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172
D. Chan and K. Nightingale
signicantly higher Young’s modulus of prostate
cancer compared to benign tissue and an AUC of
0.94 [18]. Additional studies have similarly
shown the ability of shear wave elastography to
accurately identify and visualize prostate cancer:
Morris et al. (2021) reported a sensitivity and
specicity of 81% and 82%, respectively [16],
and a systematic review by Anbarasan et al.
(2021) found that shear wave elastography is
more sensitive to clinically signicant prostate
cancer (dened as having a Gleason score > 6
with a tumor burden ≥3mm) [19].
One limitation of shear wave elastography in
the prostate is the effect of transducer compression; because all tissues exhibit a nonlinear stress/
strain response, the shear wave speed (and shear
modulus) increases with increasing compression,
which can confound the quantitative metrics
derived from this technique. Normalizing the
measured shear wave speed by computing its
ratio to the shear wave speed of a non-cancerous
region in the same prostate may mitigate these
effects of compression [16].
Acoustic Radiation Force Impulse (ARFI) Imaging
Acoustic radiation force impulse (ARFI) imaging is a qualitative elasticity imaging technique
that provides information about the relative
stiffness of tissue. ARFI imaging, like shear
wave elastography, uses a focused ultrasound
excitation to displace the tissue by several
microns, but instead of tracking the outgoing
shear waves, it uses ultrasound to track tissue
displacement magnitudes within the region of
the focused excitation. Stiffer tissues typically
have lower displacement magnitudes compared
to softer tissues [20]. The displacement magnitudes in ARFI imaging depend on the conguration of the focused ultrasound excitation as well
as the underlying stiffness of the prostate, and
so, ARFI imaging is used to assess only the relative tissue stiffness [21] and to identify prostate
cancers as regions with decreased displacement
(i.e., increased stiffness) compared to healthy
tissue.
Palmeri et al. (2016) reported results from a
clinical study of ARFI imaging of patients expecting radical prostatectomy [10]. They demonstrated
that ARFI imaging accurately identied 71% of
clinically signicant prostate cancer, including
79% of cancer in the posterior prostate. Among
the lesions identied by ARFI, 79% corresponded
to clinically signicant prostate cancer. Limitations
of ARFI imaging that were encountered in that
study included difculty imaging patients who
had signicant atrophy in the prostate and a lower
signal-to-noise ratio in the anterior prostate [10].
Because both shear wave elastography and
ARFI imaging involve using a focused ultrasound excitation to induce tissue displacements,
they can be performed simultaneously with a
combined sequence, in which a focused ultrasound excitation is delivered, and then, tissue
motion is tracked both within and outside of the
region of excitation [22]. The two imaging techniques are complementary, and while shear wave
elastography provides quantitative stiffness
information, ARFI imaging typically has higher
resolution and better signal-to-noise ratio, particularly at greater depths [4].
Quantitative Ultrasound
Quantitative ultrasound (QUS) techniques
involve analyzing the radiofrequency data of the
backscattered ultrasound signal to characterize
the underlying scatterers in the tissue. Several
parameters, including mid-band t, spectral
slope, and spectral intercept, can be extracted by
analyzing the frequency dependence of the backscattered signal [23]. To properly normalize the
power spectrum when computing the backscatter
coefcient, a calibration step must be performed
using a reference phantom with known material
properties [24, 25].
In 2008, Feleppa reported that QUS tech-
niques, combined with articial neural networks
for classication, could distinguish prostate cancer from healthy tissue [26]. Subsequent studies
have similarly demonstrated the efcacy of QUS
techniques for identifying and imaging prostate
cancer [24, 27].
ab
16 Multiparametric Ultrasound forProstate Imaging andTargeting
173
Doppler andContrast-Enhanced
Ultrasound Imaging
Ultrasound color Doppler imaging is a noninvasive technique to assess blood ow within
tissue [28, 29]. This imaging method relies on the
Doppler effect, which is a change in the frequency of sound waves as they bounce off moving
objects, such as blood cells. By measuring and
mapping the speed and direction of blood ow,
ultrasound color Doppler can provide information about the vascularity of prostate tissue [30].
