










































Key Words Competing Interests Article Information

Prostate cancer, targeted biopsy; prostate 
biopsy, micro-ultrasound, PRI-MUS, 29MHz, 
learning curve

There was no funding for this analysis. Exact 
Imaging assisted in the collection of the data. 
Hannes Cash, Christian P. Pavlovich, Laurence 
Klotz, and Neal Shore have received speaking 
honoraria from Exact Imaging. Laurence Klotz 
has received research support from Exact 
Imaging. Neal Shore has received consulting 
fees from Exact Imaging.

Received on August 12, 2021 
Accepted on December 4, 2021 
This article has been peer reviewed.

Soc Int Urol J. 2022;3(2):62–68

DOI: 10.48083/KKVJ7280

62 SIUJ  •  Volume 3, Number 2  •  March 2022 SIUJ.ORG

This is an open access article under the terms of a license that permits non-commercial use, provided the original work is properly cited.  
© 2022 The Authors. Société Internationale d'Urologie Journal, published by the Société Internationale d'Urologie, Canada.

ORIGINAL RESEARCH

Prostate Cancer Detection by Novice  
Micro-Ultrasound Users Enrolled in a  
Training Program
Hannes Cash,1,2 Sebastian L. Hofbauer,3 Neal Shore,4 Christian P. Pavlovich,5 Stephan Bulang,6  
Martin Schostak,1 Erik Planken,7 Joris J. Jaspars,7 Ferdinand Luger,8 Laurence Klotz,9 Georg Salomon10

1Department of Urology, Otto-von-Guericke-University Magdeburg, Germany 2 PROURO, Berlin, Germany 3 Department of Urology, Charité University Medicine Berlin, 
Berlin, Germany 4 Carolina Urologic Research Center, Myrtle Beach, United States 5 The Brady Urological Institute, The Johns Hopkins School of Medicine, Baltimore 
United States 6 Diakonissenkrankenhaus, Dresden, Germany 7 Department of Urology, Admiraal de Ruyter Ziekenhuis in Goes, The Netherlands 8 Department of Urology, 
Ordensklinikum Krankenhaus der Elisabethinen, Linz, Austria 9 Sunnybrook Hospital, Toronto, Canada 10 Martini Klinik, University Hospital Hamburg, Hamburg, Germany

Abstract

Objective Micro-ultrasound is an imaging modality used to visualize and target prostate cancer during transrectal 
or transperineal biopsy. We evaluated the effectiveness of a micro-ultrasound training program and estimated the 
learning curve for prostate biopsy.

Methods A training program registry was assessed for the rate of clinically significant prostate cancer (csPCa, 
grade group ≥ 2), negative predictive value, and specificity at each stage of the program. Nine metrics of biopsy quality 
were evaluated in 4 stages for each practitioner. Non-linear fitting and logistic regression models were used to evaluate 
the time-course of these metrics over training.

Results Thirteen practitioners from 8 institutions completed stages 1 to 3 of the program, and 9 completed all 4 
stages. Over 1190 micro-ultrasound biopsy procedures were performed. Detection of csPCa increased from 40% to 
57% from stage 1 to stage 4 (P < 0.01). Stage 4 “expert” level was independently associated with higher detection of 
csPCa when correcting for overall risk factors (OR 1.95; P = 0.03). Limitations include the retrospective analysis and 
variation in biopsy protocols. 

Conclusion The micro-ultrasound training program was effective in improving biopsy quality and rate of csPCa 
detection. The presented learning curve provides an initial guide for acquiring expertise with real-time micro-
ultrasound image-guided biopsy.

Introduction

Conventional transrectal ultrasound is typically used to guide prostate biopsy. This conventional ultrasound 
guided systematic biopsy strategy results in a significant proportion of false negatives and frequent under-grading 
of cancer[1,2]. Micro-ultrasound imaging of the prostate at 29MHz provides an improvement in detail resolution 
compared with conventional ultrasound. This detailed imaging required the development of educational techniques 
for interpreting micro-ultrasound images. With appropriate training, micro-ultrasound appears to provide improved 
sensitivity compared with conventional ultrasound and allows image-guided targeted prostate biopsy[3–5]. However, 
adoption of these new techniques requires training and quality assurance.

