Validation of multivariable lung cancer risk prediction models for the personalized assignment of optimal screening frequency: a retrospective analysis of data from the German Lung Cancer Screening Intervention Trial (LUSI).

BACKGROUND: Current guidelines for lung cancer screening via low-dose computed tomography recommend annual screening for all candidates meeting basic eligibility criteria. However, lung cancer risk of eligible screening participants can vary widely, and further risk stratification could be used to individually optimize screening intervals in view of expected benefits, possible harms and financial costs. To this effect, models have been developed in the US National Lung Screening Trial based on self-reported lung cancer risk factors and imaging data. We evaluated these models using data from an independent screening trial in Germany. METHODS: We examined the Polynomial model by Schreuder et al., the Lung Cancer Risk Assessment Tool extended by CT characteristics (LCRAT + CT) by Robbins et al., and a criterion of presence vs. absence of pulmonary nodules ≥4 mm (Patz et al.), applied to sub-sets of screening participants according to eligibility criteria. Discrimination was evaluated via the receiver operating characteristic curve. Delayed diagnoses and false positive results were calculated at various thresholds of predicted risk. Model calibration was assessed by comparing mean predicted risk versus observed incidence. RESULTS: One thousand five hundred and six participants were eligible for the validation of the LCRAT + CT model, and 1,889 for the validation of the Polynomial model and Patz criterion, yielding areas under the receiver operating characteristic curve of 0.73 (95% CI: 0.63, 0.82), 0.75 (0.67, 0.83), and 0.56 (0.53, 0.72) respectively. Skipping 50% annual screenings (participants within the 5 lowest risk deciles by LCRAT + CT in any round or by the Polynomial model; baseline screening round), would have avoided 75% (21.9%, 98.7%) and 40% (21.8%, 61.1%) false positive screen tests and delayed 10% (1.8%, 33.1%) or no (0%, 32.1%) diagnoses, respectively. Using the Patz criterion, referring 63.2% (61.0% to 65.4%) of participants to biennial screening would have avoided 4% (0.2% to 22.3%) of false positive screen tests but delayed 55% (24.6% to 81.9%) diagnoses. CONCLUSIONS: In this German trial, the LCRAT + CT and Polynomial models showed useful discrimination of screening participants for one-year lung cancer risk following CT examination. Our results illustrate the remaining heterogeneity in risk within screening-eligible subjects and the trade-off between a low-frequency screening approach and delayed detection.

Can autoantibody tests enhance lung cancer screening?-an evaluation of EarlyCDT®-Lung in context of the German Lung Cancer Screening Intervention Trial (LUSI).

BACKGROUND: Tumor-associated autoantibodies are considered promising markers for early lung cancer detection; so far, however, their capacity to detect cancer has been tested mostly in a clinical context, but not in population screening settings. This study evaluates the early detection accuracy, in terms of sensitivity and specificity, of EarlyCDT®-Lung-a test panel of seven tumor-associated autoantibodies optimized for lung cancer detection-using blood samples originally collected as part of the German Lung Cancer Screening Intervention Trial. METHODS: The EarlyCDT®-Lung test was performed for all participants with lung cancer detected via low-dose computed tomography and with available blood samples taken at detection, and for 180 retrospectively selected cancer-free participants at the end of follow-up: 90 randomly selected from among all cancer-free participants (baseline controls) and 90 randomly selected from among cancer-free participants with suspicious imaging findings (suspicious nodules controls). Sensitivity and specificity of lung cancer detection were estimated in the case group and the two control groups, respectively. RESULTS: In the case group, the test panel showed a sensitivity of only 13.0% (95% CI: 4.9-26.3%). Specificity was estimated at 88.9% (95% CI: 80.5-94.5%) in the baseline control group, and 91.1% (95% CI: 83.2-96.1%) among controls presenting CT-detected nodules. CONCLUSIONS: The test panel showed insufficient sensitivity for detecting lung cancer at an equally early stage as with low-dose computed tomography screening.

Overdiagnosis in lung cancer screening: Estimates from the German Lung Cancer Screening Intervention Trial.

