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IR@PKUHSC  > 北京大学第一临床医学院  > 放射治疗科  > 期刊论文
学科主题: 临床医学
题名:
Classification of breast lesions based on a dual S-shaped logistic model in dynamic contrast enhanced magnetic resonance imaging
作者: Dang Yi1; Guo Li2; Lv DongJiao2,3; Wang XiaoYing1; Zhang Jue1,3
关键词: logistic model ; breast cancer ; dynamic contrast enhanced magnetic resonance imaging
刊名: SCIENCE CHINA-LIFE SCIENCES
发表日期: 2011-10-01
DOI: 10.1007/s11427-011-4221-7
卷: 54, 期:10, 页:889-896
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology
类目[WOS]: Biology
研究领域[WOS]: Life Sciences & Biomedicine - Other Topics
关键词[WOS]: INTERSTITIAL FLUID PRESSURE ; WATER EXCHANGE ; MRI ; CANCER ; TUMORS ; BENIGN ; DIFFERENTIATION ; MAMMOGRAPHY ; BRAIN ; MICROCALCIFICATIONS
英文摘要:

This study proposes a novel dual S-shaped logistic model for automatically quantifying the characteristic kinetic curves of breast lesions and for distinguishing malignant from benign breast tumors on dynamic contrast enhanced (DCE) magnetic resonance (MR) images. D(alpha, beta) is the diagnostic parameter derived from the logistic model. Significant differences were found in D(alpha, beta) between the malignant benign groups. Fisher′s Linear Discriminant analysis correctly classified more than 90% of the benign and malignant kinetic breast data using the derived diagnostic parameter (D(alpha, beta)). Receiver operating characteristic curve analysis of the derived diagnostic parameter (D(alpha, beta)) indicated high sensitivity and specificity to differentiate malignancy from benignancy. The dual S-shaped logistic model was effectively used to fit the kinetic curves of breast lesions in DCE-MR. Separation between benign and malignant breast lesions was achieved with sufficient accuracy by using the derived diagnostic parameter D(alpha, beta) as the lesion′s feature. The proposed method therefore has the potential for computer-aided diagnosis in breast tumors.

语种: 英语
WOS记录号: WOS:000298196100001
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.bjmu.edu.cn/handle/400002259/64789
Appears in Collections:北京大学第一临床医学院_放射治疗科_期刊论文

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作者单位: 1.Peking Univ, Coll Engn, Beijing 100871, Peoples R China
2.Peking Univ, Acad Adv Interdisciplinary Studies, Beijing 100871, Peoples R China
3.Peking Univ, Dept Radiol, Hosp 1, Beijing 100034, Peoples R China

Recommended Citation:
Dang Yi,Guo Li,Lv DongJiao,et al. Classification of breast lesions based on a dual S-shaped logistic model in dynamic contrast enhanced magnetic resonance imaging[J]. SCIENCE CHINA-LIFE SCIENCES,2011,54(10):889-896.
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