Risk factors of negative prostate biopsies: A CHAID decision tree analysis.
[OBJECTIVES] This study aimed to perform a risk analysis of any prostate cancer (Pca) and of clinically significant prostate cancer (csPCa) in a contemporary cohort of prostatic biopsies.
- OR 1.001
APA
Allainguillaume P, Uthe-Spencker C, et al. (2026). Risk factors of negative prostate biopsies: A CHAID decision tree analysis.. BJUI compass, 7(2), e70162. https://doi.org/10.1002/bco2.70162
MLA
Allainguillaume P, et al.. "Risk factors of negative prostate biopsies: A CHAID decision tree analysis.." BJUI compass, vol. 7, no. 2, 2026, pp. e70162.
PMID
41635490
Abstract
[OBJECTIVES] This study aimed to perform a risk analysis of any prostate cancer (Pca) and of clinically significant prostate cancer (csPCa) in a contemporary cohort of prostatic biopsies.
[MATERIALS AND METHODS] We conducted a retrospective analysis of patients who underwent prostate biopsies in our centre between December 2020 and December 2022. We calculated Pca and csPCa rate (ISUP grade ≥2). Univariate and multivariate regression models were constructed to assess independent predictive factors for Pca and csPCa. We used automatic interaction detection (CHAID) for decision tree analysis.
[RESULTS] We included 255 patients in the analysis, of whom 69.8% had positive biopsies for Pca and 36.9% for csPCa. Multivariate analysis found PSA density (PSAd) (OR = 1.001) (1.000; 1.001), PIRADS score (OR = 1.393) (1.234; 1.571) as independent predictive factors of csPCa. For the detection of any PCa, CHAID analysis revealed that patients with PIRADS score ≤4 doubled the risk of negative biopsies (from 22.6% to 54.3%) when the prostate volume was >46 mL.
[CONCLUSION] For patients with a PIRADS ≤4, a large prostate volume (>46 mL) was a predictor of negative biopsies, independently of PSAd. MRI interpretation and targeting in these patients should therefore be performed with particular caution.
[MATERIALS AND METHODS] We conducted a retrospective analysis of patients who underwent prostate biopsies in our centre between December 2020 and December 2022. We calculated Pca and csPCa rate (ISUP grade ≥2). Univariate and multivariate regression models were constructed to assess independent predictive factors for Pca and csPCa. We used automatic interaction detection (CHAID) for decision tree analysis.
[RESULTS] We included 255 patients in the analysis, of whom 69.8% had positive biopsies for Pca and 36.9% for csPCa. Multivariate analysis found PSA density (PSAd) (OR = 1.001) (1.000; 1.001), PIRADS score (OR = 1.393) (1.234; 1.571) as independent predictive factors of csPCa. For the detection of any PCa, CHAID analysis revealed that patients with PIRADS score ≤4 doubled the risk of negative biopsies (from 22.6% to 54.3%) when the prostate volume was >46 mL.
[CONCLUSION] For patients with a PIRADS ≤4, a large prostate volume (>46 mL) was a predictor of negative biopsies, independently of PSAd. MRI interpretation and targeting in these patients should therefore be performed with particular caution.