External Development and Validation of PREDICT Prostate Multivariable Model

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Abstract

Methods and results

Using the records of the National Cancer Registration and Analysis Service (NCRAS) of the United Kingdom, 10 089 men were diagnosed with non-metastatic PCa between 2000 and 2010 in the United States. East of England have been gathered. The median follow-up was 9.8 years with 3,829 deaths (1,202 specific CPa). A total of 19.8%, 14.1%, 34.6% and 31.5% of men underwent conservative management, prostatectomy, radiotherapy (RT) and androgen deprivation monotherapy, respectively. . A total of 2,546 men diagnosed in Singapore over a similar period represented an external validation cohort. The data were randomly distributed at 70:30 in cohorts of model development and validation. PCSM and non-PCA (NPCM) mortality over 15 years was explored using separate multivariate Cox models in a competitive risk setting. Fractional polynomials were used to adapt to continuous variables and basic hazards. Model accuracy was assessed by discrimination and calibration using the quality of the Harrell C-index fit and the chi-square, respectively, within the two validation cohorts. A multivariate model estimating individualized survival outcomes at 10 and 15 years was constructed by combining age, prostate specific antigen (PSA), histological grade, central involvement at biopsy, stage and primary treatment, which were each of the independent prognostic factors of the PCSM, and age. and comorbidity, which were prognostic for NPCM. The model demonstrated good discrimination, with a C index of 0.84 (95% CI: 0.82-0.86) and 0.84 (95% CI: 0.80-0.87) for the PCSM at 15 years in the British and Singaporean validation cohorts, respectively, which compares favorably with international criteria for risk stratification. Discrimination was maintained for overall mortality, with a C score of 0.77 (95% CI: 0.75 to 0.78) and 0.76 (95% CI: 0.73 to 0.78). ). The model was well calibrated, with no significant difference between PCa's specific and predictable values ​​(p = 0.19) or the total number of deaths (p = 0.43) in the British cohort. The main limitations of the study were a relatively small external validation cohort, an inability to account for late changes in treatment beyond 12 months, and an absence of subclassifications at the tumor stage.

Author's abstract

Quote: Thurtle DR, Greenberg DC, LS Lee, Huang HH, Pharoah PD, Gnanapragasam VJ (2019) Individual prognosis at diagnosis in non-metastatic prostate cancer: development and external validation of PREDICT Prostate multivariate model. PLoS Med 16 (3):
e1002758.

https://doi.org/10.1371/journal.pmed.1002758

Academic Publisher: Ricky W. Johnstone, Peter MacCallum Center for Cancer Control, AUSTRALIA

Received: August 29, 2018; Accepted: February 4, 2019; published: March 12, 2019

Copyright: © 2019 Thurtle et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which allows unrestricted use, distribution, and reproduction on any medium, provided that the author and source are acknowledged. origin.

Availability of data: After review, this data was made available to the authors through Public Health England's Office for Data Release (ODR). The authors' contract with Public Health England prohibits open data sharing. Access to this data can be requested via the ODR. More information is available here: https://www.gov.uk/government/publications/accessing-public-health-england-data/about-the-phe-od–odr-and-accessing-data. The source code for this work has been provided for open access through an online repository: https://github.com/dt433/Predict-Prostate/releases/tag/1.0. The final web tool is also available at www.prostate.predict.nhs.uk.

Funding: This work was funded by the Foundation Research Fellowship (DRT), www.theurologyfoundation.org, and by the Evelyn Trust (REF 16/16) Cambridge (DRT, PDP and VJG), http : //evelyntrust.com. The UK Cancer Research Center in Cambridge provided support for infrastructure. Donors have not played any role in the design of the study, the collection and analysis of data, the decision to publish or the preparation of the manuscript. The researchers are independent of the sponsors.

Competing interests: The authors stated that there was no conflict of interest.

