A risk calculator to inform the need for a prostate biopsy: a rapid access clinic cohort

Abstract

Background: Prostate cancer (PCa) represents a significant healthcare problem. The critical clinical question is the need for a biopsy. Accurate risk stratification of patients before a biopsy can allow for individualised risk stratification thus improving clinical decision making. This study aims to build a risk calculator to inform the need for a prostate biopsy.

Methods: Using the clinical information of 4801 patients an Irish Prostate Cancer Risk Calculator (IPRC) for diagnosis of PCa and high grade (Gleason ≥7) was created using a binary regression model including age, digital rectal examination, family history of PCa, negative prior biopsy and Prostate-specific antigen (PSA) level as risk factors. The discrimination ability of the risk calculator is internally validated using cross validation to reduce overfitting, and its performance compared with PSA and the American risk calculator (PCPT), Prostate Biopsy Collaborative Group (PBCG) and European risk calculator (ERSPC) using various performance outcome summaries. In a subgroup of 2970 patients, prostate volume was included. Separate risk calculators including the prostate volume (IPRCv) for the diagnosis of PCa (and high-grade PCa) was created.

Results: IPRC area under the curve (AUC) for the prediction of PCa and high-grade PCa was 0.6741 (95% CI, 0.6591 to 0.6890) and 0.7214 (95% CI, 0.7018 to 0.7409) respectively. This significantly outperforms the predictive ability of cancer detection for PSA (0.5948), PCPT (0.6304), PBCG (0.6528) and ERSPC (0.6502) risk calculators; and also, for detecting high-grade cancer for PSA (0.6623) and PCPT (0.6804) but there was no significant improvement for PBCG (0.7185) and ERSPC (0.7140). The inclusion of prostate volume into the risk calculator significantly improved the AUC for cancer detection (AUC = 0.7298; 95% CI, 0.7119 to 0.7478), but not for high-grade cancer (AUC = 0.7256; 95% CI, 0.7017 to 0.7495). The risk calculator also demonstrated an increased net benefit on decision curve analysis.

Conclusion: The risk calculator developed has advantages over prior risk stratification of prostate cancer patients before the biopsy. It will reduce the number of men requiring a biopsy and their exposure to its side effects. The interactive tools developed are beneficial to translate the risk calculator into practice and allows for clarity in the clinical recommendations.

Keywords: Binary logistic regression, cross-validation, Rshiny; Biopsy; Decision-making; Prostate Cancer; Risk calculator.

Fig. 1

IPRC nomograms. The nomograms for PCa model is on the left, and high-grade PCa model on the right. The horizontal line on the top labelled `points’ allows the effect size of each variable to be assessed. To use the nomogram draw a straight line from the values/levels of each variable to measure its corresponding point. The total points on the bottom are then mapped to obtain the risk of cancer or high-grade cancer

Fig. 2

IPRC calibration and model comparison. The receiver operating characteristic (ROC) curves on the left and decision curves in the middle represent the discriminative ability of PCPT (red), PBCG (orange), ERSPC (blue) and IPRC (green) in diagnosis cancer (on top) and high-grade cancer (on the bottom). The calibration curves on the right indicate that predicted probabilities of both IPRC models are almost similar to the actual outcomes

Fig. 3

IPRC discrimination ability. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Youden index of the PCPT (red), PBCG (orange), ERSPC (blue) and IPRC (green) on the variously selected thresholds. The PCa model is displayed on the left and high-grade PCa model on the right

Fig. 4

IPRCv nomograms. The nomograms (based on IPRCv which including prostate volume) for PCa model is on the left and high-grade PCa model on the right. The horizontal line on the top labelled `points’ allows the effect size of each variable to be assessed. To use the nomogram draw a straight line from the values/levels of each variable to measure its corresponding point. The total points on the bottom are then mapped to obtain the risk of cancer or high-grade cancer