Prostate cancer is typically associated with
increased microvessel density [31].
Shigeno et al. (2001) found that while color
Doppler was useful when combined with B-mode
ultrasound, it had lower sensitivity to prostate
cancer when compared to B-mode or magnetic
resonance imaging [30]. Figure 16.3, adapted
from Carpagnano et al. (2021), shows a grayscale B-mode image and a color Doppler image
of the prostate, with cancer indicated by the red
arrows [32].
Using contrast agents can enhance the ability
to visualize the vasculature of the prostate.
Contrast-enhanced ultrasound (CEUS) imaging
of the prostate involves injecting microbubble
contrast agents into the bloodstream, insonifying
them with a transmitted ultrasound signal, and
detecting the harmonic frequencies in the
reected signal. Because prostate cancer is associated with an increase in microvascular density,
lesions can be identied based on an increase in
the reected microbubble signal [33]. Figure16.4,
Fig. 16.3 (a) B-mode and (b) color Doppler images of
the prostate. The red arrows indicate prostate cancer in the
peripheral zone. (Adapted from [32]: Carpagnano etal.
a
Fig. 16.4 (a) B-mode and (b) contrast-enhanced ultra-
sound images of the prostate. The red box indicates
histology- conrmed prostate cancer, and the yellow box
indicates a region of healthy tissue on the contralateral
side of the prostate. (Adapted from [34]: Huang et al.
(2021), “Prostate Cancer Ultrasound: Is Still a Valid
Tool?” published in Current Radiology Reports under a
Creative Commons Attribution 4.0 International license)
b
(2016), “Contrast-enhanced transrectal ultrasound for the
prediction of prostate cancer aggressiveness: The role of
normal peripheral zone time-intensity curves,” published
in Scientic Reports under a Creative Commons
Attribution 4.0 International license)
174
D. Chan and K. Nightingale
adapted from Huang etal. (2016), shows a grayscale B-mode image and a CEUS image of the
prostate, with a lesion indicated by the red box
[34].
Micro-Ultrasound
In ultrasound imaging, the frequency of the ultrasound waves affects both the spatial resolution
and the depth of penetration. Higher frequency
enables better spatial resolution, but the waves
are not able to penetrate as deep due to frequencydependent attenuation [35]. An imaging frequency of around 8 to 10 MHz has been
conventionally used for clinical prostate imaging
[36].
Micro-ultrasound imaging of the prostate uses
a higher imaging frequency for enhanced spatial
resolution [37]. In 2014, Pavlovich etal. reported
results of using a 21MHz system for transrectal
ultrasound imaging, which enabled improved
visualization of prostate lesions [38]. Since then,
29MHz transducers have been used to image the
prostate to provide a spatial resolution of 70μm
[39]. A grading system called Prostate Risk
Identication using Micro-Ultrasound (PRIMUS) has been developed to classify microultrasound images based on cancer risk, similar
to the PI-RADS system used with multiparamet-
ric magnetic resonance imaging (mpMRI) [40].
Figure 16.5, reproduced from Dias and Ghai
(2023), depicts a 29 MHz micro-ultrasound
image of the prostate, with the arrowheads indicating a PRI-MUS 4 lesion and the arrows indicating a biopsy needle [41].
The high imaging frequency used in microultrasound limits its penetration depth, which can
hamper the identication and visualization of
lesions in the anterior prostate and transition
zone, as well as in large prostates [42, 43].
Multiparametric Ultrasound
Multiparametric ultrasound (mpUS) can improve
prostate cancer detection and visualization by
combining information from different imaging
modalities. Recent work in the eld has used
various combinations of ultrasound modalities.
Figure 16.6, reproduced by Chen et al. (2022),
shows an example of mpUS prostate imaging
with B-mode, color Doppler, strain elastography,
and CEUS, along with corresponding mpMRI
and histopathology images [44].
Mannaerts etal. (2019) conducted a study in
which several modalities (B-mode, shear wave
elastography, and CEUS) were assessed individually for the likelihood of prostate cancer in each
subject. Then, a logistic linear mixed model was
Fig. 16.5 Micro-ultrasound image of the prostate; the
arrowheads indicate a PRI-MUS 4 lesion in the apical
peripheral zone and the arrows indicate a biopsy needle.
