Evidence map›Paper›PMID 35953766›Full record

ArticleBMC cancer2022

Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population.

Artitaya Lophatananon, Matthew H V Byrne, Tristan Barrett, Anne Warren, Kenneth Muir, Ibifuro Dokubo, Fanos Georgiades, Mostafa Sheba, Lisa Bibby, Vincent J Gnanapragasam

Abstract read
In one paragraph

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
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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

10 authors.

Artitaya LophatananonDivision of Population Health, Health Services Research & Primary Care Centre, University of Manchester, Manchester, UK.
Matthew H V ByrneDepartment of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Tristan BarrettDepartment of Radiology, University of Cambridge, Cambridge, UK.
Anne WarrenDepartment of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Kenneth MuirDivision of Population Health, Health Services Research & Primary Care Centre, University of Manchester, Manchester, UK.
Ibifuro DokuboDepartment of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Fanos GeorgiadesDepartment of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Mostafa ShebaKasr Al Any School of Medicine, Cairo University, Giza, Egypt.
Lisa BibbyDepartment of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Vincent J GnanapragasamDepartment of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. vjg29@cam.ac.uk.ORCID http://orcid.org/0000-0003-4722-4207

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPre-treatment risk and prognostic groups are the cornerstone for deciding management in non-metastatic prostate cancer. All however, were developed in the pre-MRI era. Here we compared categorisation of cancers using either only clinical parameters or with MRI enhanced information in men referred for suspected prostate cancer from an unscreened population. PATIENT AND

methodsData from men referred from primary care to our diagnostic service and with both clinical (digital rectal examination [DRE] and systematic biopsies) and MRI enhanced attributes (MRI stage and combined systematic/targeted biopsies) were used for this study. Clinical vs MRI data were contrasted for clinico-pathological and risk group re-distribution using the European Association of Urology (EAU), American Urological Association (AUA) and UK National Institute for Health Care Excellence (NICE) Cambridge Prognostic Group (CPG) models. Differences were retrofitted to a population cohort with long-term prostate cancer mortality (PCM) outcomes to simulate impact on model performance. We further contrasted individualised overall survival (OS) predictions using the Predict Prostate algorithm.

resultsData from 370 men were included (median age 66y). Pre-biopsy MRI stage reassignments occurred in 7.8% (versus DRE). Image-guided biopsies increased Grade Group 2 and ≥ Grade Group 3 assignments in 2.7% and 2.9% respectively. The main change in risk groups was more high-risk cancers (6.2% increase in the EAU and AUA system, 4.3% increase in CPG4 and 1.9% CPG5). When extrapolated to a historical population-based cohort (n = 10,139) the redistribution resulted in generally lower concordance indices for PCM. The 5-tier NICE-CPG system outperformed the 4-tier AUA and 3-tier EAU models (C Index 0.70 versus 0.65 and 0.64). Using an individualised prognostic model, changes in predicted OS were small (median difference 1% and 2% at 10- and 15-years' respectively). Similarly, estimated treatment survival benefit changes were minimal (1% at both 10- and 15-years' time frame).

conclusionMRI guided diagnostics does change pre-treatment risk groups assignments but the overall prognostic impact appears modest in men referred from unscreened populations. Particularly, when using more granular tiers or individualised prognostic models. Existing risk and prognostic models can continue to be used to counsel men about treatment option until long term survival outcomes are available.

Indexed as

Prostatic NeoplasmsAgedHumansImage-Guided BiopsyMagnetic Resonance ImagingMalePrognosisProstateCancer specific survivalMRI ReclassificationOverall survivalPrognostic groupsProstate cancer

Identifiers

PMID35953766
PMCPMC9367076

What Socratic 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 Socratic graph.