Open Access

An appraisal of analytical tools used in predicting clinical outcomes following radiation therapy treatment of men with prostate cancer: a systematic review

  • Elspeth Raymond1,
  • Michael E. O’Callaghan1, 2, 7, 8Email author,
  • Jared Campbell9,
  • Andrew D. Vincent1, 2,
  • Kerri Beckmann1, 3,
  • David Roder3,
  • Sue Evans4,
  • John McNeil4,
  • Jeremy Millar5,
  • John Zalcberg4, 11,
  • Martin Borg6 and
  • Kim Moretti1, 2, 3, 9, 10
Radiation Oncology201712:56

DOI: 10.1186/s13014-017-0786-z

Received: 1 August 2016

Accepted: 22 February 2017

Published: 21 March 2017

Abstract

Background

Prostate cancer can be treated with several different modalities, including radiation treatment. Various prognostic tools have been developed to aid decision making by providing estimates of the probability of different outcomes. Such tools have been demonstrated to have better prognostic accuracy than clinical judgment alone.

Methods

A systematic review was undertaken to identify papers relating to the prediction of clinical outcomes (biochemical failure, metastasis, survival) in patients with prostate cancer who received radiation treatment, with the particular aim of identifying whether published tools are adequately developed, validated, and provide accurate predictions. PubMed and EMBASE were searched from July 2007. Title and abstract screening, full text review, and critical appraisal were conducted by two reviewers. A review protocol was published in advance of commencing literature searches.

Results

The search strategy resulted in 165 potential articles, of which 72 were selected for full text review and 47 ultimately included. These papers described 66 models which were newly developed and 31 which were external validations of already published predictive tools. The included studies represented a total of 60,457 patients, recruited between 1984 and 2009. Sixty five percent of models were not externally validated, 57% did not report accuracy and 31% included variables which are not readily accessible in existing datasets. Most models (72, 74%) related to external beam radiation therapy with the remainder relating to brachytherapy (alone or in combination with external beam radiation therapy).

Conclusions

A large number of prognostic models (97) have been described in the recent literature, representing a rapid increase since previous reviews (17 papers, 1966–2007). Most models described were not validated and a third utilised variables which are not readily accessible in existing data collections. Where validation had occurred, it was often limited to data taken from single institutes in the US. While validated and accurate models are available to predict prostate cancer specific mortality following external beam radiation therapy, there is a scarcity of such tools relating to brachytherapy. This review provides an accessible catalogue of predictive tools for current use and which should be prioritised for future validation.

Keywords

Prostate cancer Systematic literature review Nomogram Outcomes Survival Biochemical recurrence

Background

Rationale

Prostate cancer is the most prevalent cancer in men globally, with 1.4 million new cases reported in 2013 [1]. Prostate cancer cases increased by 217% between 1990 and 2013 as a result of population growth and aging and increased uptake of opportunistic screening, particularly in developing countries [1]. Prostate cancer remains the leading cause of death among males in 24 of 188 countries covered by the Global Burden of Disease Cancer Collaboration [1].

Prostate cancer treatments are varied and include: deferred treatment (active surveillance), watchful waiting, radical prostatectomy, radiation therapy (with or without androgen deprivation therapy) or androgen deprivation therapy (ADT) [2, 3]. Each treatment will achieve different outcomes in terms of oncology (e.g., survival or time to biochemical recurrence), adverse events and patient reported outcomes such as urinary incontinence and impotence. These outcomes are important considerations when selecting a treatment for prostate cancer patients and are considered in the context of patient age, life expectancy, co-morbidities, tumour size, grade and stage and other risk indicators that influence outcomes and treatment choice. Determining which treatment choice is optimal for each patient remains an important challenge, particularly where directly relevant randomised controlled data is lacking.

To aid this decision making process, a number of tools have been developed with nomograms and risk stratification systems most commonly used [4]. Nomograms are graphic tools developed to aid clinical decision making and are well established in clinical practice for prostate cancer, particularly for assisting selection of treatment approaches based on risk stratification. Such tools have been shown to improve prediction of outcomes when compared with clinician judgement alone [5, 6]. Unfortunately most nomograms currently in use are likely to be based on dated treatment modalities. Furthermore predictions based on observations made in one setting may not be accurate in another (e.g., where ethnicity or health services differ). Extrapolation of published international results to local practice is a known pitfall that has potential to mislead both clinicians and patients [7]. These limitations are particularly relevant to predictive tools designed for use in patients treated with radiation therapy as this modality has changed significantly over the past decade.

Objectives

We aim to identify papers predicting clinical outcomes for patients with prostate cancer who have been treated with radiation therapy. We particularly set out to assess if the tools identified were adequately developed, validated and provide accurate predictions.

Methods

Protocol and registration

A systematic literature review protocol was developed for this study and registered before searches commenced with PROSPERO, an international prospective register of systematic reviews. The protocol can be accessed at: http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42015025428.

Inclusion criteria

Papers were eligible for inclusion where they met the following criteria; Population: Patients with prostate cancer. Exposure: Treatment with radiation therapy (including external beam radiation therapy and/or brachytherapy). Outcome: The generation or validation of a tool for the prediction of clinical outcomes (biochemical failure [BF], progression to metastases, prostate cancer specific survival, overall survival). Papers had to be written in English and published post July 2007. This date was chosen as it is the search date up to which a previous systematic review of prognostic tools for prostate cancer treated by any therapy was undertaken [4]. Studies were included which described tools using variables which are currently available in a clinical setting. This excluded papers including genetic or molecular variables.

Information sources

Searches were conducted of the Medline database (PubMed interface) and the EMBASE database.

Search

Disease-specific search terms included: prostate cancer, prostatic neoplasms, cancer of the prostate, adenocarcinoma of the prostate, prostatic cancer, prostate gland cancer and prostate tumour. Treatment specific search terms included: radiation therapy, radiotherapy, external beam radiotherapy, EBRT, brachytherapy, high dose radiotherapy, low dose radiotherapy and targeted radiotherapies. Outcome-specific search terms included: overall survival, progression-free survival, PFS, mortality, event free survival, EFS, disease free survival, prostate cancer specific survival, progression to metastases, time to progression, TTP, biochemical recurrence, BCR, biochemical failure, neoplasm recurrence. Search terms used to identify predictive models included: predictive tools, nomograms, risk stratification, Partin tables, regression tree analysis, Artificial Neural Networks, CAPRA-S or CAPRA score, risk estimates, algorithms, predictive accuracy, diagnostic test accuracy, Kattan tables/nomograms.

Study selection

Study selection included three phases. The titles and abstracts of all studies identified by the search strategy were compared to the inclusion criteria detailed above by two authors working independently (ER and MOC). All studies that appeared likely to meet the inclusion criteria were progressed to full-text review. All discrepancies, where authors reached different conclusions about the same papers, were resolved through discussion. The full-texts of these papers were then retrieved and assessed against the inclusion criteria, again by two authors (ER, JC or MOC) working independently in order to minimise the impact of human error. Studies that were identified as meeting all inclusion criteria were included in the review, while those which did not were excluded. Again, where there were differences in the authors’ conclusions consensus on the correct decision was reached through discussion. Finally, the reference lists of included papers were screened for any additional relevant papers which may have been missed by the search strategy. All new titles identified were then reviewed as described above.