Abbreviations:
AIC
Akaike Information Criteria; AJCC,
US Joint Committee on Cancer; AAU,
American Urological Association; AUC
area under the curve; BIC
Bayesian criterion of information; CAPRA,
Prostate Cancer Risk Assessment; WATER,
European Association of Urology; FP
fractional polynomial; GOF,
quality of fit; HE IS,
statistics of hospital episodes; HOUR,
Dangerousness rate; NCCN,
National Network against Cancer; NCRAS,
National Cancer Registry and Analysis Service; NPCM,
non-prostate cancer mortality; PCa,
Prostate cancer; MSCP,
PCa mortality; piNPCM,
prognostic index for NPCM; piPCSM,
prognostic index for PCSM; PIVOT,
intervention on prostate cancer versus trial observation; PPC,
percentage of positive nuclei; Protect,
prostate tests for cancer and treatment; PSA
prostate specific antigen; RP,
Radical prostatectomy; RT
radiotherapy; TRIPOD,
transparent account of a multivariable predictive model for individual prognosis or diagnosis; Stage T,
clinical tumor stage

introduction

Prostate Cancer (PCA) is the most common cancer in men and a leading cause of cancer-related morbidity[[[[1]. The vast majority of new presentations focus on localized or locally advanced diseases, which represents a considerable economic and health burden. [2]. Treatment decisions are notoriously complex, with the risk of cancer-related mortality being weighed against the potential morbidity associated with treatment, as well as competing mortality risks. Prognosis estimation in these settings is therefore extremely important, with more than 40,000 consultations for a newly diagnosed PCa every year in the UK alone. [2]. This evidence was underscored by evidence from randomized trials reporting the non-inferiority of conservative management versus radical therapy in many early cancers of the US cancer study. the prostate compared to the observation test (PIVOT) and the UK on the screening and treatment of prostate cancer (ProtecT). study [3,4].

Despite this importance, there is no single high quality prognostic model for clinical counseling and decision making. Instead, multi-level stratification systems are used that categorize men into different levels of risk. These models are widely endorsed by national and international reference groups, but are often derived from inadequate substitution criteria, such as the resurgence of prostate specific antigen (PSA) after treatment, rather than being calibrated according to the mortality[[[[5,6]. Modern extensions of these models have now sought to validate performance against cancer mortality and have extended the number of sub-classifications [7–10]. Although these extensions add granularity, they remain too heterogeneous for modern individualized medicine approaches. More recent attempts to develop survival models have focused only on men undergoing radical treatment and have not been validated appropriately. [11,12]. The shortcomings of existing models are manifested by the fact that the Joint Committee on Cancer (AJCC) has not approved a single prognostic model for non-metastatic breast cancer. [13].

The objectives of this study were to develop and validate an individualized prognostic model for non-metastatic prostate cancer. Our goal was to produce a model that could contextualize the relative outcomes in terms of CPP-specific overall survival and survival of a newly diagnosed individual and model the potential benefits of treatment on these outcomes. The design and reports of the study were based on the adoption criteria of the Commission's model and the transparent reports of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD statement), respectively.[[[[14,15].

The methods

This study is reported overall in accordance with the TRIPOD Directive (S1 checklist).

Population under study and definition of variables

Fully anonymized data was extracted from Public Health England after review by the Office for Data Release (ODR1617 / 171). Following the approval of the National Health Service Foundation Trust of Cambridge University Hospitals, it acted as the host institution for receiving the data. Information on all men in whom non-metastatic breast cancer was diagnosed in secondary care from the east of England, UK, between 2000 and 2010, was collected prospectively. by the Eastern Region of the National Cancer Registry and Analysis Service (NCRAS). The cohort derivation was previously described[[[[16]. Men with nodal or metastatic disease recorded at the time of diagnosis were excluded, as were men diagnosed only by endoscopic resection and all remaining men with PSA ≥ 100ng / mL, replacing occult metastatic disease. [17]. Only men with intact information on key predictor candidates – age, PSA (ng / mL), histological grade group, clinical tumor stage (T stage), and primary treatment were included. Of a potential cohort of 15,335 men, 5,246 (34.2%) were excluded from missing information in at least one of these variables, leaving a final analytic cohort of 10,089. statistics on hospital episodes (HES) were also included. These are based on the clinical coding of known episodes of hospitalization in the period between 27 and 3 months prior to the diagnosis of PCa, thus excluding the PCa from any comorbidity score. The vital state was established at the end of March 2017 and all analyzes were censored at the end of September 2016 to allow up to 6 months for non-fatal deaths. linked to cancer through the strategic research service of the national health service. The death was considered specific to the PCa when it was included in 1a, 1b or 1c of the death certificate.