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Expertly performed, micro-ultrasound guided biopsy 
has been demonstrated to provide risk stratification[6,7], 
improve rate of significant cancer detection[8–10], and 
aid in fusion biopsy accuracy[11,12]. Micro-ultrasound 
is therefore a promising imaging technology to reduce 
costs and improve accessibility for early detection of 
clinically significant prostate cancer (csPCa).

While some studies have demonstrated promising 
results from practitioners new to the technology[4,13], 
it is unclear how many procedures are required before 
competence is achieved. To assist in the training of new 
users, Exact Imaging (manufacturer of the ExactVu 
Micro-Ultrasound system) instituted a voluntary 
comprehensive training program in 2018. The program 
included 4 scheduled feedback stages in which prac-
titioners must score above a set value over 9 metrics of 
biopsy quality to proceed to the subsequent stage. This 
retrospective study presents the data from these feed-
back reports.

Methods 
Micro-Ultrasound Guided Biopsy 
Biopsy cases were performed transrectally using 
the ExactVu Micro-Ultrasound system and EV29L 
transducer (Exact Imaging Inc., Markham, Canada). 
All biopsy procedures were performed according to 
site-specific protocols conforming to general practice 
guidelines and by urologists experienced in prostate 
biopsy and/or fusion biopsy. These procedures were 
diverse, including various anesthesia protocols (local 
or general, or conscious sedation) and service locations 
(OR, ambulatory surgical center, or clinic). However, 
all cases included 8 to 14 systematic samples (mean 12), 
as well as micro-ultrasound-targeted biopsy samples. 
Target locations were selected based on the PRI-MUS 
protocol[4], and in some cases were informed by prior 
mpMRI imaging. Cases including mpMRI-based targets 
were completed either cognitively[11,12] or using the 
FusionVu sof tware-based MR I/micro-ultrasound 
fusion feature of the ExactVu system. We defined 
clinically significant prostate cancer (csPCa) as any 
Gleason grade > 3 + 4 = 7 cancer, a convention used by 
many other groups including in the PRECISION trial 
and Prostate Biopsy Collaborative Group (PBCG) risk 
calculator[14,15].

Training Program 
All practitioners completed a standardized training 
program including 6 online learning modules and  
1 hour of didactic instruction before undertaking their 
first live cases. An expert proctor was present for the first 
10 to 15 live cases, until the practitioner demonstrated 
confidence in image interpretation and biopsy technique, 
after which the practitioner proceeded independently. 
The curriculum was developed and implemented by 

Exact Imaging on the basis of a structured review of 
cases from the initial clinical trial of micro-ultrasound 
and expert consensus amongst proctors[5]. De-identified 
data were collected according to stage, as presented in 
Table 1. In cases where there were delays in collection 
leading to additional cases performed, the cases used 
for analysis were randomly selected to avoid bias. 
Practitioners had to complete each stage of the program 
successfully to move to the next stage. After successfully 
completing stage 4, practitioners were awarded a 
certificate of quality assurance.

Feedback Metrics 
Nine metrics associated with biopsy quality were 
selected on the basis of previous findings during 
the first micro-ultrasound based randomized trial 
(NCT02079025). These metrics are shown in Table 1. 
With 3 exceptions, each metric was assigned a point 
value based on importance and used to judge whether 
a practitioner could proceed to the next stage. The 
exceptions were for data saving, cancer detection rate, 
and anesthesia technique, used only for informational 
purposes, or in the case of data saving to recommend 
repeating the stage, because of insufficient data.

Statistical Analysis 
The Mann-Whitney U-test was used to compare 
non-parametric values, with a threshold of P < 0.05 
considered significant. A Gompertz function[16] was 
used to model learning curve through non-linear curve 
fitting. A multivariate logistic regression model was 
used to compare the detection rates at each stage with 
clinical risk factors such as age, PSA, DRE status, and 
family history. Clinical risk factors were combined 
using the validated PBCG nomogram[15] into a single 
value to prevent overfitting on this limited dataset. Only 
practitioners who had successfully completed stages 
1 to 3 of the program were included in this analysis to 
avoid bias from having different individuals at stage 
1 compared with the later stages. All computational 
modeling was performed in MATLAB (Mathworks, 
Natick, United States).

