Oncotarget

Clinical Research Papers:

Nomograms for predicting Gleason upgrading in a contemporary Chinese cohort receiving radical prostatectomy after extended prostate biopsy: development and internal validation

Biming He, Rui Chen, Xu Gao, Shancheng Ren, Bo Yang, Jianguo Hou, Linhui Wang, Qing Yang, Tie Zhou, Lin Zhao, Chuanliang Xu and Yinghao Sun _

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Oncotarget. 2016; 7:17275-17285. https://doi.org/10.18632/oncotarget.7787

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Abstract

Biming He1,*, Rui Chen1,*, Xu Gao1,*, Shancheng Ren1, Bo Yang1, Jianguo Hou1, Linhui Wang1, Qing Yang1, Tie Zhou1, Lin Zhao1, Chuanliang Xu1, Yinghao Sun1

1Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China

*These authors have contributed equally to this work

Correspondence to:

Yinghao Sun, e-mail: sunyhsmmu@126.com

Keywords: prostate cancer, Gleason score upgrading, nomogram, prostate biopsy, prostatectomy

Received: October 21, 2015    Accepted: February 09, 2016    Published: February 29, 2016

ABSTRACT

The current strategy for the histological assessment of prostate cancer (PCa) is mainly based on the Gleason score (GS). However, 30-40% of patients who undergo radical prostatectomy (RP) are misclassified at biopsy pathologically. Thus, we developed and validated nomograms for the prediction of Gleason score upgrading (GSU) in patients who underwent radical prostatectomy after extended prostate biopsy in a Chinese population. This retrospective study included a total of 411 patients who underwent radical prostatectomy at our institute after having prostate biopsies between 2011 and 2015. The final pathologic GS was upgraded in 151 (36.74%) of the cases in all patients and 92 (60.13%) cases in men with GS=6. In multivariate analyses, the primary biopsy GS, secondary biopsy GS and obesity were predictive of GSU in the patient cohort assessed. In patients with GS=6, the significant predictors of GSU included the body mass index (BMI), prostate-specific antigen density(PSAD) and percentage of positive cores. The area under the curve (AUC) of the prediction models was 0.753 for the entire patient population and 0.727 for the patients with GS=6. Both nomograms were well calibrated, and decision curve analysis demonstrated a high net benefit across a wide range of threshold probabilities. This study may be relevant for improved risk assessment and clinical decision-making in PCa patients.


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