What question did this study set out to answer?

This study aims to evaluate the diagnostic accuracy of a decision tree incorporating imaging and clinical parameters for risk stratification of extraprostatic extension in prostate cancer.

May 15, 2026Insights into Imaging0 citationsOpen Access

Imaging-based clinical decision tree enables risk stratification of extraprostatic extension before radical prostatectomy in prostate cancer patients

CHCharlie A. HammHumboldt-Universität zu Berlin TRTim RüterhenkeHumboldt-Universität zu Berlin GBGeorg L. BaumgärtnerHumboldt-Universität zu Berlin

Key Points

This study aims to evaluate the diagnostic accuracy of a decision tree incorporating imaging and clinical parameters for risk stratification of extraprostatic extension in prostate cancer.
Retrospective single-center study including 429 prostate cancer patients undergoing radical prostatectomy
Patients underwent multiparametric MRI with PI-RADS scoring and extraprostatic extension grading
Univariate and multivariate logistic regression identified predictors of extraprostatic extension.
EPE confirmed in 145 patients (33.8%)
Multivariate analysis identified rEPE grade (OR 2.64, p < 0.001) and biopsy grade group (OR 1.41, p < 0.001) as independent predictors
Decision tree assigned 48% high-risk patients (rEPE grade 3: 89% EPE risk) and 52% intermediate risk (28–45% EPE risk).

Abstract

Abstract Objectives Extraprostatic extension (EPE) significantly impacts surgical planning for prostate cancer (PCa) patients, influencing nerve-sparing surgery and neoadjuvant therapy decisions. However, Likert-scale-based radiological (r)EPE assessment lacks sufficient diagnostic accuracy for reliable clinical decision-making. Therefore, the aim was to evaluate rEPE scoring alongside clinical parameters to develop a clinically feasible decision tree for preoperative risk stratification. Methods This retrospective single-center study included 429 consecutive PCa patients undergoing radical prostatectomy between January 2012 and October 2018. All patients underwent multiparametric MRI with PI-RADS scoring and rEPE grading (grades 0–3). Clinical parameters included PSA density (PSAD) and ISUP grade group (GG) at biopsy. Univariate and multivariate logistic regression identified predictors of EPE. A clinical decision tree was developed using binary classification to stratify patients into risk groups. Results EPE was confirmed in 145 patients (33.8%). Multivariate analysis identified rEPE grade (OR 2.64, p < 0.001) and GG at biopsy (OR 1.41, p < 0.001) as independent predictors. The decision tree assigned 48% of patients to the high-risk (rEPE grade 3: 89% EPE risk) and low-risk group (rEPE < 3 + PSAD < 0.2 ng/mL² + GG < 4: 13% EPE risk), while 52% showed intermediate risk (28–45% EPE risk). Conclusions The developed decision tree combining MRI-derived rEPE grading, PSAD, and biopsy GG enables reliable identification of patients at high and low risk for EPE. This tool supports informed decision-making regarding nerve-sparing surgery and neoadjuvant therapy, potentially contributing to personalized treatment planning. Critical relevance statement Decision tree combining routine MRI-based and clinical markers reliably stratifies prostate cancer patients into high-risk and low-risk groups for EPE, supporting personalized surgical planning. Key Points EPE affects surgical planning decisions in prostate cancer patients. Combining EPE grade at MRI, PSAD, and biopsy grade improves risk stratification. The developed decision tree reliably stratified every second patient into distinct EPE-risk groups, potentially improving personalized surgical planning. Graphical Abstract

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