(Reproduced from [41]: Dias and Ghai (2023), “Micro-
Ultrasound: Current Role in Prostate Cancer Diagnosis
and Future Possibilities,” published in Cancers under a
Creative Commons Attribution 4.0 license)
16 Multiparametric Ultrasound forProstate Imaging andTargeting
175
a
e
Fig. 16.6 Multiparametric ultrasound imaging of the
prostate, showing (a) grayscale B-mode, (b) color
Doppler, (c) strain elastography, and (d) contrastenhanced ultrasound. The mpMRI sub-gures (e), (f), and
(g) show T2-weighted MRI, diffusion-weighted imaging,
and apparent diffusion coefcient, respectively. The white
arrow in each image indicates a Gleason 5+4 cancer. A
b
f
c
g
histopathological image is shown in sub-gure (h).
(Reproduced from [44]: Chen et al. (2022),
“Multiparametric transrectal ultrasound for the diagnosis
of peripheral zone prostate cancer and clinically signicant prostate cancer: novel scoring systems,” published in
BMC Urology under a Creative Commons Attribution 4.0
International license)
d
h
used to predict the overall likelihood of prostate
cancer. They found that prostate cancer detection
was signicantly improved by combining results
from the different modalities [45]. Wildeboer
etal. (2020) combined the same input modalities
using a random forest algorithm to enhance classier performance for prostate cancer localization [46].
Morris etal. (2020) used a linear support vector machine (SVM) to combine ARFI, shear
wave elastography, QUS, and B-mode prostate
data acquired in vivo [4]. The linear SVM was
trained on manually segmented invivo data and
outperformed other classier approaches (linear
discriminant analysis, decision trees, and random
forests). The study demonstrated that this method
for generating mpUS image volumes enhanced
lesion visibility and increased the contrast-tonoise ratio (CNR) of prostate cancer compared to
each of the individual modalities [4].
This approach was further developed by using
a deep neural network (DNN) to generate a multi-
parameter image volume of the prostate [47, 48].
Using the nonlinear DNN, whose inputs were the
input modalities (ARFI, shear wave elastography,
QUS, and B-mode), signicantly improved the
CNR of prostate cancer compared to the linear
SVM method. As with previous studies, these
ndings also demonstrated the value of having
complementary (stiffness-based and
echogenicity- based) imaging modalities as inputs
to the multiparametric model, allowing the neural
network to combine different characteristics of
prostate cancer [47, 48].
Zhang et al. (2019) studied mpUS using
B-mode, color Doppler, shear wave elastography,
and CEUS imaging, nding higher sensitivity
and accuracy for prostate cancer detection compared to mpMRI [49]. These same input modalities were investigated in a prospective multicenter
study by Grey etal. (2022) (CADMUS trial) [50].
They reported that mpUS detected clinically signicant prostate cancer in 26% of patients who
underwent biopsy, compared to 30% for mpMRI,
176
D. Chan and K. Nightingale
with each imaging technique detecting clinically
signicant cancer that the other missed. The
authors concluded that mpUS could be a feasible
alternative or addition to mpMRI in the diagnosis
of prostate cancer [50].
Conclusions
Ultrasound imaging is a valuable tool for visualizing prostate cancer. It is fast, safe, portable, and
inexpensive compared to other imaging
approaches and can provide imaging guidance in
both the transrectal and transperineal congurations. In addition to providing imaging guidance
during biopsies for the detection of prostate cancer, ultrasound imaging applications have been
developed to guide focal therapy procedures,
monitor responses to treatment, and longitudinally monitor disease changes in cases where
watchful waiting is indicated.
In addition to conventional grayscale B-mode
ultrasound, a plethora of ultrasound-based imaging modalities have been developed to capture different characteristics of prostate cancer: elasticity
imaging techniques (strain elastography, ARFI
imaging, and shear wave elastography) for changes
in stiffness, QUS for characterizing tissue scattering properties, color Doppler and CEUS for
changes in microvasculature and blood ow, and
micro-ultrasound for high-resolution imaging.