Data collection process and data items

After full text review, data extraction was undertaken by one reviewer (ER, JC or MOC). Items for extraction included: manuscript identifiers (author, contact, country, setting), study methods, population studied (inclusion criteria, exclusion criteria, baseline characteristics – dates of recruitment, age, ethnicity, number of patients, primary treatment, treatment subtype, adjuvant therapies, neoadjuvant therapies), and predictive model characteristics (type of model, variables included, if internal validation was reported and the type, external validation, variable definitions, if variables were readily available, sample size, number of events, definition of outcome, model accuracy, sensitivity, specificity, concordance index and receiver operator curve area under the curve). For assessment as to whether or not variables were considered ‘readily available’ the minimum data set used by the only national prostate cancer registry (Prostate Cancer Outcomes Registry, Australia and New Zealand Australian [8]) was used as a guide.

Quality assessment

Quality assessment was performed by two reviewers (ER, JC or MOC) for each paper. Four questions were selected for this assessment: 1. Was the defined representative sample of patients assembled at a common (usually early) point in the course of their disease? 2. Was patient follow-up sufficiently long and complete? 3. Were outcome criteria either objective or applied in a ‘blind’ fashion? And 4. If subgroups with different prognoses were identified, did adjustment for important prognostic factors take place? These questions were selected from the Centre for Evidence Based Medicine ‘Critical appraisal of prognostic studies’ tool [9]. Discrepancies between reviewers were discussed and consensus reached. Questions that were answered positively >75% of the time were considered to present a low risk of bias, those ≤75 to >50% a moderate risk of bias, and any ≤50% a high risk of bias. Data extraction and quality assessment were performed using the online tool ‘Covidence’.

Results

The search strategy resulted in 165 potentially relevant abstracts/articles and these were reduced to 72 once duplicates were removed and title and abstracts were screened (Fig. 1). The full-text of these papers was reviewed against the inclusion criteria (reasons for exclusion are reported in Additional file 1: Table S1a and b) and 47 finally selected. Study recruitment periods varied considerably with the earliest patients being from 1984 [10] and the latest 2009 [1013] (Table 1). The populations of individual studies varied from 80 [14] to 7,839 [14, 15] with a combined population of 60,457 (Tables 2, 3 and 4). The majority of studies were retrospective (n = 38), however seven studies recruited prospective cohorts (for one study [16] it was not stated whether it was retrospective or prospective).
Fig. 1

Flow diagram

Table 1

Summary of papers describing prognostic tools relating to clinical outcomes following radiation therapy (2007–2015)

Author

Recruitment window

Country

Population

Outcome

Study type

Setting

Bittner [27]

1995–2006

USA

Prostate cancer patients treated with brachytherapy

BFFF, PCSM

Retrospective

Single centre

Buyyounouski [38]

1989–2000

Canada, Aust, USA

Men previously treated with EBRT for clinically localized prostate adenocarcinoma and subsequently diagnosed with BCF.

PCSM

Retrospective

Multi-centre

Cooperberg [39]

1995–2007

USA

Men enrolled in CaPSURE

PCSM

Retrospective

Multi-centre (CaPSURE Registry)

Cooperberg [40]

1995–2008

USA

Men with localized disease who underwent prostatectomy, received external-beam radiation, or received primary androgen deprivation; and had at least 6 months of follow-up recorded.

10 year PCSM

Retrospective

Multi-centre (CaPSURE Registry)

D’Ambrosio [41]

1989–2004

USA

Men with prostate cancer treated with RT.

BCF

Retrospective

Single centre

D’Amico [42]

1991–2005

USA

Men with high-risk prostate cancer (locally or advanced) and 10 year life expectancy treated with brachytherapy who were observed for a min of 2 years.

PCSM and presence of hormone-refractory metastatic prostate cancer.

Prospective

Multi-centre

D’Amico [43]

1988–2004

USA

Men who underwent RT for prostate cancer for at least 1 high-risk feature.

PCSM

Prospective

Multi-centre

Delouya [19]

2002-Not stated

Canada

Men with low or intermediate-risk prostate cancer treated with brachytherapy, EBRT within a phase II or III research protocol, or ERBT outside of a protocol.

BCF

Retrospective

Single centre

Denham [44]

1996–2000

Australia & New Zealand

Men with locally advanced prostate cancer receiving RT

PCSM

Prospective

Multi-centre

Engineer [9]

1984–2004

India

Patients with a histological diagnosis of prostate cancer

BFFF, PCSM, DM, BCF, OS

Retrospective

Single centre

Feng [28]

1998–2008

USA

Men with clinically localized prostate cancer treated with EBRT.

FFM, PCSM, BFFF, OS

Retrospective

Single centre

Frank [45]

1996–2006

USA, Canada, Netherlands.

Men with prostate cancer treated with brachytherapy with at least 30 months of follow-up.

PSA failure.

Retrospective

Multi -centre

Frank [25]

1998–2006

USA

Men with prostate cancer treated with permament 125 I brachytherapy.

5 year BFFF

Retrospective

Single centre

Halverson [46]

1998–2008

USA

Men with clinically localized prostate cancer treated with EBRT with or without adjuvant ADT

BFFF

Retrospective

Single centre

Huang [47]

1993–2003

USA, Australia

Men with clinical Stage T1c-T3N0M0 prostate adenocarcinoma treated with EBRT with or without a high-dose rate brachytherapy boost.

BCF, DM, PCSM,OS.

Retrospective

Single centre

Kaplan [12]

2000–2009

Israel

Patients with prostate cancer treated with 125 I- brachytherapy.

BFFF

Retrospective

Single centre

Krishnan [20]

2003–2008

Canada

Men with intermediate-risk prostate cancer with a minimum follow-up of 3 years.

BCF

Retrospective

Single centre

Kubicek [48]

1998–2004

USA

Men with biopsy proven T1-T2 prostate adenocarcinoma treated with EBRT & LDR.

CSS

Retrospective

Single centre

Marshall [11]

1990–2009

USA

Men treated with brachytherapy for biopsy-proven prostate adenocarcinoma.

BCF

Retrospective

Single centre

McKenna [49]

1998–2003

USA

Men with biopsy-proved prostate cancer who had MRI imaging prior to EBRT.

Metastatic recurrence and BCF

Retrospective

Single centre

Murgic [50]

1998–2008

USA

Men with clinically localized prostate adenocarcinoma treated with EBRT.

BFFF, FFM,PCSM and OS

Retrospective

Single centre

Potters [16]

Not stated

USA

Prostate cancer patients treated with brachytherapy.

9-year BFFF

Retrospective

Multi-centre

Proust-Lima [51]

Not stated

USA

Men treated for localized prostate cancer with EBRT.

