Evidence map›Paper›PMID 28327203›Full record

SynthesisRadiation oncology (London, England)2017

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

Elspeth Raymond, Michael E O'Callaghan, Jared Campbell, Andrew D Vincent, Kerri Beckmann, David Roder, Sue Evans, John McNeil, Jeremy Millar, John Zalcberg and 2 more

Abstract readSystematic Review
In one paragraph

Synthesis in Radiation oncology (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Elspeth RaymondSouth Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC), Adelaide, Australia.
Michael E O'CallaghanSouth Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC), Adelaide, Australia. Michael.ocallaghan@health.sa.gov.au.
Jared CampbellJoanna Briggs Institute, University of Adelaide, Adelaide, Australia.
Andrew D VincentSouth Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC), Adelaide, Australia.
Kerri BeckmannSouth Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC), Adelaide, Australia.
David RoderCentre for Population Health Research, University of South Australia, Adelaide, Australia.
Sue EvansEpidemiology & Preventative Medicine, Monash University, Clayton, Australia.
John McNeilEpidemiology & Preventative Medicine, Monash University, Clayton, Australia.
Jeremy MillarRadiation Oncology, Alfred Health, Melbourne, Australia.
John ZalcbergEpidemiology & Preventative Medicine, Monash University, Clayton, Australia.
Martin BorgAdelaide Radiotherapy Centre, Adelaide, Australia.
Kim MorettiSouth Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC), Adelaide, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProstate 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.

methodsA 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.

resultsThe 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).

conclusionsA 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.

Indexed as

HumansMaleNomogramsPrognosisProstatic NeoplasmsTreatment OutcomeBiochemical recurrenceNomogramOutcomesProstate cancerSurvivalSystematic literature review

Identifiers

PMID28327203
PMCPMC5359887

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.