Incidental Prostate Cancer in Patients Undergoing Surgery for Benign Prostatic Hyperplasia: A Predictive Model
Abstract
The risk of finding incidental prostate cancer for patients undergoing surgery for benign prostatic hyperplasia is approximately 10%. Prostate-specific antigen density >0.1 ng/ml/cm 3 increased the risk to 23.4%, and age >72 yr further increased the risk to 35.4%. Histopathological examination of surgical specimens for benign prostatic hyperplasia (BPH) can detect incidental prostate cancer (iPCa). The aim of our study was to develop a predictive model for iPCa diagnosis for patients for whom BPH surgery is being considered. We conducted a retrospective analysis of medical files for patients who underwent BPH surgery in three academic centers between 2012 and 2022. Patients diagnosed with PCa before surgery were excluded. We calculated the global iPCa rate, and the clinically significant iPCa rate (grade group ≥2). Univariate and multivariable regression models were used to assess factors predictive of iPCa. The area under the receiver operating characteristic curve (AUC) was compared for each risk factor and for the global model. We used χ 2 automated interaction detection (CHAID) for decision tree analysis. We included 2452 patients in the analysis, of whom 247 (10.0%) had iPCa, which was clinically significant in 49/247 cases (20.2%). Multivariable analysis revealed that age and prostate-specific antigen density (PSAD) were independent predictive factors for iPCa diagnosis. The AUC for a model including age and PSAD was 0.65. CHAID analysis revealed that patients with PSAD >0.1 ng/ml/cm 3 had an iPCa risk of 23.4% (χ 2 = 52.6; p < 0.001). For those patients, age >72 yr increased the iPCa risk to 35.4% (χ 2 = 11.1, p = 0.008). Our study is mainly limited by its retrospective design. Age and PSAD were independent risk factors for iPCa diagnosis. The combination of age >72 yr and PSAD >0.1 ng/ml/cm 3 was associated with an iPCa rate of 35.4%. We performed a study to find predictors of prostate cancer for patients undergoing surgery for benign enlargement of the prostate. Our model can identify patients at risk, and diagnose their cancer before surgery. This could avoid unnecessary or harmful procedures.
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BibTeX
@article{anract2026incidental,
title = {{Incidental Prostate Cancer in Patients Undergoing Surgery for Benign Prostatic Hyperplasia: A Predictive Model}},
author = {Julien Anract and Clément Klein and Ugo Pinar and Morgan Rouprêt and Nicolas Barry Delongchamps and Grégoire Robert},
journal = {European Urology Oncology},
year = {2026},
month = oct,
doi = {10.1016/j.euo.2024.08.009},
url = {https://hal.science/hal-05796101v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Incidental Prostate Cancer in Patients Undergoing Surgery for Benign Prostatic Hyperplasia: A Predictive Model
AU - Julien Anract
AU - Clément Klein
AU - Ugo Pinar
AU - Morgan Rouprêt
AU - Nicolas Barry Delongchamps
AU - Grégoire Robert
PY - 2026
DA - 2026/10/12
JO - European Urology Oncology
DO - 10.1016/j.euo.2024.08.009
UR - https://hal.science/hal-05796101v1
AB - The risk of finding incidental prostate cancer for patients undergoing surgery for benign prostatic hyperplasia is approximately 10%. Prostate-specific antigen density >0.1 ng/ml/cm 3 increased the risk to 23.4%, and age >72 yr further increased the risk to 35.4%. Histopathological examination of surgical specimens for benign prostatic hyperplasia (BPH) can detect incidental prostate cancer (iPCa). The aim of our study was to develop a predictive model for iPCa diagnosis for patients for whom BPH surgery is being considered. We conducted a retrospective analysis of medical files for patients who underwent BPH surgery in three academic centers between 2012 and 2022. Patients diagnosed with PCa before surgery were excluded. We calculated the global iPCa rate, and the clinically significant iPCa rate (grade group ≥2). Univariate and multivariable regression models were used to assess factors predictive of iPCa. The area under the receiver operating characteristic curve (AUC) was compared for each risk factor and for the global model. We used χ 2 automated interaction detection (CHAID) for decision tree analysis. We included 2452 patients in the analysis, of whom 247 (10.0%) had iPCa, which was clinically significant in 49/247 cases (20.2%). Multivariable analysis revealed that age and prostate-specific antigen density (PSAD) were independent predictive factors for iPCa diagnosis. The AUC for a model including age and PSAD was 0.65. CHAID analysis revealed that patients with PSAD >0.1 ng/ml/cm 3 had an iPCa risk of 23.4% (χ 2 = 52.6; p < 0.001). For those patients, age >72 yr increased the iPCa risk to 35.4% (χ 2 = 11.1, p = 0.008). Our study is mainly limited by its retrospective design. Age and PSAD were independent risk factors for iPCa diagnosis. The combination of age >72 yr and PSAD >0.1 ng/ml/cm 3 was associated with an iPCa rate of 35.4%. We performed a study to find predictors of prostate cancer for patients undergoing surgery for benign enlargement of the prostate. Our model can identify patients at risk, and diagnose their cancer before surgery. This could avoid unnecessary or harmful procedures.
SN - 2588-9311
ER -