Overdiagnosis is a major potential harm of lung cancer screening; knowing its potential magnitude helps to optimize screening eligibility criteria. The German Lung Screening Intervention Trial ("LUSI") is a randomized trial among 4052 long-term smokers (2622 men), 50.3 to 71.9 years of age from the general population around Heidelberg, Germany, comparing five annual rounds of low-dose computed tomography (n = 2029) with a control arm without intervention (n = 2023). After a median follow-up of 9.77 years postrandomization and 5.73 years since last screening, 74 participants were diagnosed with lung cancer in the control arm and 90 in the screening arm: 69 during the active screening period; of which 63 screen-detected and 6 interval cancers. The excess cumulative incidence in the screening arm (N = 16) represented 25.4% (95% confidence interval: -11.3, 64.3] of screen-detected cancer cases (N = 63). Analyzed by histologic subtype, excess incidence in the screening arm appeared largely driven by adenocarcinomas. Statistical modeling yielded an estimated mean preclinical sojourn time (MPST) of 5.38 (4.76, 5.88) years and a screen-test sensitivity of 81.6 (74.4%, 88.8%) for lung cancer overall, all histologic subtypes combined. Based on modeling, we further estimated that about 48% (47.5% [43.2%, 50.7%]) of screen-detected tumors have a lead time ≥4 years, whereas about 33% (32.8% [28.4%, 36.1%]) have a lead time ≥6 years, 23% (22.6% [18.6%, 25.7%]) ≥8 years, 16% (15.6% [12.2%, 18.3%]) ≥10 years and 11% (10.7% [8.0%, 13.0%]) ≥12 years. The high proportions of tumors with relatively long lead times suggest a major risk of overdiagnosis for individuals with comparatively short remaining life expectancies.

Vascular injury biomarkers and stroke risk: A population-based study.

OBJECTIVE: Because little is known about associations between biomarkers of vascular injury and stroke risk, we evaluated associations between plasma concentrations of 6 novel biomarkers of vascular injury and stroke risk in a population-based study. METHODS: A case-cohort subset of EPIC-Heidelberg (European Prospective Investigation for Cancer and Nutrition-Heidelberg) including incident stroke cases (n = 335) and a random subcohort (n = 2,418) was selected. Concentrations of intercellular adhesion molecule 3 (ICAM3), soluble E-selectin and P-selectin, soluble thrombomodulin (sTM), thrombopoietin, and glycoprotein IIb/IIIa were measured in baseline plasma samples. Weighted Cox regression analyses were used to assess associations between biomarker levels and stroke risk. RESULTS: Median follow-up in the subcohort and among cases was 9.8 (range, 0.1-12.5) years and 6.2 (range, 0.01-12.1) years, respectively. ICAM3 levels were associated with increased risk of incident stroke after multivariable adjustment (hazard ratio, highest vs lowest quartile: 1.64 [95% confidence interval, 1.15-2.32]; p linear trend < 0.001). This association was more apparent for ischemic (1.65 [1.12-2.45]; p linear trend < 0.01) than for hemorrhagic stroke (1.29 [0.60-2.78]; p linear trend = 0.3). We further observed a borderline significant trend for a positive association between sTM and overall stroke risk (1.47 [0.99-2.19]; p linear trend = 0.05). CONCLUSIONS: In this population-based study, circulating levels of ICAM3, an adhesion molecule shed by leukocytes, were associated with increased risk of incident stroke. Further mechanistic studies are needed to elucidate the pathophysiology underlying this association. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that plasma levels of ICAM3 are associated with increased stroke risk.

Serologic markers of Chlamydia trachomatis and other sexually transmitted infections and subsequent ovarian cancer risk: Results from the EPIC cohort.