BCF

Prospective

Multi-centre

Qian [52]

1998–2008

USA

Men who were treated with EBRT for clinically localized prostate cancer with or without neoadjuvant or adjuvant ADT.

BFFF, FFM,OS, PCSM.

Retrospective

Single centre

Rodrigues [14]

Not stated

Canada

Men with prostate cancer.

BFFF, OS

Retrospective

Multi-centre (GUROC ProCaRS database)

Sabolch [53]

1998–2008

USA

Men treated for localized prostate cancer with EBRT.

BFFF, FFM,OS, PCSM.

Prospective

Single centre

Sanpaolo [21]

2000–2004

Italy

Men with T1-T3 NO prostate cancer.

BCF

Retrospective

Single centre

Slater [54]

1991–1999

USA

Randomly selected prostate cancer patients treated with proton and photon beam therapy.

bNED

Retrospective

Single centre

Spratt [55]

1997–2008

USA

Men with localized prostate cancer were treated with IMRT.

BCF, DMFS, BCR

Retrospective

Single centre

Steigler [56]

1996–2000

Australia & New Zealand

Men with localised advanced prostate cancer treated with RT and experienced BCF prior to clinical failure or secondary theraputic intervention.

TTBF, PCSM,distant progression and STI from BCF

Retrospective

Multi-centre

Sylvester [57]

1988–1992

USA

Men with clinically localized prostate cancer treated with implanted I-125.

15 year BFFF,CSS and OS.

Prospective

Consecutive case series

Taylor [58]

Not stated

USA

Men with localized prostate cancer,NO/MO treated with RT.

Clinical recurrence (local, regional or distant)

Retrospective

Multi-centre

Thames [59]

1987–1995

USA

Men with clinical stages T1b, T1c, and T2 N0M0 biopsy proven prostate adenocarcinoma.

BCF

Retrospective

Multi-centre

Vainshtein [18]

1998–2008

USA

Men with localized prostate cancer treated with EBRT, +/− ADT

FFM, PCSM.

Prospective

Single centre

Vance [60]

1998–2008

USA

Men with clinically localized prostate cancer treated with EBRT, with or without neoadjuvant or adjuvant ADT.

BFFF, DMFS, PCSM & OS.

Retrospective

Single centre

Wattson [61]

1991–2007

USA

Men with high-risk prostate cancer.

PCSM

Retrospective

Multicentre

Westphalen [62]

1998–2007

USA

Prostate cancer patients who underwent endorectal MR and MR spectroscopy prior to EBRT.

BCF

Retrospective

Multi-centre (national administrative data set)

Williams [17]

1991–2002

US, Canada, Australia

Men with clinical T1–4 N0/X M0/X prostate adenocarcinoma treated with EBRT.

BCF

Retrospective

Multi-centre

Yoshida [15]

2003–2008

Japan

Men with histologically-proven prostate adenocarcinoma, treated with HDR-ISBT.

5 year PSA failure and OS

Retrospective

Single centre

Yu [63]

1987–2001

USA

Men with prostate cancer treated with EBRT.

BCF

Retrospective

Single centre

Yu [64]

1993–2002

USA

Men newly diagnosed with clinically node-negative, localized adenocarcinoma of the prostate treated with EBRT.

BCF

Retrospective

Single centre

Zaorsky [65]

1992–2004

USA

Men with clinical stage T1-4, NO/NX-N1, MO adenocarcinoma of the prostate received RT with or without adjuvant ADT.

BCF,DM, OS.

Retrospective

Single centre

Zelefsky [66]

1988–2004

USA

Men with clinically staged T1-T3 node-negative prostate cancer treated with 3D-CRT or IMRT.

DMFS, BFFF.

Retrospective

Single centre

Zelefsky [67]

1998–2000

USA

Men with clinically localized prostate cancer treated with 3D-CRT or IMRT.

DM,PCSM,BFFF

Retrospective

Single centre

Zelefsky [68]

1988–2004

USA

Men with Stage T1-T3 prostate cancer treated with 3D-CRT or IMRT.

PSA relapse

Retrospective

Single centre

Zelefsky [10]

1998–2009

USA

Men with clinically localised prostate cancer treated with brachytherapy.

BFFF

Retrospective

Single centre

Zumsteg [69]

1992–2007

USA

Men with intermediate-risk prostate cancer, but without high-risk features treated with EBRT.

BCF, BFFF, LF,PCSM, DM.

Retrospective

Single centre

Abbreviations: OS overall survival, CaPSURE Cancer of the Prostate Strategic Urologic Research Endeavour, RT radiotherapy, BCF bio chemical failure, BFFF bio chemical freedom from failure, PCSM prostate cancer specific mortality, PSA-RFS prostate-specific antigen recurrence-free survival, LF local failure, DM distant metastases, DMFS distant metastases-free survival, FFM freedom from metastases, HDR-ISBT high-dose-rate interstitial brachytherapy, TTBF time to bio chemical failure, STI secondary therapeutic intervention, bNED bio chemical no evidence of diseaese, 2D-CRT 2D - Conformal radiotherapy, 3D-CRT 3D -Conformal radiotherapy, EBRT external beam radiotherapy, LDR brachytherapy low dose rate brachytherapy, NO/NX no nodal involvement, I-125 Iodine 125 brachytherapy

Table 2

Prognostic tools relating to brachytherapy

Author

Model type

Variables

Variable readily available?

Validation (I/E)

Accuracy

Metric

Sample size (events)

Outcome

Treatment

Frank [25]

Survival (Nomogram presented)

Biopsy gleason score, clinical stage, EBRT, pre-treatment PSA,

Yes

External validation of Prostogram

0.49; 95% CI 0.37–0.61

c-index

208 (15)

5 year BFFF

Brachytherapy

Kaplan [12]

Survival (Nomogram presented)

Kattan’s: Pretreatment PSA level, Gleason score, clinical stage, adjuvant EBRT

Yes

External validation of Kattan

0.51

c-index

747 (31)

BFFF

125 iodine brachytherapy

Frank [47]

Survival (Nomogram presented)

Pretreatment PSA level, Gleason sum score, T stage, and EBRT

Yes

External validation of Prostogram

0.66

c-index

683 (29)

BCF

Brachytherapy

Zelefsky [10]

Proportional hazards regression (Nomogram presented)

Clinical stage, Gleason, pretreatment PSA

Yes

Not stated

0.70

c-index

1466 (NR)

BCF

Brachytherapy

Potters [16]

Survival (Cox,Nomogram presented)

Clinical stage, Biopsy Gleason sum, Isotope used, EBRT, D90, pretreatment PSA

No, includes isotope used, D90

Internal (bootstrapping)

0.71

c-index

5931 (NR)

9-year BFFF

Brachytherapy

D’Amico [42]

Survival Model (Fine and Gray)

Year of brachytherapy, Log (PSA)per unit increase, Gleason score, Age

Yes

Not stated

Not stated

NA

221 (32)

PCSM and presence of hormone-refractory metastatic prostate cancer

Brachytherapy

Sylvester [57]

Survival model (Cox)

PSA only (<10, 10.1–19.9, >20)

Yes

Not stated

Not stated

NA

215 (NR)

15 year BFFF

Brachytherapy

Sylvester [57]

Survival model (Cox)

PSA only (<10, 10.1–19.9, >20)

Yes

Not stated

Not stated

NA

215 (NR)

15 year PCSM

Brachytherapy

Sylvester [57]

Survival model (Cox)

PSA only (<10, 10.1–19.9, >20)

Yes

Not stated

Not stated

NA

215 (NR)

15 year OS.