Combining information from different modalities
with mpUS improves imaging performance and
enhances prostate cancer detection and visualization. As researchers innovate new and improved
ways to image prostate cancer with ultrasound,
mpUS continues to be a promising approach for
providing image guidance in the prostate.
References
1. Tanoue H, Hagiwara Y, Kobayashi K, Saijo
Y.Echogenicity in transrectal ultrasound is determined
by sound speed of prostate tissue components. Annu
Int Conf IEEE Eng Med Biol Soc. 2012;2012:460–3.
2. Hwang SI, Lee HJ. The future perspectives in transrectal prostate ultrasound guided biopsy. Prostate Int.
2014;2(4):153–60.
3. Postema A, Mischi M, de la Rosette J, Wijkstra
H. Multiparametric ultrasound in the detection of
prostate cancer: a systematic review. World J Urol.
2015;33(11):1651–9.
4. Morris DC, Chan DY, Lye TH, Chen H, Palmeri ML,
Polascik TJ, Foo WC, Huang J, Mamou J, Nightingale
KR.Multiparametric ultrasound for targeting prostate
cancer: combining ARFI, SWEI, QUS and B-Mode.
Ultrasound Med Biol. 2020;46(12):3426–39.
5. Brock M, Eggert T, Palisaar RJ, Roghmann F, Braun
K, Löppenberg B, Sommerer F, Noldus J, von Bodman
C.Multiparametric ultrasound of the prostate: adding
contrast enhanced ultrasound to real-time elastography to detect histopathologically conrmed cancer. J
Urol. 2013;189(1):93–8.
6. Kaneko M, Lenon MSL, Ramacciotti LS, Medina
LG, Sayegh AS, Riva AL, Perez LC, Ghorei A,
Lizana M, Jadvar DS, Lebastchi AH, Cacciamani
GE, Abreu AL. Multiparametric ultrasound of prostate: role in prostate cancer diagnosis. Ther Adv Urol.
2022;14:175628722211456.
7. Grey A, Ahmed HU. Multiparametric ultrasound
in the diagnosis of prostate cancer. Curr Opin Urol.
2016;26(1):114–9.
8. Mannaerts CK, Wildeboer RR, Postema AW,
Hagemann J, Budäus L, Tilki D, Mischi M, Wijkstra
H, Salomon G.Multiparametric ultrasound: evaluation of greyscale, shear wave elastography and
contrast-enhanced ultrasound for prostate cancer detection and localization in correlation to
radical prostatectomy specimens. BMC Urol.
2018;18(1):98.
9. Wells PNT, Liang HD.Medical ultrasound: imaging
of soft tissue strain and elasticity. J R Soc Interface.
2011;8(64):1521–49.
10. Palmeri ML, Glass TJ, Miller ZA, Rosenzweig
SJ, Buck A, Polascik TJ, Gupta RT, Brown AF,
Madden J, Nightingale KR. Identifying clinically
signicant prostate cancers using 3-D invivo acoustic radiation force impulse imaging with wholemount histology validation. Ultrasound Med Biol.
2016;42(6):1251–62.
11. Kanagaraju V, Ashlyin PVK, Elango N, Devanand
B.Role of transrectal ultrasound elastography in the
diagnosis of prostate carcinoma. J Med Ultrasound.
2020;28(3):173–8.
12. Sigrist RMS, Liau J, Kaffas AE, Chammas MC,
Willmann JK. Ultrasound elastography: review of
techniques and clinical applications. Theranostics.
2017;7(5):1303–29.
13. Yoo JW, Koo KC, Chung BH, Lee KS.Role of the
elastography strain ratio using transrectal ultrasonography in the diagnosis of prostate cancer
and clinically signicant prostate cancer. Sci Rep.
2022;12(1):21171.
14. Dietrich CF, Barr RG, Farrokh A, Dighe M, Hocke
M, Jenssen C, Dong Y, Saftoiu A, Havre RF. Strain
elastography—how to do it? Ultrasound Int Open.