A substantial proportion of epithelial ovarian cancer (EOC) arises in the fallopian tube and other epithelia of the upper genital tract; these epithelia may incur damage and neoplastic transformation after sexually transmitted infections (STI) and pelvic inflammatory disease. We investigated the hypothesis that past STI infection, particularly Chlamydia trachomatis, is associated with higher EOC risk in a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort including 791 cases and 1669 matched controls. Serum antibodies against C. trachomatis, Mycoplasma genitalium, herpes simplex virus type 2 (HSV-2) and human papillomavirus (HPV) 16, 18 and 45 were assessed using multiplex fluorescent bead-based serology. Conditional logistic regression was used to estimate relative risks (RR) and 95% confidence intervals (CI) comparing women with positive vs. negative serology. A total of 40% of the study population was seropositive to at least one STI. Positive serology to C. trachomatis Pgp3 antibodies was not associated with EOC risk overall, but with higher risk of the mucinous histotype (RR = 2.30 [95% CI = 1.22-4.32]). Positive serology for chlamydia heat shock protein 60 (cHSP60-1) was associated with higher risk of EOC overall (1.36 [1.13-1.64]) and with the serous subtype (1.44 [1.12-1.85]). None of the other evaluated STIs were associated with EOC risk overall; however, HSV-2 was associated with higher risk of endometrioid EOC (2.35 [1.24-4.43]). The findings of our study suggest a potential role of C. trachomatis in the carcinogenesis of serous and mucinous EOC, while HSV-2 might promote the development of endometrioid disease.

Circulating Immune Cell Composition and Cancer Risk: A Prospective Study Using Epigenetic Cell Count Measures.

Although ample evidence indicates that immune cell homeostasis is an important prognostic outcome determinant in patients with cancer, few studies have examined whether it also determines cancer risk among initially healthy individuals. We performed a case-cohort study including incident cases of breast (n = 207), colorectal (n = 111), lung (n = 70), and prostate (n = 201) cancer as well as a subcohort (n = 465) within the European Prospective Investigation into Cancer and Nutrition-Heidelberg cohort. Relative counts of neutrophils, monocytes, and lymphocyte sublineages were measured by qRT-PCR. HRs and 95% confidence intervals were used to measure the associations between relative counts of immune cell and cancer risks. When relative counts of immune cell types were taken individually, a significant positive association was observed between relative counts of FOXP3+ regulatory T cells (Tregs) and lung cancer risk, and significant inverse associations were observed between relative CD8+ counts and risks of lung and breast cancer (overall and ER+ subtype). Multivariable models with mutual adjustments across immune markers showed further significant positive associations between higher relative FOXP3+ T-cell counts and increased risks of colorectal and breast cancer (overall and ER- subtype). No associations were found between immune cell composition and prostate cancer risk. These results affirm the relevance of elevated FOXP3+ Tregs and lower levels of cytotoxic (CD8+) T cells as risk factors for tumor development. SIGNIFICANCE: This epidemiologic study supports a role for both regulatory and cytotoxic T cells in determining cancer risk among healthy individuals.See related commentary by Song and Tworoger, p. 1801.

Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.

Importance: Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening. Objective: To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the German Lung Cancer Screening Intervention trial. Design, Setting, and Participants: In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screening Intervention trial, a randomized clinical trial conducted from October 23, 2007, to April 30, 2016, with ongoing follow-up. This analysis considers end points up to 1 year after individuals' last screening visit. Inclusion criteria for participants were at least 1 noncalcified pulmonary nodule detected on any of 5 annual screening visits, receiving a lung cancer diagnosis within the active screening phase of the Lung Cancer Screening Intervention trial, and an unequivocal identification of the malignant nodules. Data analysis was performed from February 1, 2019, through December 5, 2019. Interventions: Five annual rounds of low-dose multislice CT. Main Outcomes and Measures: Discrimination ability and calibration of malignancy probabilities estimated by 5 models developed in data from screening studies (4 Pan-Canadian Early Detection of Lung Cancer Study [PanCan] models using a parsimonious approach including nodule spiculation [PanCan-1b] or a comprehensive approach including nodule spiculation [PanCan-2b], and PanCan-2b replacing the nodule diameter variable with mean diameter [PanCan-MD] or volume [PanCan-VOL], as well as a model developed by the UK Lung Cancer Screening trial) and 3 models developed in clinical settings (US Department of Veterans Affairs, Mayo Clinic, and Peking University People's Hospital). Results: A total of 1159 participants (median [range] age, 57.63 [50.34-71.89] years; 763 [65.8%] men) with 3903 pulmonary nodules were included in this study. For nodules detected in the prevalence round of CT, the PanCan models showed excellent discrimination (PanCan-1b: area under the curve [AUC], 0.93 [95% CI, 0.87-0.99]; PanCan-2b: AUC, 0.94 [95% CI, 0.89-0.99]; PanCan-MD: AUC, 0.94 [95% CI, 0.91-0.98]; PanCan-VOL: AUC, 0.94 [95% CI, 0.90-0.98]), and all of the screening models except PanCan-MD and PanCan-VOL showed acceptable calibration (PanCan-1b: Spiegelhalter z = -1.081; P = .28; PanCan-2b: Spiegelhalter z = 0.436; P = .67; PanCan-MD: Spiegelhalter z = 3.888; P < .001; PanCan-VOL: Spiegelhalter z = 1.978; P = .05; UK Lung Cancer Screening trial: Spiegelhalter z = -1.076; P = .28), whereas the other models showed worse discrimination and calibration, from an AUC of 0.58 (95% CI, 0.46-0.70) for the UK Lung Cancer Screening trial model to an AUC of 0.89 (95% CI, 0.82-0.97) for the Mayo Clinic model. Conclusions and Relevance: This diagnostic study found that PanCan models showed excellent discrimination and calibration in prevalence screenings, confirming their ability to improve nodule management in screening settings, although calibration to nodules detected in follow-up scans should be improved. The models developed by the Mayo Clinic, Peking University People's Hospital, Department of Veterans Affairs, and UK Lung Cancer Screening Trial did not perform as well.

