Evidence map›Paper›PMID 42276879›Full record

ArticleEuropean urology oncology2026

Head-to-head Comparison of Multimodal Imaging for Prostate Cancer Detection and Local Staging with Whole-mount Histopathology Reference.

Qi Miao, Anthony Sisk, Ida Sonni, Kyla Grunden, Vinicius Ludwig, Kaifeng Pang, Kevin Walsh, Jordan Klein, Zahra Mohammadi Goldar, Robert E Reiter and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European urology oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Qi MiaoDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, China. Electronic address: qmiao@mednet.ucla.edu.
Anthony SiskPathology Department, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Ida SonniDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Kyla GrundenInstitute of Urologic Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Vinicius LudwigDepartment of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Kaifeng PangDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Kevin WalshInstitute of Urologic Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Jordan KleinDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Zahra Mohammadi GoldarDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Robert E ReiterInstitute of Urologic Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Steven S RamanDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Wayne G BrisbaneInstitute of Urologic Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
KyungHyun SungDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

Funding

Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer ClassificationR01CA248506 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SUNG, KYUNG HYUN, WU, HOLDEN H · 2020 to 2024
$3.0M
Racially-associated MRI analysis and modeling for predicting aggressive prostate cancerR01CA272702 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Harrison Kim, Kyung Hyun Sung · 2023 to 2026
$2.4M
NCI NIH HHS R01 CA248506NCI NIH HHS R01 CA272702
6 · The paper itself

Abstract

backgroundAccurate detection and local staging of prostate cancer (PCa) remain critical for treatment planning, yet head-to-head multimodal imaging comparisons remain limited.

objectiveThis study compared microultrasound (microUS), multiparametric magnetic resonance imaging (mpMRI), and prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) for detecting clinically significant PCa (csPCa) and extraprostatic extension (EPE), using whole-mount histopathology (WMHP) as the reference standard. DESIGN, SETTING, AND

participantsThis retrospective study included patients who underwent microUS and mpMRI followed by radical prostatectomy between January 2023 and October 2024. A subset also underwent PSMA PET/CT. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Detection of csPCa and EPE was assessed at the lesion and sector levels using WMHP as a reference. Detection rates and positive predictive values (PPVs) were compared using McNemar and chi-square tests, while sector-level area under the receiver operating characteristic curves (AUCs) were compared using the DeLong test. RESULTS AND LIMITATIONS: No statistically significant differences were observed between microUS and mpMRI or PSMA PET/CT for index lesion detection (0.90, 0.94, and 0.93, respectively) or csPCa detection (0.71, 0.78, and 0.72, respectively), although the study may have been underpowered to detect small differences. mpMRI had higher PPVs for csPCa than microUS (0.91 vs 0.77, p = 0.03). mpMRI achieved the highest sector-level AUCs for csPCa (0.73) and EPE (0.88). Across modalities, 15-18% of csPCa lesions were missed, which were non-index Grade Group 2 with Gleason pattern 4 ≤20%. The study is limited by its single-center retrospective design.

conclusionsMicroUS showed no statistically significant differences compared with mpMRI and PSMA PET/CT for detecting index and csPCa lesions, supporting a potential complementary role in resource-limited or MRI-contraindicated settings.

Indexed as

HistopathologyMicroultrasoundMultiparametric magnetic resonance imagingProstate cancerPSMA PET/CTRadical prostatectomy

Identifiers

PMID42276879
PMCPMC13422935

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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.