Purpose

Comparing patient reported outcomes such as urinary and erectile function across institutions is critical for prostate cancer research and quality assurance. Such comparisons are complicated due to the use of different questionnaires. We aimed to develop a method to convert scores among 4 commonly used instruments.

Materials and methods

Patient reported data on urinary and sexual function were collected on 1,284 men with localized prostate cancer using the EPIC-26 (Expanded Prostate Index Composite), PCI (UCLA Prostate Cancer Index), SHIM (Sexual Health Inventory for Men) and I-PSS (International Prostate Symptom Scale) questionnaires. We investigated several methods to convert scores among questionnaires.

Results

Conversion between EPIC and PCI urinary and sexual function subscales was best achieved using only the subset of questions asked on both questionnaires. For the conversion between EPIC or PCI erectile function scores and SHIM scores, we defined thresholds of poor, intermediate and good function as EPIC/PCI 0 to 40 and SHIM 1 to 7, EPIC/PCI 41 to 59 and SHIM 8 to 16, and EPIC/PCI 60 to 100 and SHIM 17 to 25, respectively. Urinary continence scores highly correlated for PCI and EPIC (r = 0.94). No comparison was possible for I-PSS with EPIC and PCI due to differences in the domains addressed by these questionnaires.

Conclusions

We have introduced methods to convert scores among the EPIC, PCI and SHIM questionnaires. While these conversion methods may introduce minor imprecision, to our knowledge they represent the best available tools to combine and compare patient reported outcomes that are assessed using different instruments in men undergoing radical prostatectomy or active surveillance.

Objective

To assess whether adding prostate volume to the kallikrein panel improves discrimination for ISUP Grade Group 2 or higher (GG2+) disease, as some men may have volume measurements available at the time of blood draw. While prostate volume predicts biopsy outcome, it requires an imaging procedure for measurement. The four kallikrein panel - commercially available as the 4Kscore - predicts risk of GG2+ disease and requires only a blood draw.

Materials and methods

A total of 9131 patients with available prostate volume and total PSA ≤25 ng/ml from 5 historical (sextant biopsy, pre-ISUP 2005 grading) and 4 contemporary cohorts (10+ cores, ISUP 2005 grading). Previously published kallikrein panel models were used to predict risk of GG2+. Volume was added to the model in each cohort and change in discrimination was meta-analyzed.

Results

Increased prostate volume was associated with decreased risk of GG2+ disease after controlling for the kallikrein panel in 7/9 cohorts. However, kallikrein panel discrimination (0.817, 95% CI 0.802, 0.831) was not improved after including volume (AUC difference 0.002, 95% CI -0.003, 0.006). Heterogeneity (P <.0001) was driven by an AUC increase in 1 cohort of academic cancer centers (0.044, 95% CI 0.025, 0.064), with no evidence of heterogeneity after excluding this cohort (P = .15).

Conclusion

The kallikrein panel provides a non-invasive approach to assess the risk of high-grade prostate cancer. Our results do not justify the inclusion of prostate volume in the four kallikrein panel. There is some evidence that the predictive value of prostate volume is provider dependent: further research is needed to address this question.

Objectives

To prospectively evaluate the performance of a pre-specified statistical model based on four kallikrein markers in blood (total prostate-specific antigen [PSA], free PSA, intact PSA, and human kallikrein-related peptidase 2), commercially available as the 4Kscore, in predicting Gleason Grade Group (GG) ≥2 prostate cancer at biopsy in an international multicentre study at three academic medical centres, and whether microseminoprotein-β (MSP) adds predictive value.

Patients and methods

A total of 984 men were prospectively enrolled at three academic centres. The primary outcome was GG ≥2 on prostate biopsy. Three pre-specified statistical models were used: a base model including PSA, age, digital rectal examination and prior negative biopsy; a model that added free PSA to the base model; and the 4Kscore.

Results

A total of 947 men were included in the final analysis and 273 (29%) had GG ≥2 on prostate biopsy. The base model area under the receiver operating characteristic curve of 0.775 increased to 0.802 with the addition of free PSA, and to 0.824 for the 4Kscore. Adding MSP to the 4Kscore model yielded an increase (0.014-0.019) in discrimination. In decision-curve analysis of clinical utility, the 4Kscore showed a benefit starting at a 7.5% threshold.

Conclusion

A prospective multicentre evaluation of a pre-specified model based on four kallikrein markers (4Kscore) with the addition of MSP improves the predictive discrimination for GG ≥2 prostate cancer on biopsy and could be used to inform biopsy decision-making.

Background

Prostate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems.

Objective

We prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool.

Design, setting, and participants

We obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006-2017 at eight North American institutions for model-building and three European institutions for validation.

Outcome measurements and statistical analysis

We used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts.

Results and limitations

Cross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2-76.8), a small improvement over the AUC of 72.3% (70.9-73.7) for the PCPTRC (p<0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies.

Conclusions

The PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome.

Patient summary

A contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making.