Prognostic associations of circulating phytoestrogens and biomarker changes in long-term survivors of postmenopausal breast cancer.

Lignans are associated with improved postmenopausal breast cancer (BC) survival, but whether these associations, particularly with enterolactone (major lignan metabolite), persist over time is unclear. Little is known about other phytoestrogens on prognosis in long-term survivors. The study examines associations of prognosis with 1) circulating postdiagnosis enterolactone, 2) eight circulating phytoestrogen metabolites, and 3) changes in enterolactone and genistein. In a German cohort of 2,105 postmenopausal BC patients with blood samples collected at recruitment 2002-2005 (baseline) and re-interview in 2009 (follow-up), delay-entry Cox proportional hazards regression was used. Landmark analysis showed that circulating enterolactone (log2) associations with 5-year survival changed over time, with strongest hazard ratios of 0.89 (95% CI, 0.80-0.99) at blood draw (BD) and 0.86 (0.77-0.97) at 2 years post-BD for BC mortality, and 0.87 (0.80-0.95) at BD and 0.84 (0.76-0.92) at 3 years post-BD for all-cause mortality, which attenuated thereafter. In long-term survivors, increasing concentrations of genistein (1.17, 1.01-1.36), resveratrol (1.19, 1.02-1.40), and luteolin (1.96, 1.07-3.58) measured in follow-up blood samples were associated with poorer subsequent prognosis. Neither enterolactone at follow-up nor changes in enterolactone/genistein were associated with prognosis. Large long-term longitudinal studies with multiple phytoestrogen measurements are required to understand long-term effects of phytoestrogens after BC.

Plasma Fibrinogen and sP-Selectin are Associated with the Risk of Lung Cancer in a Prospective Study.

BACKGROUND: While enhanced platelet activation and a procoagulant state may drive lung cancer progression and metastases, less is known about their role in earlier phases of cancer development. Thus, we evaluated whether prediagnostic biomarkers of platelet activation and coagulation are related to the risk of lung cancer in the prospective EPIC-Heidelberg Study using a case-cohort design. METHODS: Levels of fibrinogen, soluble glycoprotein (sGP) IIb/IIIa, soluble P-selectin (sP-selectin), soluble thrombomodulin (sTM), and thrombopoietin (TPO) were measured in baseline plasma samples of a random subcohort (n = 2,480) and incident cases of lung cancer (n = 190). Multivariable-adjusted Cox proportional hazards regression analyses were used to obtain HRs of lung cancer across quartiles of biomarker levels. RESULTS: Fibrinogen [HR highest vs. lowest quartile: 1.91 (95% confidence interval: 1.09-3.34)] and sP-Selectin [HR: 2.51 (1.39-4.52)] were significantly associated with lung cancer risk in multivariable adjusted Cox regression models. Adding both biomarkers to the established PLCOm2012 algorithm, which alone showed a C-statistic of 0.788, led to a slight increment in lung cancer risk prediction, with a C-statistic of 0.814. CONCLUSION: Our findings indicate that enhanced platelet activation and a procoagulative state contribute to lung carcinogenesis. IMPACT: The current prospective study supports the hypothesis of increased coagulation being a possible driver of lung carcinogenesis, as strong positive associations were found between two procoagulative markers, sP-Selectin and fibrinogen, with lung cancer risk. Both biomarkers could improve lung cancer risk prediction, but external validation of the results is needed.