The study was approved by the local ethics commit-
tees and the authors certify that the study was performed 
in accordance with the ethical standards as laid down 
in the 1964 Declaration of Helsinki and its later amend-
ments. Informed consent was obtained from all individ-
ual participants included in the study.

Results
In total, 60 feedback reports from 13 practitioners at 8 
institutions in Germany, Austria, the Netherlands, and 
the United States between January 2018 and January 
2020 were included. These 60 reports include data from 
412 biopsy sessions, including 200 at stage 1, 69 at stage 2,  

63SIUJ.ORG SIUJ  •  Volume 3, Number 2  •  March 2022

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TABLE 1. 

Summary of training program stages and feedback 
metrics used in judging whether a practitioner is ready  
to proceed to the next stage 

Stage
Cases Before 

Analysis
Cases  

Analyzed
Effective  
Sampling

1 10 10 100%

2 10 5 50%

3 20 5 25%

4 50 5 10%

79 at stage 3, and 64 at stage 4. In total, this group of 
practitioners completed over 1190 micro-ultrasound 
g uided prostate biopsy sessions (each w it h >12 
individual biopsy samples) with a range of total cases per 
practitioner of 40 to 160. Of the feedback reports,12/60 
(20%) required the practitioner to repeat the assessment 
stage. The majority of these repeats were at stage 1 (8/12, 
67%), as presented in more detail in Table 2.

Median patient age was 70 (IQR 64 to 74) with PSA 
7.6 (IQR 5.9 to 11.7) ng/mL. A total of 95/412 cases had a 
positive DRE (23%), and 89/412 (22%) had a prior biopsy. 
A pre-biopsy mpMRI was available in 134/412 cases. The 
demographics did not vary significantly between feed-
back stages. Detection of csPCa increased from 40% to 
57% from stage 1 to stage 4 (P < 0.01). The rate of csPCa 
detected is shown in Figure 1 by stage. The result of the 
multivariate analysis is presented in Table 3 and shows 
improvement in stages 3 and 4 “expert” level with odds 
ratios of 1.71 and 1.95 and P = 0.06 and 0.03, respectively. 
This model was tested using leave-one-out validation 
with an area under the curve (AUC) of 0.675. The use 
of mpMRI was not standardized between centers, with 
33 feedback reports incorporating mpMRI targeting 
(csPCa rate 44%) and 27 feedback reports not incorpo-
rating mpMRI targeting (average csPCa rate 54%). In 
order to investigate this difference in light of confound-
ing variables such as PSA and age, mpMRI was added to 
the multivariate model presented in Table 3. The multi-
variate OR was not significant (OR 1.07, P = 0.77) and 
the model AUC dropped slightly from 0.675 to 0.672 
suggesting the use of MRI did not influence the learning 
curve.

Ability to correctly identify negative cases also 
varied with stage of training, although the relationship 
appears more complex. Figure 2 shows an initial nega-
tive predictive value (NPV) of 86% in stage 1 with 11% of 
cases marked as non-suspicious or negative. The number 
of cases marked as non-suspicious increased through 
stages 2 and 3 while NPV declined. This trend reversed 
in stage 4 in which the fraction of cases marked non-sus-
picious decreased somewhat to 17% while NPV rose to 
91%. Despite these changes in false negative rate, spec-
ificity showed a simple improvement over the course of 
the program increasing from 16% to 37% (P = 0.01).

Discussion
Appropriate training and quality control are necessary 
for any diagnostic imaging technology, and have been 
instrumental in the adoption of CT colonography and 
mammography. Training and quality control have also 
been acknowledged as important in the diagnosis of 
prostate cancer through MRI. Recent expert consensus 
is that 1000 reads are necessary to be considered an 
expert in prostate MRI[17]. This educational program 

Metric Points Description

Data saving N/A

Quantity of data provided for analysis. 
Should include at least 1 sweep 
through the prostate, and cine loops 
showing each biopsy location

Image quality 4

Includes overall gain, TGC, and 
contrast settings, as well as presence 
of artifacts due to transducer 
preparation (air bubbles, etc.)