Potential variables entered into the main model were age, PSA, T stage, histologic grade, ethnicity, comorbidity and type of primary treatment. NCRAS information was that recorded at the time of diagnosis. Stage T has been simplified to T1, T2, T3 or T4, with subcategories rarely available and having limited impact on prognostic determination[[[[18]. Groups of histological grades (1-5) were used [19]. PSA (ng / mL) refers to the value at the time of diagnosis, before biopsy or treatment. Primary treatment is the first definitive treatment that the patient received in the first 12 months. Here, we used the term conservative management to cover active surveillance and watchful waiting, with the registry data making no distinction between the two during this period. As previously published, the majority of men receiving radiation therapy during this period followed concomitant hormone therapy, which is consistent with current best treatment practices. [20].

Development of a model

The primary cohort (United Kingdom) was randomly split in a ratio of 70:30 in model development (not = 7,062) and validation cohorts (not = 3.027) (Table 1). Within the development cohort, separate models were constructed for PCa mortality (PCSM) and non-PCa mortality (NPCM). The general modeling approach was similar to that used for the prognostic and benefit model of PREDICT breast cancer treatment [21]. Proportional hazard Cox models were used to estimate the risk ratios associated with each candidate predictor. The length of follow-up was censored at the time of death, at the last follow-up, or at age 15, whichever came first. Each variable was evaluated using one and multivariate analyzes, taking into account the proportional hazards hypothesis. A backward elimination technique was used for the selection of variables with a significance level of 5%. Risk relationships between continuous variables were modeled using multivariate fractional polynomials (FP), with continuous data kept as much as possible to maximize predictive information. T stage, histological grade group, and main treatment type were modeled as factor variables. Radical treatments (RT or radical prostatectomy) [RP]) were combined, as explained later. After adjusting the multivariate models, the smoothed functions for the baseline risk of PCSM and NPCM were calculated. The cumulative baseline risk was estimated for each patient, and then the logarithmic baseline risk was regressed over time with the aid of a univariate PF function. [21].

Model accuracy and comparison with existing models

Model calibration and quality of adjustment were studied in the UK validation cohort by comparing observed and expected deaths in the expected mortality quintiles and in the strata of other prognostic variables. To evaluate the calibration, we integrated the expected results for all follow-up periods to allow for cases with follow-up of less than 10 or 15 years. Thus, the calibration corresponds to a range of different tracking times. A simplified2 The fit quality test (GOF) was performed using the method of May and Hosmer. p-a value less than 0.05 would indicate a significant difference between the number of anticipated and observed events, evaluated up to 10 years or 15 years[[[[22]. Calibration curves were also evaluated visually. Discrimination of the model was evaluated by estimating the cumulative mortality risk over 10 and 15 years. The Harrell concordance statistic (C-index) was then calculated for PCa, non-PCa and global specific deaths. This represents censored data on the right, that is, cases with a follow-up of less than 10 or 15 years, respectively. All analyzes were performed using Stata 14 (StataCorp, College Station, TX), with the exception of the C index which was achieved using "rcorr.cens" in the "Hmisc" package »From R [23].

Comparisons with existing models were performed by calculating the C indices of three well-known tools used at the time of diagnosis at the international level, namely the CAPRA (Cancer of the Prostate Risk Evaluation) assessment score. from the University of California at San Francisco and the National Comprehensive Cancer Update. Network Criteria (NCCN) and European Three-Level Association (UAE) Criteria [7,10,24]. The available information was used to calculate them, without imputing the missing data. When subclassification of T stages was unknown, T 2 and 3 were assumed to be T2a and T3a, respectively.