2017;3(4):E137–49.
16 Multiparametric Ultrasound forProstate Imaging andTargeting
177
15. Nowicki A, Dobruch-Sobczak K. Introduction
to ultrasound elastography. J Ultrason.
2016;16(65):113–24.
16. Morris DC, Chan DY, Palmeri ML, Polascik TJ, Foo
WC, Nightingale KR.Prostate cancer detection using
3-D shear wave elasticity imaging. Ultrasound Med
Biol. 2021;47(7):1670–80.
17. Secasan CC, Onchis D, Bardan R, Cumpanas A,
Novacescu D, Botoca C, Dema A, Sporea I.Articial
intelligence system for predicting prostate cancer
lesions from shear wave elastography measurements.
Curr Oncol. 2022;29(6):4212–23.
18. Yang Y, Zhao X, Zhao X, Shi J, Huang Y. Value of
shear wave elastography for diagnosis of primary
prostate cancer: a systematic review and metaanalysis. Med Ultrason. 2019;21(4):382–8.
19. Anbarasan T, Wei C, Bamber JC, Barr RG, Nabi
G. Characterisation of prostate lesions using transrectal shear wave elastography (SWE) ultrasound
imaging: a systematic review. Cancers (Basel).
2021;13(1):122.
20. Doherty JR, Trahey GE, Nightingale KR, Palmeri
ML. Acoustic radiation force elasticity imaging in diagnostic ultrasound. IEEE Trans Ultrason
Ferroelectr Freq Control. 2013;60(4):685–701.
21. Nightingale K. Acoustic radiation force impulse
(ARFI) imaging: a review. Curr Med Imaging Rev.
2011;7(4):328–39.
22. Chan DY, Morris DC, Polascik TJ, Palmeri ML,
Nightingale KR. Combined ARFI and shear wave
imaging of prostate cancer: optimizing beam
sequences and parameter reconstruction approaches.
Ultrason Imaging. 2023;45(4):175–86.
23. Sharma D, Osapoetra LO, Faltyn M, Do NNA, Giles
A, Stanisz M, Sannachi L, Czarnota GJ.Quantitative
ultrasound characterization of therapy response
in prostate cancer in vivo. Am J Transl Res.
2021;13(5):4437–49.
24. Oelze ML, Mamou J. Review of quantitative ultrasound: envelope statistics and backscatter coefcient
imaging and contributions to diagnostic ultrasound.
IEEE Trans Ultrason Ferroelectr Freq Control.
2016;63(2):336–51.
25. Yao LX, Zagzebski JA, Madsen EL. Backscatter
coefcient measurements using a reference phantom
to extract depth-dependent instrumentation factors.
Ultrason Imaging. 1990;12(1):58–70.
26. Feleppa EJ. Ultrasonic tissue-type imaging of the
prostate: implications for biopsy and treatment guidance. Cancer Biomark. 2008;4(4–5):201–12.
27. Rohrbach D, Wodlinger B, Wen J, Mamou J, Feleppa
E. High-frequency quantitative ultrasound for imaging prostate cancer using a novel micro-ultrasound
scanner. Ultrasound Med Biol. 2018;44(7):1341–54.
28. Ashi K, Kirkham B, Chauhan A, Schultz SM, Brake
BJ, Sehgal CM. Quantitative colour Doppler and
greyscale ultrasound for evaluating prostate cancer.
Ultrasound. 2021;29(2):106–11.
29. Berger AP, Horninger W, Bektic J, Pelzer A, Spranger
R, Bartsch G, Frauscher F.Vascular resistance in the
prostate evaluated by colour Doppler ultrasonography: is benign prostatic hyperplasia a vascular disease? BJU Int. 2006;98(3):587–90.
30. Shigeno K, Igawa M, Shiina H, Wada H, Yoneda T.The
role of colour Doppler ultrasonography in detecting
prostate cancer. BJU Int. 2000;86(3):229–33.
31. Pallwein L, Mitterberger M, Pelzer A, Bartsch G,
Strasser H, Pinggera GM, Aigner F, Gradl J, Nedden
DZ, Frauscher F.Ultrasound of prostate cancer: recent
advances. Eur Radiol. 2008;18(4):707–15.
