Development and internal validation of a prediction model of prostate cancer on initial transperineal template-guided prostate biopsy

Abstract Background 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. 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. Result A 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. Conclusion We 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.

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Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

Frontiers in Oncology ◽

10.3389/fonc.2021.740868 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jin-feng Pan ◽

Rui Su ◽

Jian-zhou Cao ◽

Zhen-ya Zhao ◽

Da-wei Ren ◽

...

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Prediction Model ◽

Prostate Biopsy ◽

Clinical Decision Making ◽

Prediction Models ◽

Elevated Serum ◽

Training Group ◽

Validation Group ◽

Clinical Indicators

PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

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The Next Generation of Clinical Decision Making Tools: Development of a Real-Time Prediction Tool for Outcome of Prostate Biopsy in Response to a Continuously Evolving Prostate Cancer Landscape

The Journal of Urology ◽

10.1016/j.juro.2015.01.092 ◽

2015 ◽

Vol 194 (1) ◽

pp. 58-64 ◽

Cited By ~ 7

Author(s):

Andreas N. Strobl ◽

Ian M. Thompson ◽

Andrew J. Vickers ◽

Donna P. Ankerst

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Real Time ◽

Prostate Biopsy ◽

Clinical Decision Making ◽

Clinical Decision ◽

Prediction Tool ◽

Next Generation ◽

Time Prediction

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Quantitative and Qualitative Analysis of Blood-based Liquid Biopsies to Inform Clinical Decision-making in Prostate Cancer

European Urology ◽

10.1016/j.eururo.2020.12.037 ◽

2021 ◽

Author(s):

Irene Casanova-Salas ◽

Alejandro Athie ◽

Paul C. Boutros ◽

Marzia Del Re ◽

David T. Miyamoto ◽

...

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Qualitative Analysis ◽

Clinical Decision Making ◽

Clinical Decision ◽

Liquid Biopsies

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Beyond the Development of Health-Related Quality-of-Life (HRQOL) Measures: A Checklist for Evaluating HRQOL Outcomes in Cancer Clinical Trials—Does HRQOL Evaluation in Prostate Cancer Research Inform Clinical Decision Making?

Journal of Clinical Oncology ◽

10.1200/jco.2003.12.121 ◽

2003 ◽

Vol 21 (18) ◽

pp. 3502-3511 ◽

Cited By ~ 182

Author(s):

Fabio Efficace ◽

Andrew Bottomley ◽

David Osoba ◽

Carolyn Gotay ◽

Henning Flechtner ◽

...

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Clinical Trials ◽

Clinical Decision Making ◽

Clinical Decision ◽

Cancer Clinical Trials ◽

Health Related Quality ◽

Related Quality ◽

Health Related

Purpose: The aim of this study was to evaluate whether the inclusion of health-related quality of life (HRQOL), as a part of the trial design in a randomized controlled trial (RCT) setting, has supported clinical decision making for the planning of future medical treatments in prostate cancer. Materials and Methods: A minimum standard checklist for evaluating HRQOL outcomes in cancer clinical trials was devised to assess the quality of the HRQOL reporting and to classify the studies on the grounds of their robustness. It comprises 11 key HRQOL issues grouped into four broader sections: conceptual, measurement, methodology, and interpretation. Relevant studies were identified in a number of databases, including MEDLINE and the Cochrane Controlled Trials Register. Both their HRQOL and traditional clinical reported outcomes were systematically analyzed to evaluate their consistency and their relevance for supporting clinical decision making. Results: Although 54% of the identified studies did not show any differences in traditional clinical end points between treatment arms and 17% showed a difference in overall survival, 74% of the studies showed some difference in terms of HRQOL outcomes. One third of the RCTs provided a comprehensive picture of the whole treatment including HRQOL outcomes to support their conclusions. Conclusion: A minimum set of criteria for assessing the reported outcomes in cancer clinical trials is necessary to make informed decisions in clinical practice. Using a checklist developed for this study, it was found that HRQOL is a valuable source of information in RCTs of treatment in metastatic prostate cancer.

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