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Sci China Life Sci. 2017 Jan;60(1):37-43. doi: 10.1007/s11427-016-0389-9. Epub 2017 Jan 4.

Quantitative analysis of diffusion-weighted magnetic resonance images: differentiation between prostate cancer and normal tissue based on a computer-aided diagnosis system.

Author information

1
Department of Radiology, Peking University First Hospital, Beijing, 100034, China.
2
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
3
Department of Radiology, the First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China.
4
Department of Radiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
5
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China. zhangjue@vip.163.com.
6
Department of Radiology, Peking University First Hospital, Beijing, 100034, China. cjr.wangxiaoying@vip.163.com.

Abstract

Diffusion-weighted imaging (DWI) is considered to be one of the dominant modalities used in prostate cancer (PCa) detection and the assessment of lesion aggressiveness, especially for peripheral zone (PZ) PCa. Computer-aided diagnosis (CAD), which is capable of automatically extracting and evaluating image features, can integrate multiple parameters and improve the detection of PCa. In this study, 13 quantitative image features were extracted from DWI by CAD, and diagnostic efficacy was analyzed in both the PZ and transition zone (TZ). The results demonstrated that there was a significant difference (P<0.05) between PCa and non-PCa for nine of the 13 features in the PZ and five of the 13 features in the TZ. Besides, the prediction outcome of CAD had a strong correlation with the DWI scores that were graded by experienced radiologists according to the Prostate Imaging-Reporting and Data System Version 2 (PI-RADS v2).

KEYWORDS:

DWI; computer-assisted; diagnosis; magnetic resonance imaging; prostate cancer; prostate imaging-reporting and data system (PI-RADS)

PMID:
28078507
DOI:
10.1007/s11427-016-0389-9
[Indexed for MEDLINE]

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