Brachytherapy

Bittner [27]

Survival model (Cox)

Number of biopsy cores, PSA, Gleason score, % positive biopsies, V100, EBRT, Risk group, hypertension, Tobacco use, perineural invasion

No, tobacco use, V100, hypertension included.

Not stated

Not stated

NA

1613 (NR)

BFFF

Brachytherapy

Bittner [27]

Survival model (Cox)

PSA, Gleason score, % positive biopsies, EBRT, Risk group, hypertension

No, hypertension

Not stated

Not stated

NA

1613 (NR)

PCSM

Brachytherapy

Bittner [27]

Survival model (Cox)

Number of biopsy cores, age at implant, BMI, V100, D90, EBRT, Risk group, hypertension, diabetes, Tobacco use

No, BMI, V100, D90, hypertension, diabetes included

Not stated

Not stated

NA

1613 (NR)

OS

Brachytherapy

Cooperberg [39]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

Not stated

Not stated

NA

1441 (17)

PCSM

Brachytherapy

Yoshida [15]

Survival model

PRIX score derived from PSA, Gleason and clinical stage

Yes

External

Not stated

NA

100 (9)

5 year BCF

HDR-ISBT

Yoshida [15]

Survival model

PRIX score derived from PSA, Gleason and clinical stage

Yes

External

Not stated

NA

100 (9)

5 year OS

HDR-ISBT

Marshall [11]

Survival model (Cox)

Age, Risk group, hormone treatment, Total BED

Yes

Not stated

Not stated

NA

2495 (251)

BCF

Brachytherapy

Abbreviations OS overall survival, BCF bio chemical failure, BFFF bio chemical freedom from failure, PCSM prostate cancer specific mortality, HDR-ISBT high-dose-rate interstitial brachytherapy, EBRT external beam radiotherapy, NR not reported, NA not applicable

Table 3

Prognostic tools relating to external beam radiation therapy

Author

Model type

Variables

Variable readily available?

Validation (I/E)

Accuracy

Metric

Sample size (events)

Outcome

Tx

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, Stages.

Yes

External validation of AJCC version 6

0.54

c-index

2469 (NR)

OS

3D-CRT, IMRT

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, Stages.

Yes

External validation of AJCC version 7

0.54

c-index

2469 (NR)

OS

3D-CRT, IMRT

Vainshtein [18]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External validation of CAPRA

0.56

c-index

85 (NR)

PCSM

EBRT with long term Androgen deprivation

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, Stages.

Yes

External validation of AJCC version 7

0.58

c-index

2469 (NR)

OS

3D-CRT, IMRT

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, Stages.

Yes

External validation of AJCC version 6

0.52

c-index

2469 (NR)

BCF

3D-CRT, IMRT

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, tages.

Yes

External validation of AJCC version 7

0.6

c-index

2469 (NR)

BCF

3D-CRT, IMRT

Vance [60]

Survival model (Cox)

PSA, Gleason, clinical T stage, PCV, ADT use

Yes

Not stated

0.61 95% CI 0.53-0.68

c-index

599 (NR)

OS

EBRT

Buyyounouski [38]

Survival model

Interval to Biochemical failure (dicotomized at 18 months)

Yes

External validation of IBF

0.61; 95% CI 0.58-0.65; 48.4%; 86.1%

c-index; sensitivity; specificity.

1722 (290)

PCSM

EBRT

Westphalen [62]

Survival (Cox, Nomogram presented)

PSA level, clinical stage (from digital rectal examination findings), sum of Gleason grades, use of neoadjuvant ADT, and radiation dose

Yes

External validation of Kattan with additions

0.61; 95% CI 0.581-0.640

c-index

99 (30)

BCF

EBRT

Qian [52]

Survival model (Cox)

NCCN risk stratification tool plus percent positive cores

Yes

Not stated

0.63

c-index

652 (NR)

BFFF

3D-CRT, IMRT

Vance [60]

Survival model (Cox)

PSA, Gleason, clinical T stage, PCV, ADT use

No (prostate cancer volume)

Not stated

0.64; 95% CI 0.57-0.70

c-index

599 (NR)

BFFF

EBRT

Qian [52]

Survival model (Cox)

NCCN risk stratification tool plus percent positive cores

Yes

Not stated

0.64

c-index

652 (NR)

Metastases

3D-CRT, IMRT

Vainshtein [18]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External validation of CAPRA

0.67

c-index

85 (NR)

BFFF

EBRT with long term Androgen deprivation

Zelefsky [66]

Survival (Cox, Nomogram presented)

ADT, T stage, Gleason, Pre PSA, RT dose.

Yes

Not stated

0.67

c-index

2551

BFFF

3D-CRT, IMRT

Vance [60]

Survival model (Cox)

PSA, Gleason, clinical T stage, PCV, ADT use

No (prostate cancer volume)

Not stated

0.67; 95% CI 0.60-0.74

c-index

599 (NR)

FFM

EBRT

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, Stages.

Yes

External validation of AJCC version 6

0.68

c-index

2469 (NR)

PCSM

3D-CRT, IMRT

Halverson [46]

Survival model (Cox)

CAPRA: PSA, T stage, Gleason score, percent positive biopsy, and age

Yes

External validation of CAPRA

0.69

c-index

612 (NR)

BFFF

EBRT

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, Stages.

Yes

External validation of AJCC version 6

0.70

c-index

2469 (NR)

DM

3D-CRT, IMRT

Qian [52]

Survival model (Cox)

NCCN risk stratification tool plus percent positive cores

Yes

Not stated

0.71

c-index

652 (NR)

PCSM

3D-CRT, IMRT

Zelefsky [68]

Survival (Cox, Nomogram presented)

T stage, Gleason Score, radiation dose, Neoadjuvant ADT, Pre-treatment PSA level,

Yes

Internal (bootstrapping)

0.72

c-index

2253 (578)

BCF

3D-CRT, IMRT

Williams [17]

Survival (Cox, Nomogram presented)

Age, prostate-specific antigen value, Gleason score, clinical stage, androgen deprivation duration, and radiotherapy dose

Yes

Not stated

0.72

c-index

3264 (1048)

BCF

EBRT

Vainshtein [18]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External validation of CAPRA

0.73

c-index

153 (NR)

PCSM

EBRT with short term Androgen deprivation

Steigler [56]

Survival Model (Fine and Gray)

PSA doubling time (PSADT definition specified), time to biochemical failure, high risk category defined by PSADT <4 months or TTBF < 1 year and low risk category by PSADT >9 months or TTBF > 3 years.

