IR@PKUHSC  > 北京大学第一临床医学院  > 放射治疗科
学科主题临床医学
Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model
Zhao Kai1; Wang ChengYan2; Hu Juan1; Yang XueDong1; Wang He1; Li FeiYu1; Zhang XiaoDong1; Zhang Jue2; Wang XiaoYing1,2
关键词Prostate Cancer Magnetic Resonance Imaging T2wi Diagnosis Computer-assisted
刊名SCIENCE CHINA-LIFE SCIENCES
2015-07-01
DOI10.1007/s11427-015-4876-6
58期:7页:666-673
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]COMPUTER-AIDED DIAGNOSIS ; LOCALIZATION ; PERFORMANCE ; PREVALENCE ; STATISTICS ; MORTALITY ; PATTERNS ; ONCOLOGY ; SURVIVAL ; FEATURES
英文摘要

Computer-aided diagnosis (CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging (MRI), image features from T2-weighted images (T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone (PZ) and central gland (CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features (10/12) had significant difference (P<0.01) between PCa and non-PCa in the PZ, while only five features (sum average, minimum value, standard deviation, 10th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.

语种英语
WOS记录号WOS:000359146700005
引用统计
被引频次:7[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.bjmu.edu.cn/handle/400002259/52984
专题北京大学第一临床医学院_放射治疗科
北京大学第一临床医学院_医学影像科
北京大学临床肿瘤学院_消化肿瘤内科
作者单位1.Peking Univ, Hosp 1, Dept Radiol, Beijing 100034, Peoples R China
2.Peking Univ, Acad Adv Interdisciplinary Studies, Beijing 100871, Peoples R China
推荐引用方式
GB/T 7714
Zhao Kai,Wang ChengYan,Hu Juan,et al. Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model[J]. SCIENCE CHINA-LIFE SCIENCES,2015,58(7):666-673.
APA Zhao Kai.,Wang ChengYan.,Hu Juan.,Yang XueDong.,Wang He.,...&Wang XiaoYing.(2015).Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model.SCIENCE CHINA-LIFE SCIENCES,58(7),666-673.
MLA Zhao Kai,et al."Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model".SCIENCE CHINA-LIFE SCIENCES 58.7(2015):666-673.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhao Kai]的文章
[Wang ChengYan]的文章
[Hu Juan]的文章
百度学术
百度学术中相似的文章
[Zhao Kai]的文章
[Wang ChengYan]的文章
[Hu Juan]的文章
必应学术
必应学术中相似的文章
[Zhao Kai]的文章
[Wang ChengYan]的文章
[Hu Juan]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。