Quantitative analysis of diffusion-weighted magnetic resonance images: differentiation between prostate cancer and normal tissue based on a computer-aided diagnosis system
Science China Life Sciences, ISSN: 1674-7305, Vol: 60, Issue: 1, Page: 37-43
2017
- 15Citations
- 25Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations15
- Citation Indexes15
- 15
- CrossRef1
- Captures25
- Readers25
- 25
Article Description
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).
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85009264099&origin=inward; http://dx.doi.org/10.1007/s11427-016-0389-9; http://www.ncbi.nlm.nih.gov/pubmed/28078507; http://link.springer.com/10.1007/s11427-016-0389-9; https://dx.doi.org/10.1007/s11427-016-0389-9; https://link.springer.com/article/10.1007/s11427-016-0389-9; http://sciencechina.cn/gw.jsp?action=cited_outline.jsp&type=1&id=5917261&internal_id=5917261&from=elsevier
Springer Nature
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