Yes

Internal (bootstrapping)

0.73

c-index

485 (150)

PCSM

EBRT

Vance [60]

Survival model (Cox)

PSA, Gleason, clinical T stage, PCV, ADT use

No (prostate cancer volume)

Not stated

0.75; 95% CI 0.67-0.83

c-index

599 (NR)

PCSM

EBRT

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, Stages.

Yes

External validation of AJCC version 7

0.75

c-index

2469 (NR)

DM

3D-CRT, IMRT

Sanpaolo [21]

Survival (Cox, Nomogram presented)

Age, Gleason score, tumor stage, initial PSA, androgen deprivation therapy, pelvic radiotherapy, administered doses, days of radiotherapy, and biologically effective dose

Yes

Internal (bootstrapping)

0.75

c-index

670 (70)

BCF

3D-CRT

Vainshtein [18]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External validation of CAPRA

0.78

c-index

612 (51)

FFM

EBRT

Vainshtein [18]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External validation of CAPRA

0.79

c-index

374 (NR)

FFM

EBRT (no ADT)

Vainshtein [18]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External validation of CAPRA

0.80

c-index

612 (23)

PCSM

EBRT

Vainshtein [18]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External validation of CAPRA

0.80

c-index

153 (NR)

FFM

EBRT with short term Androgen deprivation

Zaorsky [65]

Survival model

Score derived from: Age, PSA, Gleason Score, ADT, Radiation dose, Stages.

Yes

External validation of AJCC version 7

0.81

c-index

2469 (NR)

PCSM

3D-CRT, IMRT

Proust-Lima [51]

Joint Model (Latent Class)

Repeat PSA measures

No

External (two separate cohorts n =503 and 615)

0.82

Weighted average error of prediction (WAEP) at 1 year; after 3 years 0.0614, 0.0095.

1268 (190)

Clinical recurrence

EBRT

Vainshtein [18]

Risk stratification

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External validation of CAPRA

0.86

c-index

374 (NR)

PCSM

EBRT (no ADT)

Yu [63]

Joint modelling

T stage, ln(PSA), Gleason, Age, dose, duration of RT, PSA, slope, HT, Baseline hazards, measurementerrors and tuning parameters.

No, baseline hazards, measurement errors, tuning parameters included

External (prospective on 612 patients from the original cohort)

Not stated

NA

928 (24)

BCF

EBRT

Yu [64]

Survival model (Cox)

Peri-neurial invasion, clinical T stage, Gleason, pre-treatment PSA, radiation dose, ADT

Yes

Not stated

Not stated

NA

657 (145)

BCF

EBRT

Cooperberg [40]

Survival model (Weibull parametric)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External

Not stated

NA

1143 (NR)

10 year PCSM

EBRT

Cooperberg [39]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External

Not stated

NA

1262 (62)

PCSM

EBRT

Zumsteg [69]

Survival model (Cox)

Stratification for NCCN intermediate risk patients based on: Gleason, % Positive biospy cores and number of intermediate risk factors

Yes

Not stated

Not stated

NA

424 (NR)

BFFF

EBRT

Zumsteg [69]

Survival Model (Fine and Gray)

Stratification for NCCN intermediate risk patients based on: Gleason, % Positive biospy cores and number of intermediate risk factors

Yes

Not stated

Not stated

NA

424 (NR)

PCSM

EBRT

Zumsteg [69]

Survival model (Cox)

Stratification for NCCN intermediate risk patients based on: Gleason, % Positive biospy cores and number of intermediate risk factors

Yes

Not stated

Not stated

NA

424 (NR)

LF

EBRT

Zumsteg [69]

Survival model (Cox)

Stratification for NCCN intermediate risk patients based on: Gleason, % Positive biospy cores and number of intermediate risk factors

Yes

Not stated

Not stated

NA

424 (NR)

DM

EBRT

Zelefsky [67]

Survival Model (Fine and Gray)

T stage, Gleason, RT dose, pre-RT PSA, Nadir PSA

Yes

Not stated

Not stated

NA

812 (81)

DM

3D-CRT, IMRT

Zelefsky [67]

Survival Model (Fine and Gray)

T stage, Gleason, RT dose, pre-RT PSA, Nadir PSA

Yes

Not stated

Not stated

NA

843 (65)

PCSM

3D-CRT, IMRT

Zelefsky [67]

Survival model (Cox)

T stage, Gleason, RT dose, pre-RT PSA, Nadir PSA

Yes

Not stated

Not stated

NA

769 (246)

BFFF

3D-CRT, IMRT

Thames [59]

Survival model (Cox)

T stage, Gleason Score, ln(initial PSA), PSA indicator interval, non-treatment day ratio, dose, Overall treatment time

No, Institution adjustment and PSA interval are cohort specific

Not stated

Not stated

NA

3426 (1445)

BCF

2D or 3D-CRT

Taylor [58]

Joint model (longitudinal and survival)

Gleason score, T stage, PSA before treatment, Dose and date of radiation, Serial PSA values after treatment

Yes

External (separate cohort not stated)

Not stated

NA

3232 (458)

Clinical recurrence (local, regional or distant)

EBRT

Murgic [50]

Survival model (Cox)

Age, PSA, T-stage, Gleason, ADT use, Pelvic RT, RT dose, Maximum biopsy core, percent positive cores

No, pelvic RT included

Not stated

Not stated

NA

590 (NR)

BFFF

EBRT

Murgic [50]

Survival model (Cox)

Age, PSA, T-stage, Gleason, ADT use, Pelvic RT, RT dose, Maximum biopsy core, percent positive cores

No, pelvic RT included

Not stated

Not stated

NA

590 (NR)

FFM

EBRT

Murgic [50]

Survival model (Cox)

Age, PSA, T-stage, Gleason, ADT use, Pelvic RT, RT dose, Maximum biopsy core, percent positive cores

Yes, pelvic RT included

Not stated

Not stated

NA

590 (NR)

PCSM

EBRT

Murgic [50]

Survival model (Cox)

Age, PSA, T-stage, Gleason, ADT use, Pelvic RT, RT dose, Maximum biopsy core, percent positive cores

Yes, pelvic RT included

Not stated

Not stated

NA

590 (NR)

OS

EBRT

Spratt [55]

Survival model (Cox)

Age, T-stage, Gleason score, pre-treatment PSA, >50% core involvement, use of ADT, and PSA density

Yes, PSA density can be calculated

Not stated

Not stated

NA

1002 (NR)

BCF

IMRT

Spratt [55]

Survival model (Cox)

Age, T-stage, Gleason score, pre-treatment PSA, >50% core involvement, use of ADT, and PSA density

Yes, PSA density can be calculated

Not stated

Not stated

NA

1002 (NR)

DMFS

IMRT

Spratt [55]

Survival Model (Fine and Gray)

Age, T-stage, Gleason score, pre-treatment PSA, >50% core involvement, use of ADT, and PSA density

Yes, PSA density can be calculated

Not stated

Not stated

NA

1002 (NR)

PCSM

IMRT

Sabolch [53]

Survival model (Cox)

Pre-treatment PSA, T-stage, Gleason score, GP5, ADT, and Charlson comorbidity index.