32. Carpagnano FA, Eusebi L, Carriero S, Giannubilo W,
Bartelli F, Guglielmi G.Prostate cancer ultrasound: is
still a valid tool? Curr Radiol Rep. 2021;9:7.
33. Halpern EJ.Contrast-enhanced ultrasound imaging of
prostate cancer. Rev Urol. 2006;8(Suppl 1):S29–37.
34. Huang H, Zhu ZQ, Zhou ZG, Chen LS, Zhao M,
Zhang Y, Li HB, Yin LP. Contrast-enhanced transrectal ultrasound for prediction of prostate cancer
aggressiveness: the role of normal peripheral zone
time-intensity curves. Sci Rep. 2016;6(1):38643.
35. Rizzatto G. Ultrasound transducers. Eur J Radiol.
1998;27(Suppl 2):S188–95.
36. Tyloch JF, Wieczorek AP.The standards of an ultrasound examination of the prostate gland. Part 1. J
Ultrason. 2016;16(67):378–90.
37. Harland N, Stenzl A. Micro-ultrasound: a way to
bring imaging for prostate cancer back to urology.
Prostate Int. 2021;9(2):61–5.
38. Pavlovich CP, Cornish TC, Mullins JK, Fradin J,
Mettee LZ, Connor JT, Reese AC, Askin FB, Luck
R, Epstein JI, Burke HB.High-resolution transrectal
ultrasound: pilot study of a novel technique for imaging clinically localized prostate cancer. Urol Oncol.
2014;32(1):34.e27–32.
39. Fusco F, Emberton M, Arcaniolo D, De Nunzio
C, Manfredi C, Creta M. Prostatic high-resolution
micro-ultrasound: an attractive step-forward in the
management of prostate cancer patients. Prostate
Cancer Prostatic Dis. 2023;26(3):521–2.
40. Ghai S, Eure G, Fradet V, Hyndman ME, McGrath
T, Wodlinger B, Pavlovich CP. Assessing cancer
risk on novel 29 MHz micro-ultrasound images
of the prostate: creation of the micro-ultrasound
protocol for prostate risk identication. J Urol.
2016;196(2):562–9.
41. Dias AB, Ghai S.Micro-ultrasound: current role in
prostate cancer diagnosis and future possibilities.
Cancers (Basel). 2023;15(4):1280.
42. Dias AB, O’Brien C, Correas JM, Ghai
S. Multiparametric ultrasound and micro-ultrasound
in prostate cancer: a comprehensive review. Br J
Radiol. 2022;95(1131):20210633.
43. Avolio PP, Lughezzani G, Fasulo V, Maffei D,
Sanchez-Salas R, Paciotti M, Saitta C, De Carne
F, Saita A, Hurle R, Lazzeri M, Guazzoni G, Buf
NM, Casale P.Assessing the role of high-resolution
microultrasound among Naïve patients with negative
multiparametric magnetic resonance imaging and
a persistently high suspicion of prostate cancer. Eur
Urol Open Sci. 2023;47:73–9.
178
D. Chan and K. Nightingale
44. Chen T, Wang F, Chen H, Wang M, Liu P, Liu S, Zhou
Y, Ma Q. Multiparametric transrectal ultrasound for
the diagnosis of peripheral zone prostate cancer and
clinically signicant prostate cancer: novel scoring
systems. BMC Urol. 2022;22(1):64.
45. Mannaerts CK, Wildeboer RR, Remmers S, van
Kollenburg RAA, Kajtazovic A, Hagemann J,
Postema AW, van Sloun RJG, Roobol MJ, Tilki D,
Mischi M, Wijkstra H, Salomon G.Multiparametric
ultrasound for prostate cancer detection and
localization: correlation of B-mode, shear wave
elastography and contrast enhanced ultrasound
with radical prostatectomy specimens. J Urol.
2019;202(6):1166–73.
46. Wildeboer RR, Mannaerts CK, van Sloun RJG,
Budäus L, Tilki D, Wijkstra H, Salomon G, Mischi
M. Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics. Eur
Radiol. 2019;30(2):806–15.
