External validation and comparison of magnetic resonance imaging-based risk prediction models for prostate biopsy stratification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F24%3AE0111019" target="_blank" >RIV/00843989:_____/24:E0111019 - isvavai.cz</a>
Alternative codes found
RIV/61988987:17110/24:A2503AEA
Result on the web
<a href="https://link.springer.com/article/10.1007/s00345-024-05068-0" target="_blank" >https://link.springer.com/article/10.1007/s00345-024-05068-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00345-024-05068-0" target="_blank" >10.1007/s00345-024-05068-0</a>
Alternative languages
Result language
angličtina
Original language name
External validation and comparison of magnetic resonance imaging-based risk prediction models for prostate biopsy stratification
Original language description
Purpose: Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). Methods: A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ? 3 or ? 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ? 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. Results: Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. Conclusions: We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30217 - Urology and nephrology
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
World journal of urology
ISSN
0724-4983
e-ISSN
1433-8726
Volume of the periodical
42
Issue of the periodical within the volume
1
Country of publishing house
DE - GERMANY
Number of pages
8
Pages from-to
1-8
UT code for WoS article
001246629000001
EID of the result in the Scopus database
2-s2.0-85195960042