Results

Model development and specification

Age, PSA, histological grade group, clinical stage T, and type of primary treatment were all independent predictors of PCSM in the developing cohort (Table 2). Comorbidity has a predictive effect on NPCM but not on PCSM. Age was also prognostically independent for NPCM. In the final model, comorbidity was modeled as a binary variable (0 or ≥1). The HR and PF functions for the prognostic factors in the final model are presented in Table 2. The associated PF functions for age and PSA are shown in Figure 1. They allow more flexibility in relationships for continuous variables. The estimated basic survival functions for PCSM and NPCM are recorded in Appendix S1 and plotted against the actual PCSM and NPCM baseline values ​​shown in Figure E of Appendix S1.

thumbnail "class =" thumbnail "/></div> <p><span>Fig. 1. </span> PCSM HR works for age (left) and PSA (center), and NPCM HR for age (right).</p> <p>Each derived from the model development data. HR, risk ratio; NPCM, non-PCa mortality; PCa, prostate cancer; PCSM, PCa specific mortality; PSA, prostate specific antigen.</p> <p class=

https://doi.org/10.1371/journal.pmed.1002758.g001

British validation

The model was well calibrated in the Eastern England validation cohort, with absolute differences between deaths observed and predicted by PCa and overall deaths less than 1% at 10 years (Table 3). GOF tests showed that the model relied well on different risk quintiles, as shown by the calibration curves (Fig. 2), with no significant difference between observed and predicted BCPs (p = 0.19) or the total number of deaths (p = 0.43) over 10 years (Table 3). Discrimination of the model was good, especially for the PCSM, with a C index of 0.84 (95% CI: 0.82-0.86) and 0.84 (95% CI: 0.82-0, 86) on a follow-up of 10 and 15 years, respectively (Table 3). In the British cohort, discrimination by the model was higher (p <0.001) to the current EUS, NCCN and CAPRA risk stratification criteria for PCSM and overall mortality (Table 4).

thumbnail "class =" thumbnail "/></div> <p><span>Fig 2 </span> Calibration curves comparing the observed and expected probability of PCa (left), non-PCA (center) and global (right) deaths at age 10, by risk quintile, within the Kingdom validation cohort -United.</p> <p>PCa, prostate cancer.</p> <p class=

https://doi.org/10.1371/journal.pmed.1002758.g002

The calibration remained good in various subcategories of patients, as shown in Table C of Annex S1. It is important to note that predictions of prostate and non-prostate cancer deaths in men undergoing conservative management or radical therapy were less than 2%. GOF tests among this sub-treatment cohort continued to show no significant difference between predicted and observed BCPs (p = 0.23) or the total number of deaths (p = 0.11) over 10 years.

Extension du modèle et nouvel essai avec inclusion d&#39;informations de biopsie diagnostique

Après avoir évalué plusieurs catégorisations de PPC, PPC a été intégré au modèle à l’aide d’une variable dichotomique autour d’un seuil de 50% (tableaux E et F de l’annexe S1). Un CPP <50% ou ≥ 50% était associé à des valeurs ajustées de HR pour la PCSM de 0,54 et 1,78, respectivement. Un HR de 1,0 est appliqué si PPC est inconnu ou si la variable PPC est omise (tableau G de l&#39;annexe S1).

