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      • Record: found
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      Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

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          Abstract

          To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa).

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          Author and article information

          Journal
          Journal ID (iso-abbrev): Eur Radiol
          Title: European radiology
          Publisher: Springer Nature
          ISSN (Electronic): 1432-1084
          ISSN (Print): 0938-7994
          Publication date (Electronic): Oct 2017
          Volume: 27
          Issue: 10
          Affiliations
          [1 ] Center for Medical Device Evaluation, CFDA, Beijing, China, 100044.
          [2 ] Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009.
          [3 ] Department of Pathology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, 210009.
          [4 ] Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009. njmu_zyd@163.com.
          Article
          Publisher Item ID: 10.1007/s00330-017-4800-5
          DOI: 10.1007/s00330-017-4800-5
          PubMed ID: 28374077
          SO-VID: b3c300c5-f06a-46c3-a6d3-e8a48c9b0821
          History

          Keywords: Prostate cancer,Machine learning,Multi-parametric MRI,Support vector machine,Prostate Imaging Reporting and Data System v2
          Data availability:
          Keywords: Prostate cancer, Machine learning, Multi-parametric MRI, Support vector machine, Prostate Imaging Reporting and Data System v2

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