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Pre- and Post-Operative Nomograms to Predict Recurrence-Free Probability in Korean Men with Clinically Localized Prostate Cancer

Author

Listed:
  • Minyong Kang
  • Chang Wook Jeong
  • Woo Suk Choi
  • Yong Hyun Park
  • Sung Yong Cho
  • Sangchul Lee
  • Seung Bae Lee
  • Ja Hyeon Ku
  • Sung Kyu Hong
  • Seok-Soo Byun
  • Hyeon Jeong
  • Cheol Kwak
  • Hyeon Hoe Kim
  • Eunsik Lee
  • Sang Eun Lee
  • Seoul National University-Uro-Oncology Group

Abstract

Objectives: Although the incidence of prostate cancer (PCa) is rapidly increasing in Korea, there are few suitable prediction models for disease recurrence after radical prostatectomy (RP). We established pre- and post-operative nomograms estimating biochemical recurrence (BCR)-free probability after RP in Korean men with clinically localized PCa. Patients and Methods: Our sampling frame included 3,034 consecutive men with clinically localized PCa who underwent RP at our tertiary centers from June 2004 through July 2011. After inappropriate data exclusion, we evaluated 2,867 patients for the development of nomograms. The Cox proportional hazards regression model was used to develop pre- and post-operative nomograms that predict BCR-free probability. Finally, we resampled from our study cohort 200 times to determine the accuracy of our nomograms on internal validation, which were designated with concordance index (c-index) and further represented by calibration plots. Results: Over a median of 47 months of follow-up, the estimated BCR-free rate was 87.8% (1 year), 83.8% (2 year), and 72.5% (5 year). In the pre-operative model, Prostate-Specific Antigen (PSA), the proportion of positive biopsy cores, clinical T3a and biopsy Gleason score (GS) were independent predictive factors for BCR, while all relevant predictive factors (PSA, extra-prostatic extension, seminal vesicle invasion, lymph node metastasis, surgical margin, and pathologic GS) were associated with BCR in the post-operative model. The c-index representing predictive accuracy was 0.792 (pre-) and 0.821 (post-operative), showing good fit in the calibration plots. Conclusions: In summary, we developed pre- and post-operative nomograms predicting BCR-free probability after RP in a large Korean cohort with clinically localized PCa. These nomograms will be provided as the mobile application-based SNUH Prostate Cancer Calculator. Our nomograms can determine patients at high risk of disease recurrence after RP who will benefit from adjuvant therapy.

Suggested Citation

  • Minyong Kang & Chang Wook Jeong & Woo Suk Choi & Yong Hyun Park & Sung Yong Cho & Sangchul Lee & Seung Bae Lee & Ja Hyeon Ku & Sung Kyu Hong & Seok-Soo Byun & Hyeon Jeong & Cheol Kwak & Hyeon Hoe Kim , 2014. "Pre- and Post-Operative Nomograms to Predict Recurrence-Free Probability in Korean Men with Clinically Localized Prostate Cancer," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-7, June.
  • Handle: RePEc:plo:pone00:0100053
    DOI: 10.1371/journal.pone.0100053
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    References listed on IDEAS

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    1. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    2. Chatterjee, Snigdhansu, 1998. "Another look at the jackknife: further examples of generalized bootstrap," Statistics & Probability Letters, Elsevier, vol. 40(4), pages 307-319, November.
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