47. Chan DYX.A 3-D multiparametric ultrasound elasticity imaging system for targeted prostate biopsy guid-
ance [dissertation]. Durham, NC: Duke University;
2023.
48. Chan DY, Morris DC, Lye T, Polascik TJ, Palmeri
ML, Mamou J, Nigthingale KR.Deep neural network
for multiparametric ultrasound imaging of prostate
cancer. Proc IEEE Int Ultrason Symp. 2021:1–4.
49. Zhang M, Tang J, Luo Y, Wang Y, Wu M, Memmott
B, Gao J.Diagnostic performance of multiparametric
transrectal ultrasound inlocalized prostate cancer: a
comparative study with magnetic resonance imaging.
J Ultrasound Med. 2018;38(7):1823–30.
50. Grey ADR, Scott R, Shah B, Acher P, Liyanage S,
Pavlou M, Omar R, Chinegwundoh F, Patki P, Shah
TT, Hamid S, Ghei M, Gilbert K, Campbell D, BrewGraves C, Arumainayagam N, Chapman A, McLeavy
L, Karatziou A, Alsaadi Z, Collins T, Freeman A,
Eldred-Evans D, Bertoncelli-Tanaka M, Tam H,
Ramachandran N, Madaan S, Winkler M, Arya M,
Emberton M, Ahmed HU.Multiparametric ultrasound
versus multiparametric MRI to diagnose prostate cancer
(CADMUS): a prospective, multicentre, paired-cohort,
conrmatory study. Lancet Oncol. 2022;23(3):428–38.
Staging Imaging forFocal Therapy
ofProstate Cancer
MichaelB.Rothberg
17
Multi-Parametric Magnetic Resonance Imaging
Multi-parametric magnetic resonance imaging of
the prostate, including T2-weighted (T2W),
diffusion- weighted (DWI), and dynamic contrastenhanced (DCE) sequences, provides both anatomical and functional characterization of
intraprostatic lesions, yielding critical information about their number, size, and anatomic location. The ability to localize and subsequently
target lesions concerning for clinically signicant
prostate cancer (csPCa) on MRI/US-fusion
biopsy has become an essential component of the
localized prostate cancer diagnostic pathway.
Multiple studies of high levels of evidence have
proven the clinical utility and improved diagnostic accuracy of mpMRI, demonstrating increased
rates of detection of csPCa and fewer diagnoses
of indolent, low-risk disease when performing
MRI/US-fusion targeted prostate biopsy compared to conventional TRUS-guided prostate
biopsy [1–4].
M. B. Rothberg (*)
Division of Urologic Oncology, Department of
Urology, Duke Cancer Institute Center for Prostate
and Urologic Cancers, Duke University School of
Medicine, Durham, NC, USA
e-mail: michael.rothberg@duke.edu
Identication oftheIndex Lesion
The basis of focal therapy as a modality to treat a
highly select population of patients with visible
lesions on mpMRI and concordant biopsy pathology originates from the concept of clonal origin
of prostate cancer metastasis [5]. Therefore,
imaging characterization of the so-called index
lesion, specically the tumor with the largest size
and highest Gleason grade, would identify the
source of greatest potential for biological aggressiveness with the objective of treatment to mitigate the risk for development of metastatic
disease [6]. Proper execution of a focal therapy
treatment regimen is dependent on, among many
other variables, a reliable means by which to
identify the index lesion. Using mpMRI with
MRI/US-fusion-targeted prostate biopsy for
diagnosis and correlating ndings with radical
prostatectomy specimens, Radtke et al. report
mpMRI detected 92% of pathologically conrmed index lesions; moreover, combined fusiontargeted biopsy and saturation biopsy detected
96% of index lesions [7]. Additionally, Russo
et al. report similar ndings whereby mpMRI
identied 93% of index lesions harboring csPCa
on nal whole-mount pathology [8]. While
mpMRI was shown to be highly sensitive for
identifying csPCa index lesions, they report suboptimal characterization of clinically insignicant non-index satellite lesions, particularly those
of smaller volume (<0.5mL) and lower Gleason
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
T. J. Polascik et al. (eds.), Imaging and Focal Therapy of Early Prostate Cancer,
https://doi.org/10.1007/978-3-031-66754-1_17
179
180
M. B. Rothberg
grade (GG1) [8]. Historically, index lesions have
been shown to contain an average of 80% of total
disease volume; moreover, non-index satellite
lesions have been characterized as small in volume and highly unlikely to demonstrate locally
advanced pathology [9, 10]. Consistent with the
aforementioned clonal origin of prostate cancer
metastasis, such investigations highlight the ability of mpMRI to reliably identify the index lesion
to not only determine a patient’s candidacy for
focal therapy but also to guide subsequent treatment planning.
