Optimizing multiparametric magnetic resonance imaging-targeted biopsy and detection of clinically significant prostate cancer: the role of perilesional sampling

Background The added-value of systematic biopsy (SB) in patients undergoing magnetic resonance imaging (MRI)-targeted biopsy (TB) remains unclear and the spatial distribution of positive cores relative to the MRI lesion has been poorly studied. The aim of this study was to determine the utility of p...

Full description

Saved in:
Bibliographic Details
Published in:Prostate cancer and prostatic diseases 2023-09, Vol.26 (3), p.575-580
Main Authors: Noujeim, Jean-Paul, Belahsen, Yassir, Lefebvre, Yolene, Lemort, Marc, Deforche, Maxime, Sirtaine, Nicolas, Martin, Robin, Roumeguère, Thierry, Peltier, Alexandre, Diamand, Romain
Format: Article
Language:English
Subjects:
59/57
631/67/589/466
692/308/409
Algorithms
Biomedical and Life Sciences
Biomedicine
Biopsy
Cancer Research
Clinical significance
Cores
Density
Lesions
Machine learning
Magnetic resonance imaging
Medical imaging
Patients
Prostate cancer
Reproductive Medicine
Risk
Risk groups
Sampling
Spatial distribution
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background The added-value of systematic biopsy (SB) in patients undergoing magnetic resonance imaging (MRI)-targeted biopsy (TB) remains unclear and the spatial distribution of positive cores relative to the MRI lesion has been poorly studied. The aim of this study was to determine the utility of perilesional biopsy in detecting clinically significant prostate cancer (csPCa). Methods We enrolled 505 consecutive patients that underwent SB and TB for suspicious MRI lesions (PI-RADS score 3-5) at Jules Bordet Institute between June 2016 and January 2022. Patient-specific tridimensional prostate maps were reviewed to determine the distance between systematic cores containing csPCa and the MRI index lesion. Primary outcomes were the cancer detection rate (CDR) per patient and the cumulative cancer distribution rate of positive cores for each 5 mm interval from the MRI index lesion. The secondary outcome was the identification of risk groups for the presence of csPCa beyond a 10 mm margin using the chi-square automated interaction detector (CHAID) machine learning algorithm. Results Overall, the CDR for csPCa of TB, SB, and combined method were 32%, 25%, and 37%, respectively. While combined method detected more csPCa compared to TB (37% vs. 32%, p  
ISSN:1365-7852
1476-5608
DOI:10.1038/s41391-022-00620-8