Danish Prostate Registry (DanProst) - an Updated Version of the Danish Prostate Cancer Registry, Methodology, and Early Results

In 2016, we introduced the Danish Prostate Cancer Registry (DaPCaR) which was built on the National Pathology Register from 1995 to 2011. DaPCaR was laborious to use as most data had to be manually imputed with no regular updates. In here we present a new comprehensive centralized prostate registry called the Danish Prostate Registry (DanProst), which includes all men having undergone any histological evaluation of prostate tissue merged with laboratory-, treatment-, prescription data as well as vital status. Here the data included and the methodology of DanProst are described. DanProst is built upon all men with a histological assessment of the prostate from the Danish National Registry for Pathology. The primary histology and potential prostate cancer histological diagnosis for each unique individual is extracted and translated by newly made algorithms for topography, procedure, diagnostic conclusion, and pathological staging. Further information is added from DaPCaR, the CPR Registry, the Danish Cause of Death Registry, the Danish Cancer Registry, the National Patient Registry, the Danish Register of Laboratory Results for Research, and the Danish National Prescription Registry. The translation algorithms were validated based on the comparison with DaPCaR in the period 2010-2016. DanProst includes 190,422 men. A total of 95,152 (50%) men are diagnosed with prostate cancer until 2021. Median diagnostic PSA was 11 ng/ml, most men are diagnosed by ultrasound-guided biopsy (N = 63,751; 67%), and most frequently defined primary treatment was radical prostatectomy (N = 14,778; 19%). DanProst to DaPCaR coherency was > 99%, 95%, and 94% for the primary histological procedure, primary histological conclusion, and diagnostic histological conclusion, respectively. DanProst is a continuously updated, centrally kept, validated registry with automatic integration of data from other national registries, allowing for contemporary nationwide analysis in men with histological assessment of the prostate.

Danish Prostate Cancer Registry - methodology and early results from a novel national database

Background

Systematized Nomenclature of Medicine (SNOMED) codes are computer-processable medical terms used to describe histopathological evaluations. SNOMED codes are not readily usable for analysis. We invented an algorithm that converts prostate SNOMED codes into an analyzable format. We present the methodology and early results from a new national Danish prostate database containing clinical data from all males who had evaluation of prostate tissue from 1995 to 2011.

Materials and methods

SNOMED codes were retrieved from the Danish Pathology Register. A total of 26,295 combinations of SNOMED codes were identified. A computer algorithm was developed to transcode SNOMED codes into an analyzable format including procedure (eg, biopsy, transurethral resection, etc), diagnosis, and date of diagnosis. For validation, ~55,000 pathological reports were manually reviewed. Prostate-specific antigen, vital status, causes of death, and tumor-node-metastasis classification were integrated from national registries.

Results

Of the 161,525 specimens from 113,801 males identified, 83,379 (51.6%) were sets of prostate biopsies, 56,118 (34.7%) were transurethral/transvesical resections of the prostate (TUR-Ps), and the remaining 22,028 (13.6%) specimens were derived from radical prostatectomies, bladder interventions, etc. A total of 48,078 (42.2%) males had histopathologically verified prostate cancer, and of these, 78.8% and 16.8% were diagnosed on prostate biopsies and TUR-Ps, respectively.

Future perspectives

A validated algorithm was successfully developed to convert complex prostate SNOMED codes into clinical useful data. A unique database, including males with both normal and cancerous histopathological data, was created to form the most comprehensive national prostate database to date. Potentially, our algorithm can be used for conversion of other SNOMED data and is available upon request.

Automated Bone Scan Index as a quantitative imaging biomarker in metastatic castration-resistant prostate cancer patients being treated with enzalutamide

Background

Having performed analytical validation studies, we are now assessing the clinical utility of the upgraded automated Bone Scan Index (BSI) in metastatic castration-resistant prostate cancer (mCRPC). In the present study, we retrospectively evaluated the discriminatory strength of the automated BSI in predicting overall survival (OS) in mCRPC patients being treated with enzalutamide.

Methods

Retrospectively, we included patients who received enzalutamide as a clinically approved therapy for mCRPC and had undergone bone scan prior to starting therapy. Automated BSI, prostate-specific antigen (PSA), hemoglobin (HgB), and alkaline phosphatase (ALP) were obtained at baseline. Change in automated BSI and PSA were obtained from patients who have had bone scan at week 12 of treatment follow-up. Automated BSI was obtained using the analytically validated EXINI Bone^BSI version 2. Kendall’s tau (τ) was used to assess the correlation of BSI with other blood-based biomarkers. Concordance index (C-index) was used to evaluate the discriminating strength of automated BSI in predicting OS.

Results

Eighty mCRPC patients with baseline bone scans were included in the study. There was a weak correlation of automated BSI with PSA (τ = 0.30), with HgB (τ = −0.17), and with ALP (τ = 0.56). At baseline, the automated BSI was observed to be predictive of OS (C-index 0.72, standard error (SE) 0.03). Adding automated BSI to the blood-based model significantly improved the C-index from 0.67 to 0.72, p = 0.017. Treatment follow-up bone scans were available from 62 patients. Both change in BSI and percent change in PSA were predictive of OS. However, the combined predictive model of percent PSA change and change in automated BSI (C-index 0.77) was significantly higher than that of percent PSA change alone (C-index 0.73), p = 0.041.

Conclusions

The upgraded and analytically validated automated BSI was found to be a strong predictor of OS in mCRPC patients. Additionally, the change in automated BSI demonstrated an additive clinical value to the change in PSA in mCRPC patients being treated with enzalutamide.