Comparison of prostate cancer detection rates in patients undergoing MRI/TRUS fusion prostate biopsy with two different software-based systems

Impact of Relative Volume Difference Between Magnetic Resonance Imaging and Three-dimensional Transrectal Ultrasound Segmentation on Clinically Significant Prostate Cancer Detection in Fusion Magnetic Resonance Imaging-targeted Biopsy

BACKGROUND

Segmentation of three-dimensional (3D) transrectal ultrasound (TRUS) images is known to be challenging, and the clinician often lacks a reliable and easy-to-use indicator to assess its accuracy during the fusion magnetic resonance imaging (MRI)-targeted prostate biopsy procedure.

OBJECTIVE

To assess the effect of the relative volume difference between 3D-TRUS and MRI segmentation on the outcome of a targeted biopsy.

DESIGN, SETTING, AND PARTICIPANTS

All adult males who underwent an MRI-targeted prostate biopsy for clinically suspected prostate cancer between February 2012 and July 2021 were consecutively included.

INTERVENTION

All patients underwent a fusion MRI-targeted prostate biopsy with a Koelis device.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Three-dimensional TRUS and MRI prostate volumes were calculated using 3D prostate models issued from the segmentations. The primary outcome was the relative segmentation volume difference (SVD) between transrectal ultrasound and MRI divided by the MRI volume (SVD = MRI volume - TRUS volume/MRI volume) and its correlation with clinically significant prostate cancer (eg, International Society of Urological Pathology [ISUP] ≥2) positiveness on targeted biopsy cores.

RESULTS AND LIMITATIONS

Overall, 1721 patients underwent a targeted biopsy resulting in a total of 5593 targeted cores. The median relative SVD was significantly lower in patients diagnosed with clinically significant prostate cancer than in those with ISUP 0-1: (6.7% [interquartile range {IQR} -2.7, 13.6] vs 8.0% [IQR 3.3, 16.4], p < 0.01). A multivariate regression analysis showed that a relative SVD of >10% of the MRI volume was associated with a lower detection rate of clinically significant prostate cancer (odds ratio = 0.74 [95% confidence interval: 0.55-0.98]; p = 0.038).

CONCLUSIONS

A relative SVD of >10% of the MRI segmented volume was associated with a lower detection rate of clinically significant prostate cancer on targeted biopsy cores. The relative SVD can be used as a per-procedure quality indicator of 3D-TRUS segmentation.

PATIENT SUMMARY

A discrepancy of ≥10% between segmented magnetic resonance imaging and transrectal ultrasound volume is associated with a reduced ability to detect significant prostate cancer on targeted biopsy cores.

Image-guided prostate biopsy robots: A review

At present, the incidence of prostate cancer (PCa) in men is increasing year by year. So, the early diagnosis of PCa is of great significance. Transrectal ultrasonography (TRUS)-guided biopsy is a common method for diagnosing PCa. The biopsy process is performed manually by urologists but the diagnostic rate is only 20%-30% and its reliability and accuracy can no longer meet clinical needs. The image-guided prostate biopsy robot has the advantages of a high degree of automation, does not rely on the skills and experience of operators, reduces the work intensity and operation time of urologists and so on. Capable of delivering biopsy needles to pre-defined biopsy locations with minimal needle placement errors, it makes up for the shortcomings of traditional free-hand biopsy and improves the reliability and accuracy of biopsy. The integration of medical imaging technology and the robotic system is an important means for accurate tumor location, biopsy puncture path planning and visualization. This paper mainly reviews image-guided prostate biopsy robots. According to the existing literature, guidance modalities are divided into magnetic resonance imaging (MRI), ultrasound (US) and fusion image. First, the robot structure research by different guided methods is the main line and the actuators and material research of these guided modalities is the auxiliary line to introduce and compare. Second, the robot image-guided localization technology is discussed. Finally, the image-guided prostate biopsy robot is summarized and suggestions for future development are provided.

Systematic Review and Meta-Analysis of Free-Hand and Fixed-Arm Spatial Tracking Methodologies in Software-Guided MRI-TRUS Fusion Prostate Biopsy Platforms

OBJECTIVE

To evaluate the cancer detection rate (CDR) between the 2 dominant spatial tracking methodologies in software-guided MRI-transrectal ultrasound fusion prostate biopsy (SGF-Bx) platforms: fixed-arm and free-hand.

METHODS

We conducted a systematic review and meta-analysis on published primary analyses of prospective trials and cohort studies that enrolled biopsy-naïve patients for SFG-Bx. Inclusion criteria included the use of the Prostate Imaging Reporting & Data System (PI-RADS) v2.0 or later and the targeting of lesions graded as PI-RADS 3 or higher. Random effects models were used to assess the overall prostate cancer (PCa) CDR and the clinically significant prostate cancer (csPCa) CDR for both platforms. csPCa was standardized to a definition of Gleason Grade Group 2 or higher when possible. Subgroup analysis was performed by stratifying studies into the average number of cores taken per lesion.

RESULTS

The PCa CDR was 0.674 for free-hand systems and 0.681 for fixed-arm systems. The csPCa CDR was 0.492 for free-hand systems and 0.500 for fixed-hand systems. There was no significant difference between free-hand and fixed-arm cancer detection rates for both overall PCa (P = .88) and csPCa (P = .90). Subgroup analyses revealed significant PCa CDR and csPCa CDR differences (P < .001) between free-hand and fixed-arm platforms only when 2 cores per lesion were taken, in favor of fixed-arm platforms.

CONCLUSIONS

Fixed-arm platforms performed similarly in cancer detection to free-hand platforms but show a minor benefit on fewer samples. While tracking methodology differences appear subtle, further investigation into the clinical impact of platform-specific features are warranted.