La précision du modèle étendu final incorporant le PPC a été réévaluée à l&#39;aide de la cohorte singapourienne (not = 2 546). La discrimination du modèle a été légèrement améliorée par rapport au modèle de base, avec un indice C de 0,85 (IC à 95%: 0,82–0,88) et de 0,76 (IC à 95%: 0,73–0,79) pour la PCSM et la mortalité globale, respectivement (tableau H dans S1, annexe). ). L&#39;étalonnage a également été amélioré avec l&#39;incorporation de la variable PPC (figure K de l&#39;annexe S1). L’analyse par GOF n’a montré aucune différence significative entre les décès liés au PCa observés et prévus (p = 0,11), bien que le modèle semble encore sous-estimer légèrement PCSM. L&#39;étalonnage au sein de sous-groupes (tableau J de l&#39;annexe S1) a suggéré que le modèle PCSM était sous-estimé dans le contexte de caractéristiques présentant un risque très élevé: groupe de qualité 5 (prévu: 30,6; observé: 36), stade T 4 (prévu: 4,1; observé: 8). ), et PSA> 50 ng / mL (prévu: 21; observé: 25).

Ensuite, nous avons comparé la précision de notre modèle étendu avec les modèles de PCa existants dans cette cohorte externe. Le modèle a continué de surperformer les modèles existants pour prédire la mortalité globale (p <0,001) (Tableau 6). Pour PCSM, des indices C améliorés ont été observés pour PCSM par rapport aux modèles existants, mais là encore, ils n’ont atteint qu’une importance significative par rapport aux critères EAU. Enfin, nous avons limité la cohorte aux seuls hommes ayant reçu une gestion conservatrice ou un traitement radical, afin de modéliser la pratique contemporaine, dans laquelle l&#39;hormonothérapie primaire est moins utilisée [20]. De nouveau, le modèle a généralement montré une discrimination supérieure par rapport aux autres modèles (tableau K de l’annexe S1).

Discussion

Dans cette étude, à notre connaissance, nous présentons le premier modèle de pronostic multivariable individualisé pour le cancer de la prostate non métastatique construit et validé dans une cohorte de prétraitement non dépistée. Nous montrons que ce modèle, ci-après appelé PREDICT Prostate, est capable de dériver des prédictions de CPa et de mortalité globale avec un degré élevé de concordance en utilisant des données clinico-diagnostiques de diagnostic disponibles de manière routinière, et semble surperformer les modèles existants. Le modèle intègre l’impact de la thérapie radicale, ce qui permet de comparer la possibilité d’une gestion conservatrice dans le contexte des risques concurrents d’un individu. Il est important de noter que le modèle ne nécessite aucun test ni aucune information supplémentaire, mais pourrait être affiné à l&#39;avenir si des facteurs indépendants supplémentaires présentant une valeur pronostique prouvée sont établis.

L’incidence du cancer de la prostate augmente avec le vieillissement de la population masculine et le nombre accru de tests. Au Royaume-Uni seulement, l&#39;incidence devrait augmenter de 69% d&#39;ici 2030[[[[26]. Plus de 84% des hommes britanniques ont une maladie non métastatique au moment de la présentation, plus de la moitié d&#39;entre eux étant classés comme présentant un risque faible ou moyen en fonction des critères de risque traditionnels. [2]. Les preuves de niveau 1 montrent que de nombreux hommes présentant ces caractéristiques de maladie ne bénéficieront pas d&#39;une thérapie radicale immédiate, les essais randomisés ProtecT et PIVOT ne signalant aucune différence de survie chez les hommes traités par intervention ou par un traitement conservateur après 10 ans de suivi. [3,4]. En outre, un traitement radical est associé à des risques d&#39;effets indésirables importants, notamment incontinence, impuissance, dysfonction intestinale et regret décisionnel à long terme. [27,28]. Unsurprisingly, conservative management or active surveillance is therefore becoming increasingly popular in low-risk disease, and emerging evidence also suggests very favourable outcomes in intermediate-risk disease [29].

