Evidence map›Paper›PMID 39079991›Full record

ArticleAbdominal radiology (New York)2024

Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study.

Rossano Girometti, Valeria Peruzzi, Paolo Polizzi, Maria De Martino, Lorenzo Cereser, Letizia Casarotto, Stefano Pizzolitto, Miriam Isola, Alessandro Crestani, Gianluca Giannarini and 1 more

Abstract read
In one paragraph

Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

11 authors.

Rossano GiromettiInstitute of Radiology, Department of Medicine (DMED), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy. rossano.girometti@uniud.it.ORCID 0000-0002-0904-5147
Valeria PeruzziInstitute of Radiology, Department of Medicine (DMED), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy.
Paolo PolizziInstitute of Radiology, Department of Medicine (DMED), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy.
Maria De MartinoDivision of Medical Statistics, Department of Medicine (DMED), University of Udine, pl.le Kolbe, 4 - 33100, Udine, Italy.
Lorenzo CereserInstitute of Radiology, Department of Medicine (DMED), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy.
Letizia CasarottoPathology Unit, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy.
Stefano PizzolittoPathology Unit, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy.
Miriam IsolaDivision of Medical Statistics, Department of Medicine (DMED), University of Udine, pl.le Kolbe, 4 - 33100, Udine, Italy.
Alessandro CrestaniUrology Unit, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy.
Gianluca Giannarini *Urology Unit, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy.
Chiara Zuiani *Institute of Radiology, Department of Medicine (DMED), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 - 33100, Udine, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives.

methodsOne hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions.

resultscsPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9-77.6/54.5-70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5-84.5/59.4-77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only.

conclusionCase-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

Indexed as

Magnetic Resonance ImagingProstatic NeoplasmsAgedFalse Positive ReactionsHumansMaleMiddle AgedProof of Concept StudyProstateRadiology Information SystemsRetrospective StudiesSensitivity and SpecificityBiopsyMultiparametric magnetic resonance imagingPI-RADSProstatic neoplasms

Identifiers

PMID39079991
PMCPMC11522071

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