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A Nomogram for Predicting Pelvic Lymph Node Metastasis in Prostate Cancer

Received: 25 December 2021     Accepted: 19 January 2022     Published: 26 January 2022
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Abstract

Background: Prostate cancer (PCa) is prone to lymph node metastasis. In this report, the authors described a model predictive of the probability of lymph node metastasis in prostate cancer patients. Methods: Two-hundred seventy-eight middle-high-risk PCa patients who received laparoscopic radical prostatectomy (LRP) combined with extended pelvic lymph node dissection (e-PLND) in our hospital were selected as the subjects and the authors performed a retrospective analysis. According to the postoperative pathological results, the patients were divided into a pelvic lymph node metastasis group (n=100) and a non-pelvic lymph node metastasis group (n=178). Univariable and multivariable logistic regression analyses were performed to identify independent risk factors for pelvic lymph node metastasis from PCa. Finally, a clinical prediction model nomogram was further established and verified, and a calibration plot was drawn to verify the accuracy of the model. Results: The TPSA level, FPSA level, PI-RADS score, biopsy ISUP classification and Gleason score of the two groups were statistically different (P<0.05), and there was no statistical difference between the age groups (P>0.05). Receiver operating characteristic curve (ROC) showed that the best diagnostic cut-off value of TPSA was 77.45 ng/ml (AUC=0.785, 95%CI: 0.729-0.842), and the best diagnostic cut-off value of FPSA was 0.085 ng/ml (AUC=0.282, 95%CI: 0.215-0.348). Univariable and multivariable logistic regression analyses showed that, TPSA level (OR=1.00, 95%Cl: 1.000-1.006, P<0.05), FPSA level (OR=0.00, 95%Cl: 0.000-0.089, P<0.01), PI-RADS score (OR=9.26, 95%CI: 5.278-16.248, P<0.01) and biopsy ISUP grade (OR=1.69, 95%CI: 1.163-2.450, P<0.01) were independent predictors of pelvic lymph node metastasis. Conclusions: The nomogram established in this study has a good predictive ability for pelvic lymph node metastasis in patients with PCa, and can provide a reference for the selection of clinical treatment options.

Published in International Journal of Clinical Urology (Volume 6, Issue 1)
DOI 10.11648/j.ijcu.20220601.13
Page(s) 10-14
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Prostate Cancer, Pelvic Lymph Node Metastasis, Risk Factors, Nomogram

References
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  • APA Style

    Xu Yang, Chen Rui, Li Shuofeng, Zhang Chi, Zheng Yuxin, et al. (2022). A Nomogram for Predicting Pelvic Lymph Node Metastasis in Prostate Cancer. International Journal of Clinical Urology, 6(1), 10-14. https://doi.org/10.11648/j.ijcu.20220601.13

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    ACS Style

    Xu Yang; Chen Rui; Li Shuofeng; Zhang Chi; Zheng Yuxin, et al. A Nomogram for Predicting Pelvic Lymph Node Metastasis in Prostate Cancer. Int. J. Clin. Urol. 2022, 6(1), 10-14. doi: 10.11648/j.ijcu.20220601.13

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    AMA Style

    Xu Yang, Chen Rui, Li Shuofeng, Zhang Chi, Zheng Yuxin, et al. A Nomogram for Predicting Pelvic Lymph Node Metastasis in Prostate Cancer. Int J Clin Urol. 2022;6(1):10-14. doi: 10.11648/j.ijcu.20220601.13

