors. MRI presented a diagnostic accuracy of 86.9%, as well as a sensitivity of 95.5% and an area under curve (AUC) of 0.948, which are higher than mammography and ultrasonography.
CONCLUSION: By using a diverse dataset and comparing the diagnostic accuracy of three imaging modalities commonly used in breast cancer detection and diagnosis, this study also demonstrated that mammography, ultrasonography and MRI had different diagnostic performance in breast tumor identification. Among them, MRI yielded the highest performance even though the unexpected specificity may lead to over-diagnosis, and ultrosonography is slightly better than mammography.

Entities:  

Keywords:  Mammography; breast cancer diagnosis; breast magnetic resonance imaging (MRI); diagnostic performance assessment; ultrasonography

Mesh:

Year:  2018        PMID: 30103371     DOI: 10.3233/XST-18388

Source DB:  PubMed          Journal:  J Xray Sci Technol        ISSN: 0895-3996            Impact factor:   1.535


  2 in total

1.  Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.

Authors:  Xuxin Chen; Abolfazl Zargari; Alan B Hollingsworth; Hong Liu; Bin Zheng; Yuchen Qiu
Journal:  Comput Methods Programs Biomed       Date:  2019-07-29       Impact factor: 5.428

2.  Developing global image feature analysis models to predict cancer risk and prognosis.

Authors:  Bin Zheng; Yuchen Qiu; Faranak Aghaei; Seyedehnafiseh Mirniaharikandehei; Morteza Heidari; Gopichandh Danala
Journal:  Vis Comput Ind Biomed Art       Date:  2019-11-19
  2 in total

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