Systematic 
spacing

2
Lateral and axial spacing of 
systematic samples to ensure  
good coverage 

PRI-MUS 
target 
identification

10
Number of serious (PRI-MUS 4/5) 
lesions not sampled or annotated

Apical horn 
technique

4

Distance of apical samples from 
capsule of the prostate. This is meant 
to ensure that the apical horn tissue is 
correctly sampled

Sample core 
length

2
Mean length in millimeters. Small 
values may indicate more pressure is 
required for tissue compression

Targeted 
sampling

2

All annotated lesions should be 
correctly sampled with clear 
visualization of needle traversing 
lesion

Cancer 
detection  
rate

N/A
Rate of all cancer and significant  
(GG >1) cancer compared to validated 
clinical risk calculators 

Anesthesia 
technique

N/A
Presence of hematoma or other 
artifact causing significant image 
degradation over course of procedure

Stage 1 begins after the practitioner has been certified as independent 
by the proctor and includes an analysis of the first 10 cases completed. 
Upon successful completion, the user completes an additional 10 
independent cases of which 5 are analyzed. This process continues 
until successful completion of stage 4 (5/50 cases) which can occur 
after as little as 90 total cases.

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analysis demonstrates that a formal training and quality 
control program was effective in increasing the ability to 
detect clinically significant cancer by 17%. Optimizing 
the negative predictive value appears to be a more 
prolonged process, with the number of false negatives 
increasing through stage 3 of the program. However, 
this effect appears transient with higher values in stage 4 
and a steady increase in specificity throughout.

The improvement occurred over the duration of the 
feedback program, which was established at 90 cases. 

The ability to consistently evaluate suspicious lesions 
according to the PRI-MUS protocol, with an impact on 
the PCa detection rate, is important to ensure adequate 
biopsy quality performance of the new micro-ultra-
sound system. The learning curve and reproducibility of 
the evaluation of the prostate using micro-ultrasound is 
the basis for the adoption of this new technology and the 
potential practitioner acceptance.

Of note, there is a large body of literature about the 
importance of training and feedback during interpre-
tation of mpMRI. Akin et al. demonstrated an increase 
in AUC from 0.52 to 0.66 for identifying lesions in the 
peripheral zone using mpMRI after didactic lectures[18]. 
Similarly, Rosenkrantz et al. demonstrated that with 
feedback accuracy interpreting prostate MRI using 
PI-RADS v1 improved from 58.1% to 77.4% over 124 
examinations[19]. These studies both report reader accu-
racy rather than detection rate, which complicates any 
comparison with the work described here. Meng et al. 

TABLE 2. 

Number of feedback sessions completed at each stage, with failure rate (required to repeat stage) and rate of 
significant cancer detected 

Stage
Number of feedback 

sessions (unique  
practitioners)

Number required 
to repeat stage 

(%)
Age PSA

DRE 
n (%)

Previous 
biopsy 
n (%)

Rate of  
CSPCA

1 21 (13) 8 (38)
69 

(62–73)
7.6 

(5.8–10.4)
43 (22) 42 (21) 40%±3%

2 13 (13) 0 (0)
70 

(68–75)
7.6 

(6.2–13.1)
20 (29) 15 (22) 45%±7%

3 15 (13) 2 (13)
70 

(63–76)
7.6 

(5.6–13.0)
15 (19) 12 (15) 58%±5%

4 11 (9) 2 (18)
71 

(66–74)
8.8 

(6.6–13.2)
17 (27) 20 (31) 57%±4%

Rate of csPCa is presented as mean ± standard error of the mean.

TABLE 3. 

Multivariate logistic regression model results 
accounting for patient risk levels 

Variable OR P -value

Stage 1 Reference N/A

Stage 2 0.835 0.551

Stage 3 1.714 0.057

Stage 4 1.953 0.029

PBCG Risk Score 23.987 <0.001

Stages 3 and 4 were associated with increased odds of detecting 
clinically significant cancer, with stage 4 achieving statistical 
significance at P = 0.03.