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  • @article{10.11648/j.ijcu.20220601.13,
      author = {Xu Yang and Chen Rui and Li Shuofeng and Zhang Chi and Zheng Yuxin and Li Wang},
      title = {A Nomogram for Predicting Pelvic Lymph Node Metastasis in Prostate Cancer},
      journal = {International Journal of Clinical Urology},
      volume = {6},
      number = {1},
      pages = {10-14},
      doi = {10.11648/j.ijcu.20220601.13},
      url = {https://doi.org/10.11648/j.ijcu.20220601.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijcu.20220601.13},
      abstract = {Background: Prostate cancer (PCa) is prone to lymph node metastasis. In this report, the authors described a model predictive of the probability of lymph node metastasis in prostate cancer patients. Methods: Two-hundred seventy-eight middle-high-risk PCa patients who received laparoscopic radical prostatectomy (LRP) combined with extended pelvic lymph node dissection (e-PLND) in our hospital were selected as the subjects and the authors performed a retrospective analysis. According to the postoperative pathological results, the patients were divided into a pelvic lymph node metastasis group (n=100) and a non-pelvic lymph node metastasis group (n=178). Univariable and multivariable logistic regression analyses were performed to identify independent risk factors for pelvic lymph node metastasis from PCa. Finally, a clinical prediction model nomogram was further established and verified, and a calibration plot was drawn to verify the accuracy of the model. Results: The TPSA level, FPSA level, PI-RADS score, biopsy ISUP classification and Gleason score of the two groups were statistically different (P0.05). Receiver operating characteristic curve (ROC) showed that the best diagnostic cut-off value of TPSA was 77.45 ng/ml (AUC=0.785, 95%CI: 0.729-0.842), and the best diagnostic cut-off value of FPSA was 0.085 ng/ml (AUC=0.282, 95%CI: 0.215-0.348). Univariable and multivariable logistic regression analyses showed that, TPSA level (OR=1.00, 95%Cl: 1.000-1.006, PConclusions: The nomogram established in this study has a good predictive ability for pelvic lymph node metastasis in patients with PCa, and can provide a reference for the selection of clinical treatment options.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - A Nomogram for Predicting Pelvic Lymph Node Metastasis in Prostate Cancer
    AU  - Xu Yang
    AU  - Chen Rui
    AU  - Li Shuofeng
    AU  - Zhang Chi
    AU  - Zheng Yuxin
    AU  - Li Wang
    Y1  - 2022/01/26
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ijcu.20220601.13
    DO  - 10.11648/j.ijcu.20220601.13
    T2  - International Journal of Clinical Urology
    JF  - International Journal of Clinical Urology
    JO  - International Journal of Clinical Urology
    SP  - 10
    EP  - 14
    PB  - Science Publishing Group
    SN  - 2640-1355
    UR  - https://doi.org/10.11648/j.ijcu.20220601.13
    AB  - Background: Prostate cancer (PCa) is prone to lymph node metastasis. In this report, the authors described a model predictive of the probability of lymph node metastasis in prostate cancer patients. Methods: Two-hundred seventy-eight middle-high-risk PCa patients who received laparoscopic radical prostatectomy (LRP) combined with extended pelvic lymph node dissection (e-PLND) in our hospital were selected as the subjects and the authors performed a retrospective analysis. According to the postoperative pathological results, the patients were divided into a pelvic lymph node metastasis group (n=100) and a non-pelvic lymph node metastasis group (n=178). Univariable and multivariable logistic regression analyses were performed to identify independent risk factors for pelvic lymph node metastasis from PCa. Finally, a clinical prediction model nomogram was further established and verified, and a calibration plot was drawn to verify the accuracy of the model. Results: The TPSA level, FPSA level, PI-RADS score, biopsy ISUP classification and Gleason score of the two groups were statistically different (P0.05). Receiver operating characteristic curve (ROC) showed that the best diagnostic cut-off value of TPSA was 77.45 ng/ml (AUC=0.785, 95%CI: 0.729-0.842), and the best diagnostic cut-off value of FPSA was 0.085 ng/ml (AUC=0.282, 95%CI: 0.215-0.348). Univariable and multivariable logistic regression analyses showed that, TPSA level (OR=1.00, 95%Cl: 1.000-1.006, PConclusions: The nomogram established in this study has a good predictive ability for pelvic lymph node metastasis in patients with PCa, and can provide a reference for the selection of clinical treatment options.
    VL  - 6
    IS  - 1
    ER  - 

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Author Information
  • Department of Urology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

  • Department of Urology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

  • Department of Urology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

  • Department of Urology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

  • Department of Urology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

  • Department of Urology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

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