CASIA OpenIR  > 中国科学院分子影像重点实验室
Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications
Lei, Chuqian1,2,3; Wei, Wei4,5,6; Liu, Zhenyu6; Xiong, Qianqian1,2,3; Yang, Ciqiu1; Yang, Mei1; Zhang, Liulu1; Zhu, Teng1; Zhuang, Xiaosheng1,7; Liu, Chunling8; Liu, Zaiyi8; Tian, Jie5,6,9; Wang, Kun1,2,3
Source PublicationEUROPEAN JOURNAL OF RADIOLOGY
ISSN0720-048X
2019-12-01
Volume121Pages:8
Corresponding AuthorTian, Jie(jie.tian@ia.ac.cn) ; Wang, Kun(gzwangkun@126.com)
AbstractPurpose: We developed and validated a radiomic model based on mammography and assessed its value for predicting the pathological diagnosis of Breast Imaging Reporting and Data System (BI-RADS) category 4 calcifications. Materials and methods: Patients with a total of 212 eligible calcifications were recruited (159 cases in the primary cohort and 53 cases in the validation cohort). In total, 8286 radiomic features were extracted from the craniocaudal (CC) and mediolateral oblique (MLO) images. Machine learning was used to select features and build a radiomic signature. The clinical risk factors were selected from the independent clinical factors through logistic regression analyses. The radiomic nomogram incorporated the radiomic signature and an independent clinical risk factor. The diagnostic performance of the radiomic model and the radiologists' empirical prediction model was evaluated by the area under the receiver operating characteristic curve (AUC). The differences between the various AUCs were compared with DeLong's test. Results: Six radiomic features and the menopausal state were included in the radiomic nomogram, which discriminated benign calcifications from malignant calcifications with an AUC of 0.80 in the validation cohort. The difference between the classification results of the radiomic nomogram and that of radiologists was significant (p < 0.05). Particularly for patients with calcifications that are negative on ultrasounds but can be detected by mammography (MG+/US-calcifications), the identification ability of the radiomic nomogram was very strong. Conclusions: The mammography-based radiomic nomogram is a potential tool to distinguish benign calcifications from malignant calcifications.
KeywordRadiomics Breast Calcification Predictive value of test Unnecessary procedures
DOI10.1016/j.ejrad.2019.108711
WOS KeywordBREAST-CANCER ; MICROCALCIFICATION DESCRIPTORS ; SCREENING MAMMOGRAPHY ; REGULARIZATION PATHS ; RISK ; CHEMORADIOTHERAPY ; PERFORMANCE ; WOMEN ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFC1309100] ; National Key Research and Development Program of China[2017YFA0205200] ; Natural Science Foundation of Guangdong Province, China[2017A030313882] ; National Natural Science Foundation of China[81871513] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81922040] ; Beijing Natural Science Foundation[7182109] ; Youth Innovation Promotion Association CAS[2019136] ; CSC0-constant Rui Tumor Research Fund, China[Y-HR2016-067] ; National Key Research and Development Program of China[2017YFC1309100] ; National Key Research and Development Program of China[2017YFA0205200] ; Natural Science Foundation of Guangdong Province, China[2017A030313882] ; National Natural Science Foundation of China[81871513] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81922040] ; Beijing Natural Science Foundation[7182109] ; Youth Innovation Promotion Association CAS[2019136] ; CSC0-constant Rui Tumor Research Fund, China[Y-HR2016-067]
Funding OrganizationNational Key Research and Development Program of China ; Natural Science Foundation of Guangdong Province, China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS ; CSC0-constant Rui Tumor Research Fund, China
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000500465900028
PublisherELSEVIER IRELAND LTD
Sub direction classification医学影像处理与分析
Citation statistics
Cited Times:28[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29368
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie; Wang, Kun
Affiliation1.Southern Med Univ, Sch Clin Med 2, Guangzhou 510515, Guangdong, Peoples R China
2.Guangdong Prov Peoples Hosp, Canc Ctr, Dept Breast Canc, Guangzhou 510080, Guangdong, Peoples R China
3.Guangdong Acad Med Sci, Guangzhou 510080, Guangdong, Peoples R China
4.Xian Polytech Univ, Sch Elect & Informat, Xian 710000, Shaanxi, Peoples R China
5.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shaanxi, Peoples R China
6.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
7.Shantou Univ, Med Coll, Shantou 515041, Guangdong, Peoples R China
8.Guangdong Prov Peoples Hosp, Dept Radiol, Guangzhou 510080, Guangdong, Peoples R China
9.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Lei, Chuqian,Wei, Wei,Liu, Zhenyu,et al. Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications[J]. EUROPEAN JOURNAL OF RADIOLOGY,2019,121:8.
APA Lei, Chuqian.,Wei, Wei.,Liu, Zhenyu.,Xiong, Qianqian.,Yang, Ciqiu.,...&Wang, Kun.(2019).Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications.EUROPEAN JOURNAL OF RADIOLOGY,121,8.
MLA Lei, Chuqian,et al."Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications".EUROPEAN JOURNAL OF RADIOLOGY 121(2019):8.
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