Evidence map›Paper›PMID 37717420›Full record

ArticleEuropean journal of radiology2023

Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI.

Enis C Yilmaz, Stephanie A Harmon, Mason J Belue, Katie M Merriman, Tim E Phelps, Yue Lin, Charisse Garcia, Lindsey Hazen, Krishnan R Patel, Maria J Merino and 5 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. AI in Prostate MRI: A Task-Based Review.Journal of the Korean Society of Radiology · 2025
    Review
  4. 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

15 authors at 2 institutions in 1 country.

Enis C YilmazMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Stephanie A HarmonMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Mason J BelueMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Katie M MerrimanMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Tim E PhelpsMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Yue LinMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Charisse GarciaCenter for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States.
Lindsey HazenCenter for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States.
Krishnan R PatelRadiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Maria J MerinoLaboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Bradford J WoodCenter for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States.
Peter L ChoykeMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Peter A PintoUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Deborah E CitrinRadiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
Baris TurkbeyMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, United States. Electronic address: turkbeyi@mail.nih.gov.
National Cancer Institute · USNational Institutes of Health · US

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

objectiveTo evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history. MATERIALS AND

methodsThis study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles.

resultsOf the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0-2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively.

conclusionThe reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.

Indexed as

Deep LearningProstatic NeoplasmsAgedArtificial IntelligenceHumansMagnetic Resonance ImagingMaleProspective StudiesProstateProstate-Specific AntigenRetrospective StudiesProstate-Specific AntigenArtificial intelligenceBiochemical recurrenceMRIProstate cancerRadiotherapy

Identifiers

PMID37717420
PMCPMC10615746
OpenAlexW4386629635

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.