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Updated: Jan 10, 2026

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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
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Conventional Cognitive Freehand versus Software-Supported, Volumetric-Optimized Systematic Ultrasound-Guided Prostate
Martin H Umbehr1, Cédric Poyet2, Bertram Feil3
1Department of Urology, Municipal Hospital Zurich, Zurich, Switzerland, martin.umbehr@stadtspital.ch.
Urologia Internationalis
|November 25, 2025
Summary
Volumetric-optimized software-supported (VOT) prostate biopsy detects significant prostate cancer (PCa) more effectively than conventional cognitive freehand (CFT) biopsy. VOT offers a 1.54 times higher risk of diagnosing significant PCa, improving detection rates.
Area of Science:
- Urology
- Oncology
- Medical Imaging
Background:
- Routine systematic prostate biopsy remains crucial for detecting prostate cancer (PCa), complementing targeted biopsies.
- Conventional cognitive freehand (CFT) and volumetric-optimized software-supported (VOT) ultrasound-guided systematic biopsies are compared for PCa detection.
Purpose of the Study:
- To compare the diagnostic performance of VOT versus CFT ultrasound-guided systematic prostate biopsy in detecting significant PCa.
- To assess the relative risk of diagnosing significant PCa using VOT compared to CFT.
Main Methods:
- Retrospective analysis of 573 patients undergoing prostate biopsy between 2014 and 2023.
- Comparison of diagnostic yield and relative risk for PCa detection between CFT (n=135) and VOT (n=438) groups.
- Significant PCa defined as Gleason Score ≥7.
Main Results:
- VOT group showed a higher detection rate of significant PCa (28.5%) compared to the CFT group (18.5%).
- The relative risk for diagnosing significant PCa was significantly higher with VOT (RR 1.54; 95% CI 1.05-2.26).
- Detection rates for insignificant PCa were similar between both methods.
Conclusions:
- VOT demonstrates superior diagnostic performance over CFT for detecting significant PCa.
- Further validation in diverse cohorts is recommended to enhance the generalizability of these findings.