Identifying men appropriate for initial conservative management and conveying this information to an individual within their own context of competing mortality is currently an imprecise exercise, with a lack of objective data on potential outcomes. Instead, most current prognostication is directed by categorisation of men into risk-stratified criteria and discussions with clinicians who may or may not be PCa specialists and are potentially conflicted by a bias to a treatment they offer[[[[8–10,30]. PREDICT Prostate was conceived to address this critical gap in clinical need and to better inform and standardise the decision-making process. It is built around long-term actual survival data and has been designed to address all AJCC criteria [14]. The model incorporates variables available for almost any man diagnosed around the world and has wide potential applications in informing patient, clinician, and multidisciplinary team decision-making to reduce both over- and undertreatment [31]. Abundant literature shows that better decision aids contribute to more knowledgeable, informed patients and that this improves clinician-patient communication [32,33]. Therefore, we anticipate our model will be widely acceptable and highly impactful, although formal clinical impact assessments will also be undertaken [34].

The parameters used within PREDICT Prostate for PCSM are well-established independent variables such as grade group, PSA, and T-Stage[[[[35–37]. Here, they have been combined in a novel way and by utilising FPs to maintain as much predictive information as possible. PREDICT Prostate is also distinctive in estimating the competing risks of PCSM and NPCM to accurately model overall mortality. The model deliberately uses histological grade groups (1–5) as we standardise practice towards this more intuitive scale [19]. Biopsy information was integrated as an optional variable in PREDICT Prostate, as biopsy quantification is accepted as a surrogate for tumour volume. However, no consensus on the best methodology for its assessment yet exists, with few studies exploring its relationship with long-term survival [38]. Hence, we used a pragmatic assessment of this by using the simplest common denominator, the number of positive versus overall biopsy cores taken (PPC). Our data showed an independent prognostic impact around the dichotomous cutoff of <50% versus ≥50% PPC. This is the same cutoff reported in two American studies exploring survival, for which effect size is comparable. This cutoff has now also been integrated into the latest NCCN risk criteria [10,39,40]. PPC thus maintains simplicity and facilitates ease of interpretation (although the model can function without biopsy information). During the study period, local practice was to perform 12-core systematic transrectal biopsy. However, contemporary practice in prostate biopsy is evolving with the use of more image targeting [41]. It is unknown how these changes will alter the prognostic value of biopsy involvement. In the meantime, we recommend adherence to the American Urological Association (AUA) guidelines, which suggest any biopsies from a target are considered as a single core if taken as part of a ‘target and systematic’ biopsy approach [9].

A key question whilst developing PREDICT Prostate was whether to use data-derived coefficients for treatment effect or published trial data. Ultimately, the data-derived coefficient for the combination of radical treatment types was used, with a HR of 0.50 (95% CI: 0.38–0.67). This is in fact very similar to published randomised controlled trial data of treatment effect, e.g., PIVOT (RP versus AS: HR, 0.63; 95% CI: 0.36–1.09) and ProtecT trials (RT versus active monitoring: HR, 0.51; 95% CI: 0.15–1.69. RP versus active monitoring: HR, 0.63 95% CI: 0.21–1.93) [3,4]. In the web-based presentation of the model, uncertainty around treatment effect is demonstrated by displaying treatment benefit from 0%–100% of PCSM around the estimated survival (Fig 3). Separate presentation of RT and RP outcomes was not explored, as no adequate randomised data yet shows a survival difference between the two treatment approaches [4,42]. One caveat in the clinical utility of PREDICT Prostate is that primary androgen deprivation, used in a proportion of our study cohorts, is now seldom used as a first-line therapy. Indeed, within this cohort, the poor prognosis apparently associated with primary androgen deprivation is likely to reflect a selection bias towards men unfit for other treatment options, or with potentially occult metastatic disease. Our model, however, is primarily for use among men deciding between conservative management and radical treatment—where decision dilemmas are most acute. Indeed, as shown in Table C in S1 Appendix, calibration of the model was best amongst men with low- to intermediate-risk features, for whom this model would be most useful and appropriate in clinical decision-making. Using disease status information from the National Prostate Cancer Audit, this may represent up to 47% of all newly diagnosed PCa [2].