Biomarkers of vascular injury in relation to myocardial infarction risk: A population-based study.

Little is known about circulating biomarkers of vascular injury in relation to cardiovascular disease risk. Thus, we evaluated associations between six novel markers (E-Selectin, P-Selectin, thrombomodulin, thrombopoietin, intercellular adhesion molecule 3 and GPIIb/IIIa) and established cardiovascular risk factors as well as the risk of myocardial infarction (MI) in a population-based study. Biomarkers were measured in pre-diagnostic plasma samples of a case-cohort subset of EPIC-Heidelberg (incident MI cases: n = 369, random sub-cohort: n = 2,418). Generalized Linear models were used to analyse cross-sectional associations between biomarkers and cardiovascular risk factors. Multivariable Cox Regression analyses were carried out to obtain Hazard Ratios (HRs) of MI across quartiles of biomarkers levels. Cross-sectional analyses showed that sex, smoking, alcohol consumption, diabetes and exogenous hormone use were associated with biomarker levels. However, while fibrinogen was associated with MI risk (HR per standard deviation: 2.97 [95% confidence interval: 1.61, 5.46]), none of the six novel biomarkers was associated with MI risk after multivariable adjustment. In a population-based cohort, biomarkers of vascular injury were associated with established cardiovascular risk factors, but not MI risk. The tested biomarkers may reflect pathophysiological alterations in cardiovascular disease development rather than constituting independent MI risk factors.

Comprehensive plasma and tissue profiling reveals systemic metabolic alterations in cardiac hypertrophy and failure.

AIMS: Heart failure is characterized by structural and metabolic cardiac remodelling. The aim of the present study is to expand our understanding of the complex metabolic alterations in the transition from pathological hypertrophy to heart failure and exploit the results from a translational perspective. METHODS AND RESULTS: Mice were subjected to transverse aortic constriction (TAC) or sham surgery and sacrificed 2 weeks, 4 weeks, or 6 weeks after the procedure. Samples from plasma, liver, skeletal muscle, and heart were collected and analysed using metabolomics. Cardiac samples were also analysed by transcriptional profiling. Progressive alterations of key cardiac metabolic pathways and gene expression patterns indicated impaired mitochondrial function and a metabolic switch during transition to heart failure. Similar to the heart, liver, and skeletal muscle revealed significant metabolic alterations such as depletion of essential fatty acids and glycerolipids in late stages of heart failure. Circulating metabolites, particularly fatty acids, reflected cardiac metabolic defects, and deteriorating heart function. For example, inverse correlation was found between plasma and the heart levels of triacylglycerol (C18:1, C18:2, C18:3), and sphingomyelin (d18:1, C23:0) already at an early stage of heart failure. Interestingly, combining metabolic and transcriptional data from cardiac tissue revealed that decreased carnitine shuttling and transportation preceded mitochondrial dysfunction. We, thus, studied the therapeutic potential of OCTN2 (Organic Cation/Carnitine Transporter 2), an important factor for carnitine transportation. Cardiac overexpression of OCTN2 using an adeno-associated viral vector significantly improved ejection fraction and reduced interstitial fibrosis in mice subjected to TAC. CONCLUSION: Comprehensive plasma and tissue profiling reveals systemic metabolic alterations in heart failure, which can be used for identification of novel biomarkers and potential therapeutic targets.

Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis.