No, includes Charlson comorbidity index

Not stated

Not stated

NA

718 (NR)

BFFF

3D CT or IMRT

Sabolch [53]

Survival model (Cox)

Pre-treatment PSA, T-stage, Gleason score, GP5, ADT, and Charlson comorbidity index.

No, includes Charlson comorbidity index

Not stated

Not stated

NA

718 (NR)

Freedom from Metastases

3D CT or IMRT

Sabolch [53]

Survival model (Cox)

Pre-treatment PSA, T-stage, Gleason score, GP5, ADT, and Charlson comorbidity index.

No, includes Charlson comorbidity index

Not stated

Not stated

NA

718 (NR)

PCSM

3D CT or IMRT

Sabolch [53]

Survival model (Cox)

Pre-treatment PSA, T-stage, Gleason score, GP5, ADT, and Charlson comorbidity index.

No, includes Charlson comorbidity index

Not stated

Not stated

NA

718 (NR)

OS

3D CT or IMRT

Huang [47]

Survival model (Cox)

Gleason score, iPSA, and % positive cores

Yes

Not stated

Not stated

NA

1056 (176)

BCF

EBRT

Huang [47]

Survival Model (Fine and Gray)

Gleason score, iPSA, and % positive cores

Yes

Not stated

Not stated

NA

1056 (30)

PCSM

EBRT

Huang [47]

Survival model (Cox)

Gleason score, iPSA, and % positive cores

Yes

Not stated

Not stated

NA

1056 (634)

OS

EBRT

Feng [28]

Survival model (Cox); also recursive partitioning

age, race, T stage, PSA, No of biopsy cores taken, percent positive cores, Gleason Score, NCCN risk group, RT dose, Pelvic RT, ADT

No, includes pelvic RT

Not stated

Not stated

NA

651 (NR)

FFM

EBRT

Feng [28]

Survival model (Cox); also recursive partitioning

age, race, T stage, PSA, No of biopsy cores taken, percent positive cores, Gleason Score, NCCN risk group, RT dose, Pelvic RT, ADT

No, includes pelvic RT

Not stated

Not stated

NA

651 (NR)

PCSM

EBRT

Feng [28]

Survival model (Cox); also recursive partitioning

age, race, T stage, PSA, No of biopsy cores taken, percent positive cores, Gleason Score, NCCN risk group, RT dose, Pelvic RT, ADT

No, Includes pelvic RT

Not stated

Not stated

NA

651 (NR)

BFFF

EBRT

Feng [28]

Survival model (Cox); also recursive partitioning

age, race, T stage, PSA, No of biopsy cores taken, percent positive cores, Gleason Score, NCCN risk group, RT dose, Pelvic RT, ADT

No, includes Pelvic RT

Not stated

Not stated

NA

651 (NR)

OS

EBRT

Engineer [9]

Survival model (Cox)

Age, Tumour stage, Gleason score, PSA, ADT, radiation dose, period of treatment

No, includes period of treatment

Not stated

Not stated

NA

174 (21)

BFFF

2D or 3D-CRT

Engineer [9]

Survival model (Cox)

Age, Tumour stage, Gleason score, PSA, ADT, radiation dose, period of treatment

No, includes period of treatment

Not stated

Not stated

NA

174 (98)

Disease free survival

2D or 3D-CRT

Engineer [9]

Survival model (Cox)

Age, Tumour stage, Gleason score, PSA, ADT, radiation dose, period of treatment

No, includes period of treatment

Not stated

Not stated

NA

174 (124)

OS

2D or 3D-CRT

Denham [44]

Survival model (Cox)

Time to biochemical failure

Yes

Not stated

Not stated

NA

802 (125)

PCSM

EBRT

Denham [44]

Survival model (Cox)

PSA doubling time

No, multiple PSA measures required

Not stated

Not stated

NA

802 (125)

PCSM

EBRT

D’Amico [43]

Survival Model (Fine and Gray)

PSA velocity, biopsy Gleason score, PSA, and clinical stage

No, PSA velocity

Not stated

Not stated

NA

288 (32)

PCSM

3D-CRT

Slater [54]

Survival model (Cox)

NCCN grouping, percent positive biopsy cores (PPBC), percentage of cancer volume (PCV), maximum involvement of biopsy scores (MIBC)

No, percentage cancer volume

Not stated

Not stated

NA

398 (NR)

bNED

Proton and photonbeam therapy

D’Ambrosio [41]

Survival model (Cox)

Non-treatment day ratio, absolute number of non-treatment days, Gleason, pre-treatment PSA, T stage, radiation dose

No, includes treatment days

Not stated

Not stated

NA

1796 (NR)

BCF

3D-CRT, IMRT

Abbreviations: OS overall survival, RT radiotherapy, BCF bio chemical failure, BFFF bio chemical freedom from failure, PCSM prostate cancer specific mortality, LF local failure, DM distant metastases, DMFS distant metastases-free survival, FFM freedom from metastases, TTBF time to bio chemical failure, STI secondary therapeutic intervention, bNED bio chemical no evidence of disease, 2D-CRT 2D - Conformal radiotherapy; 3D-CRT 3D -Conformal radiotherapy, EBRT external beam radiotherapy, NA not applicable, NR not reported

Table 4

Prognostic tools relating to combinations of brachytherapy and external beam radiation therapy

Author

Model type

Variables

Variable readily available?

Validation (I/E)

Accuracy

Metric

Sample size (number of events)

Outcome

Tx

Rodrigues [14]

Survival model (Cox)

T stage, PSA and Gleason

Yes

Internal (cross validation)

0.64

c-index

7839 (NR)

OS

Brachytherapy and or EBRT

Rodrigues [14]

Survival model (Cox)

T stage, PSA and Gleason

Yes

Internal (cross validation)

0.67

c-index

7839 (NR)

BFFF

Brachytherapy and or EBRT

Delouya [19]

Survival model (Cox)

CAPRA score (Age, PSA, Gleason score, T-stage, PPB)

Yes

External

0.69, 95%CI 55.0 to 83.8; 0.66, 95%CI 54.4 to 78.3; 0.68, 95%CI 58.5 to 77.2; 0.62 95%CI 53.2 to 70.7

c-index at 2, 3, 4, and 5 years

744 (47)

BFFF

Brachytherapy or EBRT

Delouya [19]

Survival model (Cox)

D’Amico classification (T-stage, PSA and Gleason)

Yes

External

59.1% - 61.6%; and 54.5% - 61.6%

3-5 year sensitivity and specificity

744 (47)

BFFF

Brachytherapy or EBRT

Wattson [61]

Survival Model (Fine and Gray)

Number of high-risk factors (prostate-specific antigen >20 ng/mL, biopsy Gleason score 8–10, or clinical stage T2c), adjusted for age, comorbidity, and the type of supplemental treatment

No, comorbidity

Not stated

Not stated

NA

2234 (57)

PCSM

EBRT and or Brachytherapy

Kubicek [48]

Survival model

Mid therapy PSA (<25% vs > =25%)