Particular strengths of PREDICT Prostate include the derivation from a large cohort from a geographical area straddling two academic centres and nine general hospitals. These data were collected prospectively by an independent cancer registry with accurate death certificate notification, avoiding many potential biases associated with single-centre studies. The accuracy of UK PCa cause-of-death reporting is also known to be very reliable[[[[43]. However, we do acknowledge limitations in the model. We do not have data on MRI-defined lesions or radiological stage. However, it is yet unknown if these data will improve prognostic ability, with MRI primarily used to guide biopsies rather than offer prognostic information. Indeed, the additional value of MRI in detecting missed cancers is debatable given that men with a missed cancer using non-imaging approaches have extremely low rates of PCa death [44]. The model also does not currently integrate genomic tests or molecular markers. However, the most established tools such as Prolaris CCP and Oncotype DX GPS have predominantly been tested against shorter-term outcomes in very selected groups, particularly in the posttreatment setting [45,46]. When these expensive tools have been assessed against PCSM, concordance is very similar to our model. For example, the Decipher genomic classifier alongside CAPRA showed an area under the curve (AUC) of 0.78 (95% CI: 0.68–0.87) for 10-year PCSM following prostatectomy [47]. We agree with others that good data should be sought as to whether any such marker truly adds independent prognostic information beyond a gold-standard multivariable model [48]. As with MRI, if one or more marker does show independent prognostic value in the future, it can be included in future refinements to PREDICT Prostate [49]. By using real world data, our treatment categories were based upon actual treatments received as opposed to assigned treatments, as is often problematic in randomised trials [4]. However, our analysis cannot account for the impact of delayed conversions to treatment beyond 1 year, albeit the number of men switching from conservative management was very small (5.7%). A final potential limitation of the model is the lack of T-stage subclassifications. However, it is accepted that T-stage is often inaccurately assigned in localised disease [18].

In terms of statistical approach, we recognise that more complex flexible parametric survival modelling frameworks exist. For example, there are several penalised regression approaches such as least absolute shrinkage and selection operator (LASSO) regression, ridge-regression, and random forests, which could have been applied. However, we have used an established methodology, which in other tumour types could not be improved upon by more complex approaches[[[[50]. Our approach also has the advantages of allowing straightforward external validation and the incorporation of additional parameters should sufficient evidence support their inclusion, as demonstrated by updates to the PREDICT breast cancer model [51]. We also appreciate that our external validation cohort was relatively small, and different from our model-development dataset. Gaining access to well-annotated cohorts with long-term follow-up outcomes is difficult; this dataset represented the best independent cohort available to us. Applying the model in this cohort of differing case mix and ethnicity was considered a good test of the generalisability of the tool. The similar discriminatory performance herein may suggest that ethnicity is not a key determinant of prognosis. However, we recognise that follow-up duration in the Singaporean cohort is short, and the model remains untested among many other healthcare, geographic, and ethnic contexts. Finally, our comparisons to the EAU, NCCN, and CAPRA stratification criteria are pragmatic but potentially unfair. These models are intended to delineate patients into groups of risk, rather than offering predictions of 10- or 15-year risk. However, these are widely used clinical models such that these comparisons may be of interest to PCa specialists, particularly in the absence of equivalent models to compare against.

In conclusion, we have developed an individualised prognostication and decision-making tool for use at the point of PCa diagnosis. For the first time to our knowledge, this simultaneously presents individualised estimates of cancer-specific and overall survival outcomes and can model the impact of treatment on these outcomes. The accuracy of the model is promising across populations, and provides encouraging levels of discrimination in two validation cohorts. This model underpins a new web tool and decision aid to inform the decision-making process for patients and clinicians available at www.prostate.predict.nhs.uk. Further external validation of the model should be established to explore accuracy and generalisability across other contexts—particularly testing validity amongst non-Caucasians and those detected through screening.

Acknowledgments

Data for this study are based on information collected and quality assured by the PHE National Cancer Registration and Analysis Service. Access to the data was facilitated by the PHE Office for Data Release.

We thank our colleagues from The Winton Centre for Risk and Evidence Communication, Cambridge, particularly David Spiegelhalter, Alex Freeman, and Mike Pearson, who have provided invaluable insight into this project and important web-development and design expertise.

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