Value of MRI texture analysis for predicting high-grade prostate cancer

  • Author Footnotes
    1 Dr. Hui Xiong and Dr. Xiaojing He contributed equally to this work and should be considered co-first authors.
    Hui Xiong
    1 Dr. Hui Xiong and Dr. Xiaojing He contributed equally to this work and should be considered co-first authors.
    Department of Radiology, the Ninth People's Hospital Chongqing, China
    Search for articles by this author
  • Author Footnotes
    1 Dr. Hui Xiong and Dr. Xiaojing He contributed equally to this work and should be considered co-first authors.
    Xiaojing He
    1 Dr. Hui Xiong and Dr. Xiaojing He contributed equally to this work and should be considered co-first authors.
    Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, China
    Search for articles by this author
  • Dajing Guo
    Corresponding author at: Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China.
    Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, China
    Search for articles by this author
  • Author Footnotes
    1 Dr. Hui Xiong and Dr. Xiaojing He contributed equally to this work and should be considered co-first authors.



      To explore the potential value of MRI texture analysis (TA) combined with prostate-related biomarkers to predict high-grade prostate cancer (HGPCa).

      Materials and methods

      Eighty-five patients who underwent MRI scanning, including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters derived from T2WI and DWI, prostate-specific antigen (PSA), and free PSA (fPSA) were compared between the HGPCa and non-high-grade prostate cancer (NHGPCa) groups using independent Student's t-test and the Mann-Whitney U test. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the predictive value for HGPCa.


      Univariate analysis showed that PSA and entropy based on apparent diffusion coefficient (ADC) map differed significantly between the HGPCa and NHGPCa groups and showed higher diagnostic values for HGPCa (area under the curve (AUC) = 82.0% and 80.0%, respectively). Logistic regression and ROC curve analyses revealed that kurtosis, skewness and entropy derived from ADC maps had diagnostic power to predict HGPCa; when the three texture parameters were combined, the area under the ROC curve reached the maximum (AUC = 84.6%; 95% confidence interval (CI): 0.758, 0.935; P = 0.000).


      TA parameters derived from ADC may be a valuable tool in predicting HGPCa. The combination of specific textural parameters extracted from ADC map may be additional tools to predict HGPCa.


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