No, mid therapy PSA cohort specific

Not stated

Not stated

NA

717 (NR)

Disease free survival

Brachytherapy and EBRT

Kubicek [48]

Survival model

Mid therapy PSA (<25% vs > =25%)

No, mid therapy PSA cohort specific

Not stated

Not stated

NA

717 (NR)

OS

Brachytherapy and EBRT

Krishnan [20]

Survival model (Cox)

CAPRA scores (based on PSA, Biopsy Gleason, Age at diagnosis, clinical tumour stage and % biopsy cores positive for cancer)

Yes

External

Not stated

NA

345 (45)

BCF

EBRT and/or LDR

McKenna [49]

Survival model (Cox)

Patient age, hormonal treatment, baseline PSA, and degree of extracapsular extension, pre-treatment MRI

Yes, where MRI is routine

Not stated

Not stated

NA

80 (4)

Metastatic recurrence and BCF

EBRT or EBRT with Brachytherapy

Abbreviations: OS overall survival, BCF bio chemical failure, BFFF bio chemical freedom from failure, PCSM prostate cancer specific mortality, NA not applicable, NR not reported, MRI magnetic resonance imaging

The 47 papers finally included in this review described 97 individual predictive models. Of these models, 16 related to brachytherapy treatment (Table 2), 72 to external beam radiation therapy (Table 3) and nine to a combination of brachytherapy and external beam radiation therapy (Table 4).

Across all radiation treatment modalities, outcomes relating to PSA levels post treatment were most common (39 models) followed by prostate cancer specific mortality (29 models). Measures of metastases (17) and overall survival (14 models) were less common (note that some papers report more than one outcome and model). Of those studies reporting development of new models (66), only nine reported validation either internally or in an additional cohort. Only 67/97 (69%) models included variables which were considered to be readily available in existing data sets.

Critical appraisal considered the criteria set by the CEBM appraisal tool for prognostic studies [9]. Risk of bias ranged from moderate (Q1; Was the defined representative sample of patients assembled at a common point in the course of their disease? (72%), Q2; Was patient follow-up sufficiently long and complete? (64%)) to low (Q3; Were outcome criteria either objective or applied in a ‘blind’ fashion? (85%), Q4; If subgroups with different prognoses are identified, did adjustment for important prognostic factors take place? (91%)) (Table 5).
Table 5

Risk of bias assessment summary table

Study Id

Q1

Q2

Q3

Q4

Cooperberg [39]

high

low

low

low

Bittner [27]

high

low

high

low

Buyyounouski [38]

low

low

low

low

Cooperberg (41)

low

high

low

low

Delouya [19]

low

high

low

low

Engineer [9]

low

high

low

low

Feng [28]

low

low

low

low

Frank [25]

unclear

high

low

low

Frank [45]

unclear

low

unclear

low

Halverson [46]

low

low

low

low

Huang [47]

low

low

low

low

Kaplan [12]

unclear

high

low

low

Krishnan [20]

low

high

low

low

Kubicek [48]

low

low

low

high

Marshall [11]

unclear

low

low

low

Potters [16]

unclear

high

low

low

Rodrigues [14]

high

unclear

low

low

Proust-Lima [51]

low

low

unclear

low

Sabolch [53]

low

low

low

low

Sanpaolo [21]

low

low

low

low

Slater [54]

high

low

low

low

Spratt [55]

low

low

low

low

Steigler [56]

low

low

low

unclear

Taylor [58]

low

low

unclear

low

Vainshtein [18]

low

low

low

low

Vance [60]

low

low

low

low

Wattson [61]

low

high

low

low

Westphalen [62]

unclear

high

low

low

Williams [17]

low

high

low

low

Yoshida [15]

unclear

low

unclear

low

Zaorsky [65]

low

low

low

low

Zelefsky [10]

low

high

low

low

Zelefsky [68]

low

low

low

low

Zelefsky [66]

low

low

low

low

Zumsteg [69]

low

low

low

low

D’Amico [43]

low

high

low

low

Yu [64]

low

low

low

low

D’Ambrosio [41]

unclear

low

low

low

Denham [44]

low

unclear

low

low

McKenna [49]

unclear

high

low

high

Yu [63]

low

unclear

unclear

low

D’Amico [42]

low

low

low

low

Zelefsky [67]

low

low

low

low

Thames [59]

low

low

unclear

low

Qian [52]

low

low

low

low

Sylvester [57]

low

low

low

high

Murgic [50]

low

high

low

low

Low/47

34 (72%)

30 (64%)

40 (85%)

43 (91%)

Q1: Was the defined representative sample of patients assembled at a common (usually early) point in the course of their disease)? Q2: Was patient follow-up sufficiently long and complete? Q3: Were outcome criteria either objective or applied in a ‘blind’ fashion? Q4: If subgroups with different prognoses are identified, did adjustment for important prognostic factors take place?

High = high risk of bias, low = low risk of bias, unclear = unclear if study design is at high or low risk of bias

Brachytherapy

In regards to models predicting outcomes following brachytherapy, Potters et al. [17] report the highest c-index in a model developed and internally validated using a cohort of 5,931 patients. This model predicts 9 year freedom from biochemical failure and remains to be validated externally. Eleven models relating to brachytherapy (69%) did not report model accuracy and among those models which did report accuracy, all related to biochemical failure endpoints. Three studies report to be external validations of the Prostogram nomogram (also known as the Kattan nomogram), all of which have low c-indices (0.49, 0.51 and 0.66) suggesting that this model is of limited clinical utility. A c-index of 1 ‘indicates a perfect ability to rank the outcomes in the order they actually occurred (100% sensitivity and specificity), whereas 0.5 is a purely random ranking and is analogous to the area under the receiver operator characteristic curve’ (definition from [18]).

The majority of papers identified in this review reported models relating to external beam radiation therapy (72/97 = 74%). Fifty-four percent (39 of 72) of these models did not have their accuracy reported. 61% of models did not report validation (either internal or external, including external validation of already published models).

External beam radiation therapy

The model relating to external beam radiation therapy with the highest accuracy was described by Vainshtein [19], which was an external validation of the CAPRA stratification in the context of external beam radiation therapy. The cohort included 374 patients and the endpoint of prostate cancer specific mortality was predicted with c-index of 0.86. Accuracy of this model is also reported for the outcome of biochemical failure and subgroups of patients receiving long term ADT or short term ADT, all which had lower accuracy.

External beam radiation therapy with brachy therapy

Nine models were identified which were specific to patients treated with external beam radiation therapy in combination with brachytherapy. Of these models, five (56%) did not report accuracy. The highest accuracy was reported by Delouya [15, 20] (c-index 0.69) predicting biochemical failure free survival at 2-years. This study was based on a cohort of 744 patients and was an external validation of the CAPRA score. Prediction at 5-years was achieved with c-index 0.62.

