Evidence map›Paper›PMID 36209237›Full record

ArticleProstate cancer and prostatic diseases2023

MRI-targeted biopsy cores from prostate index lesions: assessment and prediction of the number needed.

Nick Lasse Beetz, Franziska Dräger, Charlie Alexander Hamm, Seyd Shnayien, Madhuri Monique Rudolph, Konrad Froböse, Sefer Elezkurtaj, Matthias Haas, Patrick Asbach, Bernd Hamm and 7 more

Open access · hybridAbstract read
In one paragraph

Article in Prostate cancer and prostatic diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.3field-weighted citation impact, top 10% of its field
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

11 citing papers in PubMed, 15 citations in OpenAlex.

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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

17 authors at 3 institutions in 1 country.

Nick Lasse BeetzDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany. nick-lasse.beetz@charite.de.ORCID 0000-0003-4960-5652
Franziska DrägerDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Charlie Alexander HammDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0003-4104-4758
Seyd ShnayienDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Madhuri Monique RudolphDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0002-8274-0779
Konrad FroböseDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Sefer ElezkurtajDepartment of Pathology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Matthias HaasDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0001-8398-2908
Patrick AsbachDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Bernd HammDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Samy MahjoubDepartment of Urology, Medizinische Hochschule Hannover, Hannover, Germany.
Frank KonietschkeInstitute of Biometry and Clinical Epidemiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Maximilian WechsungInstitute of Biometry and Clinical Epidemiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Felix BalzerInstitute of Medical Informatics, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0003-1575-2056
Hannes CashDepartment of Urology, University Hospital Magdeburg, Magdeburg, Sachsen-Anhalt, Germany.ORCID 0000-0002-1064-5633
Sebastian HofbauerDepartment of Urology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Tobias PenzkoferDepartment of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Humboldt-Universität zu Berlin · DEMedizinische Hochschule Hannover · DEUniversity Hospital Magdeburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMagnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model.

methodsWe retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates.

resultsThe most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model.

conclusionThe most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates.

Indexed as

ProstateProstatic NeoplasmsBayes TheoremHumansImage-Guided BiopsyMagnetic Resonance ImagingMaleProstate-Specific AntigenRetrospective StudiesProstate-Specific Antigen

Identifiers

PMID36209237
PMCPMC10449625
OpenAlexW4303645601

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.