Evidence map›Paper›PMID 39579754›Full record

ArticleClinical imaging2025

Multimodal approach to optimize biopsy decision-making for PI-RADS 3 lesions on multiparametric MRI.

Omer Tarik Esengur, Enis C Yilmaz, Kutsev B Ozyoruk, Alex Chen, Nathan S Lay, David G Gelikman, Maria J Merino, Sandeep Gurram, Bradford J Wood, Peter L Choyke and 3 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

13 authors.

Omer Tarik EsengurMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Enis C YilmazMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Kutsev B OzyorukMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Alex ChenMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Nathan S LayMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
David G GelikmanMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Maria J MerinoLaboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Sandeep GurramUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Bradford J WoodCenter for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Peter L ChoykeMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Stephanie A HarmonMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Peter A PintoUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Baris TurkbeyMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. Electronic address: turkbeyi@mail.nih.gov.

Funding

Imaging, image guided biopsy and treatment of prostate cancerZIABC012062 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI TÜRKBEY, BARIS · 2021 to 2025
$1.3M
Intramural NIH HHS ZIA BC012062
6 · The paper itself

Abstract

purposeTo develop and evaluate a multimodal approach including clinical parameters and biparametric MRI-based artificial intelligence (AI) model for determining the necessity of prostate biopsy in patients with PI-RADS 3 lesions.

methodsThis retrospective study included a prospectively recruited patient cohort with PI-RADS 3 lesions who underwent prostate MRI and MRI/US fusion-guided biopsy between April 2019 and February 2024 in a single institution. The study examined demographic data, PSA and PSA density (PSAD) levels, prostate volumes, prospective PI-RADS v2.1-compliant interpretations of a genitourinary radiologist, lesion characteristics, history of prior biopsies, and AI evaluations, focusing mainly on the detection of clinically significant prostate cancer (csPCa) (International Society of Urological Pathology grade group ≥2) on MRI/US fusion-guided biopsy. The AI model lesion segmentations were compared to manual segmentations and biopsy results. The statistical methods employed included Fisher's exact test and logistic regression.

resultsThe cohort was comprised of 248 patients with 312 PI-RADS 3 lesions in total (n = 268 non-csPCa, n = 44 csPCa). The AI model's negative predictive value (NPV) was 89.2 % for csPCa in all lesions. In patient-level analysis, the NPV was 91.2 % for patients with a highest PI-RADS score of 3. PSAD was a significant predictor of csPCa (odds ratio = 5.8, p = 0.038). Combining AI and PSAD, where AI correctly mapped a lesion or PSAD ≥0.15 ng/mL

conclusionCombining AI and PSAD has the potential to enhance biopsy decision-making for PI-RADS 3 lesions by minimizing missed csPCa occurrences and reducing unnecessary biopsies.

Indexed as

Image-Guided BiopsyMultiparametric Magnetic Resonance ImagingProstatic NeoplasmsAgedArtificial IntelligenceClinical Decision-MakingHumansMaleMiddle AgedMultimodal ImagingProstateRetrospective StudiesArtificial intelligenceBiopsy decision-makingMultiparametric MRIPI-RADS 3 lesionsProstate cancerPSA density

Identifiers

PMID39579754
PMCPMC11624978

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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.