Ra
te

 o
f c

sP
Ca

Stage

0.7

0.65

0.6

0.5

0.45

0.4

0.35

0.3
1 2 3 4

0.55

Blue error bars represent the standard error of the mean. Black dashed 
line is the Gompertz function fit with 95% CI shown in magenta. The fit 
indicates the most likely learning curve with clear improvement by stage 
outside of the 95% CI. The lack of a plateau suggests further improvement 
may be possible. 

FIGURE 1.  
Rate of csPCa detected by feedback program stage 
 

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demonstrate a 26% improvement in targeted detection 
rates over a 4-year period with mpMRI fusion biopsy, 
which more closely aligns with the results presented 
here, while Calio et al. demonstrated an 11.6% improve-
ment in fusion biopsy detection rate over a 9-year 
period[20,21]. However, in both these cases the popula-
tion is limited to men with suspicious mpMRIs. These 
data reflect an improvement in positive predictive value 
rather than overall detection rate in a general biopsy 
population.

Similar studies of fusion biopsy accuracy have 
focused on accuracy in reaching a prespecified target 
position, showing improvement within the first 98 
cases for a robotic fusion system[22]. Other studies have 
investigated complication rates and differences in use 
between junior and senior operators without reference 
to number of cases performed on a particular system, 
which was not investigated here[23].

The primary limitation of this study is the retrospec-
tive nature. As data were compiled from the feedback 
program registry, the biopsy procedures themselves were 
not standardized and differences in biopsy technique 
may be associated with different learning curves. In 
particular, use of mpMRI was not standardized between 
centers. To examine this effect, we added mpMRI to the 
multivariate model presented in Table 3; however, the 
multivariate OR was not significant, suggesting the use 

of MRI did not influence the learning curve. Compli-
cations during biopsy were not recorded as part of this 
study, but complication rates for the procedure are 
generally low and unrelated to the guidance/imaging 
device used[5]. Further, the broad cross-section of physi-
cians evaluated included those with prior ultrasound 
fellowship training who may be expected to have devel-
oped expertise in the technique earlier than those with-
out this training.

Conclusion
A formal training and feedback program for micro-
ultrasound establishes a standardized scan, and 
reporting with micro-ultrasound and adds significant 
value in improving clinically significant cancer detection 
rates. The learning curve presented here suggests expert 
sensitivity is achieved within the first 20 to 40 cases, 
while expert specificity generally takes 40 to 90 cases to 
develop. This provides a guide for practitioners who are 
interested in acquiring expertise with real-time micro-
ultrasound image-guided biopsy. Educational program 
enhancements and prospective registries are currently 
evolving.

Acknowledgments
The aut hors wou ld like to ack nowledge Dr Brian 
Wodlinger for his help with the registry data and  
Dr Sangeet Ghai for editorial review of the manuscript.

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

0.9

0.7

0.4

0.8

0.6

0.5

1 2 3 4

1

N
eg

at
iv

e 
Pr

ed
ic

tiv
e 

Va
lu

e

Fr
ac

tio
n 

of
 N

on
-S

us
pi

ci
ou

s 
Ca

se
s

Stage

Initially, practitioners appear hesitant to label a case non-suspicious; 
however, NPV is high. A larger percentage of cases were marked non-
suspicious in stages 2 and 3, with an observed decline in NPV. This trend 
appears to reverse in stage 4 with a lower number of non-suspicious cases 
but very high NPV. Error bars denote the standard error of the mean.

FIGURE 2.

Number of cases marked non-suspicious on  
micro-ultrasound and negative predictive value  
(NPV) by stage of feedback program 

0.6

0.7

0.4

0.3

0.2

0.1

0
1 2 3 4

Sp
ec
i�
ci
ty

Stage

Blue error bars represent the standard error of the mean. Black dashed 
line is the Gompertz function fit with 95% CI shown in magenta. The 
fit shows a good match to the data points with clear increasing trend 
with stage, although no plateau is reached suggesting possible further 
improvement.

FIGURE 3. 

Specificity to recognize benign cases by feedback 
program stage 

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68 SIUJ  •  Volume 3, Number 2  •  March 2022 SIUJ.ORG

 ORIGINAL RESEARCH

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