Evidence map›Paper›PMID 36006066›Full record

ArticleTomography (Ann Arbor, Mich.)2022

Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study.

Eliodoro Faiella, Daniele Vertulli, Francesco Esperto, Ermanno Cordelli, Paolo Soda, Rosa Maria Muraca, Lorenzo Paolo Moramarco, Rosario Francesco Grasso, Bruno Beomonte Zobel, Domiziana Santucci

Abstract read
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Practical applications of AI in body imaging.Abdominal radiology (New York) · 2026
    Review
  4. Review
  5. Review
  6. Observational
  7. Review
  8. Article
  9. Review
  10. Article
  11. [Digitalization in urology-challenge and opportunity].Urologie (Heidelberg, Germany) · 2023
    Review
  12. Review
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.

Eliodoro FaiellaDepartment of Radiology, Campus Bio-Medico University, Via Alvaro del Portillo, 00128 Rome, Italy.
Daniele VertulliDepartment of Radiology, Campus Bio-Medico University, Via Alvaro del Portillo, 00128 Rome, Italy.ORCID 0000-0002-7301-1670
Francesco EspertoDepartment of Urology, Campus Bio-Medico University, Via Alvaro del Portillo, 00128 Rome, Italy.
Ermanno CordelliUnit of Computer Systems and Bioinformatics, Department of Engineering, Campus Bio-Medico University, Via Alvaro del Portillo, 00128 Rome, Italy.
Paolo SodaUnit of Computer Systems and Bioinformatics, Department of Engineering, Campus Bio-Medico University, Via Alvaro del Portillo, 00128 Rome, Italy.ORCID 0000-0003-2621-072X
Rosa Maria MuracaDepartment of Radiology, Sant'Anna Hospital, Via Ravona, San Fermo della Battaglia, 22042 Como, Italy.
Lorenzo Paolo MoramarcoDepartment of Radiology, Sant'Anna Hospital, Via Ravona, San Fermo della Battaglia, 22042 Como, Italy.
Rosario Francesco GrassoDepartment of Radiology, Campus Bio-Medico University, Via Alvaro del Portillo, 00128 Rome, Italy.
Bruno Beomonte ZobelDepartment of Radiology, Campus Bio-Medico University, Via Alvaro del Portillo, 00128 Rome, Italy.
Domiziana SantucciDepartment of Radiology, Campus Bio-Medico University, Via Alvaro del Portillo, 00128 Rome, Italy.ORCID 0000-0003-4903-8715

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To evaluate the clinical utility of an Artificial Intelligence (AI) radiology solution, Quantib Prostate, for prostate cancer (PCa) lesions detection on multiparametric Magnetic Resonance Images (mpMRI). Methods: Prostate mpMRI exams of 108 patients were retrospectively studied. The diagnostic performance of an expert radiologist (>8 years of experience) and of an inexperienced radiologist aided by Quantib software were compared. Three groups of patients were assessed: patients with positive mpMRI, positive target biopsy, and/or at least one positive random biopsy (group A, 73 patients); patients with positive mpMRI and a negative biopsy (group B, 14 patients), and patients with negative mpMRI who did not undergo biopsy (group-C, 21 patients). Results: In group A, the AI-assisted radiologist found new lesions with positive biopsy correlation, increasing the diagnostic PCa performance when compared with the expert radiologist, reaching an SE of 92.3% and a PPV of 90.1% (vs. 71.7% and 84.4%). In group A, the expert radiologist found 96 lesions on 73 mpMRI exams (17.7% PIRADS3, 56.3% PIRADS4, and 26% PIRADS5). The AI-assisted radiologist found 121 lesions (0.8% PIRADS3, 53.7% PIRADS4, and 45.5% PIRADS5). At biopsy, 33.9% of the lesions were ISUP1, 31.4% were ISUP2, 22% were ISUP3, 10.2% were ISUP4, and 2.5% were ISUP5. In group B, where biopsies were negative, the AI-assisted radiologist excluded three lesions but confirmed all the others. In group-C, the AI-assisted radiologist found 37 new lesions, most of them PIRADS 3, with 32.4% localized in the peripherical zone and 67.6% in the transition zone. Conclusions: Quantib software is a very sensitive tool to use specifically in high-risk patients (high PIRADS and high Gleason score).

Indexed as

Multiparametric Magnetic Resonance ImagingProstatic NeoplasmsArtificial IntelligenceHumansMagnetic Resonance ImagingMaleProstateRadiologistsRetrospective StudiesArtificial Intelligence (AI)multiparametric Magnetic Resonance Imaging (mpMRI)prostate cancer (PCa) lesionsQuantib Prostate software

Identifiers

PMID36006066
PMCPMC9415513

What OpenQuestion holds

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LicenceCC BY
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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.