Evidence map›Paper›PMID 38578393›Full record

ArticleInternational urology and nephrology2024

Novel clinical risk calculator for improving cancer predictability of mpMRI fusion biopsy in prostates.

Anthony Bruccoliere, Vivie Tran, Naseem Helo, Abdul Awal, Stephanie Stroever, Werner T W de Riese

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Article in International urology and nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Anthony BruccoliereSchool of Medicine, Department of Urology, Texas Tech University Health Sciences Center, 3601-4 Street STOP 7260, Lubbock, TX, 79430-7260, USA.ORCID http://orcid.org/0000-0002-0727-1140
Vivie TranSchool of Medicine, Department of Urology, Texas Tech University Health Sciences Center, 3601-4 Street STOP 7260, Lubbock, TX, 79430-7260, USA.
Naseem HeloDepartment of Radiology, University Medical Center, Lubbock, TX, 79415, USA.
Abdul AwalSchool of Medicine, Texas Tech University Health Sciences Center, Clinical Research Institute, Lubbock, TX, 79415, USA.
Stephanie StroeverSchool of Medicine, Texas Tech University Health Sciences Center, Clinical Research Institute, Lubbock, TX, 79415, USA.
Werner T W de RieseSchool of Medicine, Department of Urology, Texas Tech University Health Sciences Center, 3601-4 Street STOP 7260, Lubbock, TX, 79430-7260, USA. Werner.Deriese@ttuhsc.edu.ORCID http://orcid.org/0000-0003-1963-2334

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeProstate Imaging-Reporting and Data System (PI-RADS) assists in evaluating lesions on multiparametric magnetic resonance imaging (mpMRI), but there are still ongoing efforts in improving the predictive value for the presence of clinically significant PCa (csPCa) with a Gleason grade group ≥ 2 on Fusion-Biopsy. This pilot study intends to propose an easily implementable method for augmenting predictability of csPCa for PI-RADS.

methodsA cohort of 151 consecutive patients underwent mpMRI Fusion and random US Biopsy as a result of having at least one PI-RADS lesion grade 3-5 between January 1, 2019 and December 31, 2022. A single radiologist reads all films in this study applying PI-RADS V2.

resultsOf the 151 consecutive patients, 49 had a highest lesion of PI-RADS 3, 82 had a highest lesion of PI-RADS 4, and 20 had a highest lesion of PI-RADS 5. For each respective group, 12, 42, and 18 patients had proven csPCa. Two predictive models for csPCa were created by employing a logistical regression with parameters readily available to providers. The models had an AUC of 0.8133 and 0.8206, indicating promising effective models.

conclusionPI-RADS classification has relevant predictability problems for grades 3 and 4. By applying the presented risk calculators, patients with PI-RADS 3 and 4 are better stratified, and thus, a significant number of patients can be spared biopsies with potential complications, such as infection and bleeding. The presented predictive models may be a valuable diagnostic tool, adding additional information in the clinical decision-making process for biopsies.

Indexed as

Image-Guided BiopsyMultiparametric Magnetic Resonance ImagingProstatic NeoplasmsAgedHumansMaleMiddle AgedPilot ProjectsPredictive Value of TestsRetrospective StudiesRisk AssessmentMRI-fusion biopsyProstate cancerRisk calculator

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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.