MicroUS at 29 MHz provides superior spatial resolution enabling characterization of anterior prostate tissue.
A novel microUS risk score was developed and validated to predict clinically significant anterior prostate cancer.
The risk score categorizes anterior prostate tissue based on echogenicity and margin irregularity to stratify cancer risk.
Guideline-Based Recommendations
Diagnosis
Use microUS with the PRI-MUS protocol adapted for anterior prostate to identify lesions with hypoechoic appearance and irregular margins.
Combine microUS findings with mpMRI and clinical data to enhance detection of clinically significant prostate cancer.
Management
Perform targeted biopsy of anterior prostate lesions identified as high-risk on microUS to confirm diagnosis.
Use microUS imaging to guide biopsy in real-time to improve detection accuracy.
Monitoring & Follow-up
Assess microUS imaging features longitudinally to monitor anterior prostate tissue changes in patients under active surveillance.
Risks
Be aware of artifacts such as edge artifact, shadowing from gas or calcifications, and benign prostatic hyperplasia nodules that may mimic cancer.
Interpret microUS findings cautiously to avoid false positives due to common imaging artifacts.
Patient & Prescribing Data
Patients undergoing prostate biopsy for suspected significant prostate cancer with available microUS imaging
MicroUS risk scoring aids in identifying patients with anterior prostate lesions at higher risk for clinically significant cancer, informing biopsy targeting and management decisions.
Clinical Best Practices
Train clinicians on the specific microUS anterior prostate risk scoring module to standardize interpretation.
Review microUS images alongside pathology and mpMRI to correlate imaging features with histopathology.
Use a systematic scanning protocol covering the entire prostate including anterior zones with imaging depths of 3 and 5 cm.
Consider multiple readers and consensus when interpreting microUS scans to improve diagnostic reliability.
Recognize and differentiate common imaging artifacts to reduce misclassification.
by Sandy Schaer, Arnas Rakauskas, Julien Dagher, Stefano La Rosa, Jake Pensa, Wayne Brisbane, Leonard Marks, Adam Kinnaird, Robert Abouassaly, Eric Klein, Lewis Thomas, Jean-Yves Meuwly, Pamela Parker, Beat Roth, Massimo Valerio