Discussion

Since the publication of previous reviews, there has been considerable progress in the field of outcomes prediction following prostate cancer treatment. This review identified 47 papers published between 2007 and 2015, which describe 97 predictive tools for men receiving radiotherapy. This includes 66 models which were newly developed and 31 which were validations of already published predictive tools. Consistent with previous reports, most tools (65%) are yet to be validated in a population outside the derivation set. Studies were included from 2007 as the modality of radiation therapy has changed significantly over the past decade, and historic data may not be a useful basis for prognosis. Apart from modality, the total dose has also significantly increased however, we found that only five studies [13, 16, 2022] did not use data from men treated as far back as the 1990s.

The volume of research carried out in the field of prognostics has exploded over the last decade. A systematic review that included all studies published before July 2007 (the cut-off date for inclusion in the present review) identified 17 studies on prognostic models that related to prostate cancer patients treated with radiotherapy [4]. In this review 39 new studies were identified which investigated prognostic markers for BCF. Unfortunately, the majority of new studies did not undertake validation, mirroring the finding of the previous systematic review. As validation – particularly external validation – is vital for the appropriate clinical implementation of prognostic models, this suggests that resources and efforts are not being efficiently targeted to improve tools available for clinical practice.

With regards to the methodological quality of the literature, our critical appraisal found that overall studies were at low to moderate risk of bias. The greatest risk was created by insufficient follow-up (defined as a mean or median of ≥5 years) which only occurred in 64% of studies. There was also a moderate risk of bias created by the possibility of included patients being at different points in the course of their prostate cancer, however in the majority of cases this was due to insufficient specificity in the description of inclusion criteria as opposed to reported differences. There was little risk of bias created by the measurement of outcomes, as the main outcomes (biochemical failure [various definitions], metastasis, survival) were objective, or by a lack of adjustment for important prognostic factors as the essential factors of prostate cancer prognosis (PSA, Gleason score, and clinical stage) were used nearly universally.

Model accuracy was not reported in 57% of the models included. Model accuracy was reported to be highest in Vainshtein 2014 [23] with a c-index of 0.86 derived for prediction of prostate cancer specific mortality with the CAPRA score (originally established in [24]), including the addition of variables for the presence of Gleason 5 and treatment with ADT (this c-index relates to patients not receiving ADT). This study acts to externally validate the CAPRA scoring system (with modifications) in patients treated with external beam radiation therapy, though this improvement to the score requires further validation in other populations. Of the remaining 42 models which reported predictive accuracy, c indices were typically in the 0.70–0.80 range which would be considered ‘reasonable’ according to Hosmer and Lemeshow [25]. Notably, those papers which did not report external validation typically had higher c-indices suggesting that original model developments should be considered optimistic in their predictive capacity. The lowest c-index (0.49, 95%CI 0.37 to 0.61) was reported for a study [26] performing external validation of the Prostogram nomogram (originally established in [27]) suggesting this nomogram may have little predictive value.

The predictive tools identified in this review included joint-modelling approaches but not neural networks which have featured in previous reviews. This may reflect a change in statistical tools available since publication of earlier catalogues [4]. Two of the survival models [28, 29] did not account for competing risks when predicting prostate cancer specific mortality, a potential weakness which could easily be addressed.

The majority of papers attempted prediction relating to biochemical recurrence, prostate cancer specific mortality or overall survival with a smaller subset predicting metastases. Sixteen of the 97 models identified related to brachytherapy with 72 for external beam radiation therapy and 9 a combination of the two. This could reflect more wide-spread use of external beam radiation therapy, and we might anticipate more tools relating to HDR brachytherapy (with or without EBRT) in the future. There is a dearth of externally validated nomograms focusing on brachytherapy and brachytherapy in combination with external beam radiation therapy particularly looking at overall survival and cancer specific survival outcomes.

This study did not explicitly set out to uncover tools incorporating novel variables, but only those which could be used in current clinical settings. Despite this, 31% of studies included reference to variables which have been less studied to date (e.g. mid-point PSA levels). While such variables may prove useful, there is currently limited opportunity to validate these observations using existing datasets. It is possible that additional variables including standardised measures of comorbidity, imaging features or genetic markers, which are becoming more accessible may help to improve the accuracy of future models. For a recent review of potential molecular and genetic candidate see Hall et al. 2016 [30].

Most predictive tools identified in this review were developed in US populations. This observation should be considered by clinicians who are based outside the US when selecting a predictive model to assist treatment decision making. Where possible, tools validated in a setting similar to one’s own clinical practice should be selected for use. The number of tools available internationally would be increased with additional validation work conducted outside the US and particularly in multi-national cohorts.

We observed a large degree of variation in the quality of reporting clinical predictive tools. This may stem from the fact that authors are not aware of reporting guidelines in the field or indeed that such guidelines exist. The TRIPOD guidelines (http://www.equator-network.org/reporting-guidelines/tripod-statement/) for reporting of multivariable prediction models were published in March 2015, shortly before the cut-off for papers included in this review. These guidelines have been widely endorsed and published in key journals [3139]. Further publication of multivariable models would benefit greatly from adherence to these guidelines.

Conclusions

Tools which aid decision making offer more accurate prediction of clinical outcomes when compared to clinical judgement alone. This understanding has led to a large increase in the number of predictive tools relating to clinical outcomes post radiation therapy between 2007 and 2015. This review identifies 47 papers describing 97 models published in the period, a substantial increase compared to the 17 models previously described between 1966 and 2007. Of the models identified, 65% had no external validation and 57% did not report accuracy. Thirty one percent of models included variables which are not part of typical registry data sets, and are therefore difficult to validate. Despite these limitations, there are accurate and externally validated models for external beam radiation therapy treatment which predict prostate cancer specific mortality. There are fewer models which accurately predict outcomes following brachytherapy (alone or in combination with external beam radiation therapy). This review provides an accessible catalogue of predictive tools which could be used currently (i.e. those with high accuracy after external validation) and identifies those which should be prioritised for future validation.

Abbreviations

BCR: 

Biochemical recurrence

BF: 

Biochemical failure

EBRT: 

External beam radiation therapy

OS: 

Overall survival

PCSM: 

Prostate cancer specific mortality

TTP: 

Time to progression

Declarations

Acknowledgments

This project was funded by the Movember Foundation as part of the Prostate Cancer Health Outcomes Research Unit.

Availability of data and materials

All data reported in this publication is publically available.

Authors’ contributions

ER, JC, and MOC conducted the literature searches, screening, appraisal and drafted the manuscript. AV, KB, DR, SE, JM, JM, JZ, MB and KM critically reviewed the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
South Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC)
(2)
Freemasons Foundation Centre for Men’s Health, University of Adelaide
(3)
Centre for Population Health Research, University of South Australia
(4)
Epidemiology & Preventative Medicine, Monash University
(5)
Radiation Oncology, Alfred Health
(6)
Adelaide Radiotherapy Centre
(7)
SA Health, Repatriation General Hospital, Urology Unit
(8)
Flinders Centre for Innovation in Cancer
(9)
Joanna Briggs Institute, University of Adelaide
(10)
Discipline of Surgery, University of Adelaide
(11)
School of Public Health and Preventive Medicine, Monash University

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© The Author(s). 2017

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