Evidence map›Paper›PMID 39847533›Full record

Trial reportThe British journal of radiology2025

[18F]F-DCFPyL PET/MRI radiomics for intraprostatic prostate cancer detection and metastases prediction using whole-gland segmentation.

Seyed Ali Mirshahvalad, Adriano Basso Dias, Claudia Ortega, Jorge Andres Abreu Gomez, Satheesh Krishna, Nathan Perlis, Alejandro Berlin, Theodorus van der Kwast, Kartik Jhaveri, Sangeet Ghai and 3 more

2 registry-linked trialsAbstract readClinical Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in The British journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT03149861 nacompletednot on this map

Detection of Clinically Significant Prostate Cancer With 18F-DCFPyL PET/MR

TypeinterventionalSponsorUniversity Health Network, TorontoRan2017 to 2025Enrolled56ConditionsNeoplasm, ProstateArmsPET/MR-ultrasound guided Fusion biopsy, Systemic TRUS guided biopsy
NCT03535831 nacompletednot on this map

Princess Margaret Cancer Centre 18F-DCFPyL PET Registry

TypeinterventionalSponsorUniversity Health Network, TorontoRan2018 to 2025Enrolled142ConditionsCarcinoma of ProstateArms18F-DCFPyL
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. IntegratingCancers · 2025
    Article
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.

Seyed Ali MirshahvaladJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.ORCID 0000-0003-2271-9764
Adriano Basso DiasJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.
Claudia OrtegaJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.
Jorge Andres Abreu GomezJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.
Satheesh KrishnaJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.
Nathan PerlisDivision of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 2M9, Canada.
Alejandro BerlinDepartment of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, ON, M5G 2M9, Canada.
Theodorus van der KwastLaboratory Medicine Program, University Health Network, Toronto, ON, M5G 2M9, Canada.
Kartik JhaveriJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.
Sangeet GhaiJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.
Ur MetserJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.
Anna Theresa SantiagoBiostatistics Department, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 2M9, Canada.
Patrick Veit-HaibachJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, M5G 2M9, Canada.

Funding

Mount Sinai Hospital-University Health Network Academic Medical Organization Innovation NCT 03149861Toronto General Toronto Western Hospital Foundation NCT03535831
6 · The paper itself

Abstract

objectivesTo evaluate [18F]F-DCFPyL PET/MRI whole-gland-derived radiomics for detecting clinically significant (cs) prostate cancer (PCa) within the prostate gland and predicting extra-prostatic metastasis (N and M staging).

methodsIn this single-centre, retrospective study, therapy-naïve PCa patients who underwent [18F]F-DCFPyL PET/MRI were included. Whole-prostate segmentation was performed. Feature extraction from each modality was done. The selection of potential variables was made through regularized binomial logistic regression. The oversampled training data were used to train binomial logistic regression for each outcome. The estimates of the models were calculated, and the mean accuracy was reported. The trained models were assessed on the test data for comparative evaluation of performance.

resultsA total of 103 patients (mean age = 65; mean PSA = 23.4) were studied. Among them, 89 had csPCa and 20 had metastatic disease. There were five radiomics variables selected for the International Society of Urological Pathology Grade Group (ISUP GG) ≥ 2 from T2w, ADC, and PET. To detect N1, five radiomics variables were selected from the T2w and PET. For M1, four radiomics variables were selected from T2w and ADC. Regarding the performance of models for the prediction of csPCa, the imaging-based hybrid model (T2w + PET) provided the highest AUC (0.98). The performance of N1 models showed the highest AUC (0.80) for T2w + PET. To predict M1, the T2w + ADC model showed the highest AUC (0.93).

conclusionsWhole-gland PET/MRI radiomics may provide a reliable model to predict csPCa. Also, acceptable performance was reached for predicting metastatic disease in our limited population. Our findings may support the value of whole-gland radiomics for non-invasive csPCa detection and prediction of metastatic disease. ADVANCES IN KNOWLEDGE: Whole-gland PET/MRI radiomics, a less operator-dependent segmentation method, can be potentially used for treatment personalization in PCa patients.

trial registrationNCT03535831. Registered 2018; NCT03149861. Registered 2017.

Indexed as

Magnetic Resonance ImagingPositron-Emission TomographyProstatic NeoplasmsUreaAgedHumansLysineMaleMiddle AgedNeoplasm MetastasisProstateRadiomicsRadiopharmaceuticalsRetrospective Studies2-(3-(1-carboxy-5-((6-fluoropyridine-3-carbonyl)amino)pentyl)ureido)pentanedioic acidLysineRadiopharmaceuticalsUreaDCFPyLMRIpositron emission tomographyprostatePSMAradiomics

Identifiers

PMID39847533
PMCPMC12515043

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Registered trials

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.