OBJECTIVE: Current non-invasive diagnostic tests can distinguish between pancreatic cancer (pancreatic ductal adenocarcinoma (PDAC)) and chronic pancreatitis (CP) in only about two thirds of patients. We have searched for blood-derived metabolite biomarkers for this diagnostic purpose. DESIGN: For a case-control study in three tertiary referral centres, 914 subjects were prospectively recruited with PDAC (n=271), CP (n=282), liver cirrhosis (n=100) or healthy as well as non-pancreatic disease controls (n=261) in three consecutive studies. Metabolomic profiles of plasma and serum samples were generated from 477 metabolites identified by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry. RESULTS: A biomarker signature (nine metabolites and additionally CA19-9) was identified for the differential diagnosis between PDAC and CP. The biomarker signature distinguished PDAC from CP in the training set with an area under the curve (AUC) of 0.96 (95% CI 0.93-0.98). The biomarker signature cut-off of 0.384 at 85% fixed specificity showed a sensitivity of 94.9% (95% CI 87.0%-97.0%). In the test set, an AUC of 0.94 (95% CI 0.91-0.97) and, using the same cut-off, a sensitivity of 89.9% (95% CI 81.0%-95.5%) and a specificity of 91.3% (95% CI 82.8%-96.4%) were achieved, successfully validating the biomarker signature. CONCLUSIONS: In patients with CP with an increased risk for pancreatic cancer (cumulative incidence 1.95%), the performance of this biomarker signature results in a negative predictive value of 99.9% (95% CI 99.7%-99.9%) (training set) and 99.8% (95% CI 99.6%-99.9%) (test set). In one third of our patients, the clinical use of this biomarker signature would have improved diagnosis and treatment stratification in comparison to CA19-9.

Fast Filtration of Bacterial or Mammalian Suspension Cell Cultures for Optimal Metabolomics Results.

The metabolome offers real time detection of the adaptive, multi-parametric response of the organisms to environmental changes, pathophysiological stimuli or genetic modifications and thus rationalizes the optimization of cell cultures in bioprocessing. In bioprocessing the measurement of physiological intracellular metabolite levels is imperative for successful applications. However, a sampling method applicable to all cell types with little to no validation effort which simultaneously offers high recovery rates, high metabolite coverage and sufficient removal of extracellular contaminations is still missing. Here, quenching, centrifugation and fast filtration were compared and fast filtration in combination with a stabilizing washing solution was identified as the most promising sampling method. Different influencing factors such as filter type, vacuum pressure, washing solutions were comprehensively tested. The improved fast filtration method (MxP® FastQuench) followed by routine lipid/polar extraction delivers a broad metabolite coverage and recovery reflecting well physiological intracellular metabolite levels for different cell types, such as bacteria (Escherichia coli) as well as mammalian cells chinese hamster ovary (CHO) and mouse myeloma cells (NS0).The proposed MxP® FastQuench allows sampling, i.e. separation of cells from medium with washing and quenching, in less than 30 seconds and is robustly designed to be applicable to all cell types. The washing solution contains the carbon source respectively the 13C-labeled carbon source to avoid nutritional stress during sampling. This method is also compatible with automation which would further reduce sampling times and the variability of metabolite profiling data.

The prostate cancer risk stratification (ProCaRS) project: recursive partitioning risk stratification analysis.

BACKGROUND: The Genitourinary Radiation Oncologists of Canada (GUROC) published a three-group risk stratification (RS) system to assist prostate cancer decision-making in 2001. The objective of this project is to use the ProCaRS database to statistically model the predictive accuracy and clinical utility of a proposed new multi-group RS schema. METHODS: The RS analyses utilized the ProCaRS database that consists of 7974 patients from four Canadian institutions. Recursive partitioning analysis (RPA) was utilized to explore the sub-stratification of groups defined by the existing three-group GUROC scheme. 10-fold cross-validated C-indices and the Net Reclassification Index were both used to assess multivariable models and compare the predictive accuracy of existing and proposed RS systems, respectively. RESULTS: The recursive partitioning analysis has suggested that the existing GUROC classification system could be altered to accommodate as many as six separate and statistical unique groups based on differences in BFFS (C-index 0.67 and AUC 0.70). GUROC low-risk patients would be divided into new favorable-low and low-risk groups based on PSA ⩽6 and PSA >6. GUROC intermediate-risk patients can be subclassified into low-intermediate and high-intermediate groups. GUROC high-intermediate-risk is defined as existing GUROC intermediate-risk with PSA >=10 AND either T2b/c disease or T1T2a disease with Gleason 7. GUROC high-risk patients would be subclassified into an additional extreme-risk group (GUROC high-risk AND (positive cores ⩾87.5% OR PSA >30). CONCLUSIONS: Proposed RS subcategories have been identified by a RPA of the ProCaRS database.