Abstract
BackgroundDue to the invasiveness of prostate biopsy, a prediction model of the individual risk of a positive biopsy result could be helpful to guide clinical decision-making. Most existing models are based on transrectal ultrasonography (TRUS)-guided biopsy. On the other hand, transperineal template-guided prostate biopsy (TTPB) has been reported to be more accurate in evaluating prostate cancer. The objective of this study is to develop a prediction model of the detection of high-grade prostate cancer (HGPC) on initial TTPB.ResultA total of 1352 out of 3794 (35.6%) patients were diagnosed with prostate cancer, 848 of whom had tumour with Grade Group 2–5. Age, PSA, PV, DRE and f/t PSA are independent predictors of HGPC with p < 0.001. The model showed good discrimination ability (c-index 0.886) and calibration during internal validation and good clinical performance was observed through decision curve analysis. The external validation of CPCC-RC, an existing model, demonstrated that models based on TRUS-guided biopsy may underestimate the risk of HGPC in patients who underwent TTPB.ConclusionWe established a prediction model which showed good discrimination ability and calibration in predicting the detection of HGPC by initial TTPB. This model can be used to aid clinical decision making for Chinese patients and other Asian populations with similar genomic backgrounds, after external validations are conducted to further confirm its clinical applicability.
Highlights
According to GLOBOCAN data in 2018, the incidence and mortality of prostate cancer ranked second and fifth, respectively, among all cancers in men [1]
We established a prediction model which showed good discrimination ability and calibration in predicting the detection of high-grade prostate cancer (HGPC) by initial template-guided prostate biopsy (TTPB). This model can be used to aid clinical decision making for Chinese patients and other Asian populations with similar genomic backgrounds, after external validations are conducted to further confirm its clinical applicability
The predicted risk of these models was reported to be overestimated by 20% in Chinese patients [5], which highlights the necessity of building a prediction model for Chinese patients, as well as Asian populations with similar genomic backgrounds
Summary
According to GLOBOCAN data in 2018, the incidence and mortality of prostate cancer ranked second and fifth, respectively, among all cancers in men [1]. Due to the invasive nature of biopsy, it would be very helpful if the individual risk of a positive biopsy result can be calculated through prediction models and guide clinical decision-making. The incidence and prevalence of prostate cancer in Asian populations are significantly lower than in individuals of Caucasian and African descent [1, 2], suggesting ethnic differences in the occurrence of prostate cancer. The most widely used and well-validated prediction models for prostate biopsy are the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) and the. Due to the invasiveness of prostate biopsy, a prediction model of the individual risk of a positive biopsy result could be helpful to guide clinical decision-making. Transperineal template-guided prostate biopsy (TTPB) has been reported to be more accurate in evaluating prostate cancer. The objective of this study is to develop a prediction model of the detection of high-grade prostate cancer (HGPC) on initial TTPB
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