Multifocality andLesion Visibility
The inability of mpMRI to identify the totality of
prostate cancer disease burden presents unique
clinical challenges when considering patient candidacy for focal therapy. Clinically signicant
MR-invisible disease, specically csPCa identied on systematic biopsy and not associated with
lesions characterized on mpMRI, have been
reported in approximately 10% of patients [11].
In fact, if targeted biopsy of MR-visible lesions
was exclusively performed and systematic biopsies were omitted, approximately 8.8% of csPCa
would likely be undiagnosed [12]. Such false
negatives have been attributed to lesions that
were either unidentied, mischaracterized as
benign, or underestimated in size on mpMRI [13,
14]. Therefore, when clinically signicant
MR-invisible disease is diagnosed on systematic
biopsy, or in instances where MR-visible lesions
return as benign on targeted biopsy, several concerns arise regarding selection of an ablation
template, denition of treatment margins, and
whether such patients remain suitable candidates
for focal therapy. Several studies have suggested
that while some csPCa may not be detected as a
discrete, visible lesion on mpMRI, the vast
majority of these cancers reside within close
proximity to an MR-visible index lesion. Feuer
etal. report that “missed” csPCa was located ipsilateral to MR-visible lesions in 81% of instances
and were more likely to be small volume and
low-risk [15]. Furthermore, in performing an
analysis of targeted biopsies of MR-visible
lesions with perilesional sampling, Brisbane
etal. report that 90% of csPCa was located within
10mm of an MR-visible lesion, and only 6.1% of
patients with csPCa harbored cancer beyond that
distance [16]. Moreover, as the grade of
MR-visible lesions increased, the distance from
the visible region of interest, whereby 90% of
csPCa would be contained, decreased [16].
Emerging evidence suggests prostate cancers
associated with visible lesions on mpMRI may
have increased biological aggressiveness compared to MR-invisible lesions. Stavrinides etal.
report on the natural history of GG1 and GG2
prostate cancers based on their association with
an MR-visible or MR-invisible lesion at the time
of enrollment in active surveillance. At the time
of diagnosis, cancers identied on targeted
biopsy of MR-visible lesions were associated
with signicantly shorter event-free survival and
time to prostate cancer treatment compared to
MR-invisible cancers [17]. Similar ndings from
Olivier etal. demonstrate the presence of lesions
suspicious for csPCa on mpMRI was more likely
to result in histological progression and active
surveillance discontinuation [18]. Additionally,
Wibmer etal. report on a large cohort of patients
with preoperative mpMRI who ultimately underwent radical prostatectomy and, compared to
patients with MR-invisible disease, those with
MR-visible lesions had signicantly increased
rates of biochemical recurrence, development of
metastatic disease, and prostate cancer-specic
mortality [19]. Ultimately, underlying biological
aggressiveness, and not necessarily tumor size or
histological grade, may drive lesion visibility on
mpMRI and be prognostic for oncologic outcomes [20].
Estimation ofTumor Volume
andAnatomic Extent
Beyond pure identication of index lesions,
mpMRI has been utilized as a means to predict
tumor volume and anatomic extent within the
prostate. Multiple studies have now demonstrated
mpMRI underestimates lesion size, with some
reports describing histologic tumor volumes up
17 Staging Imaging forFocal Therapy ofProstate Cancer
181
to three times as large as those characterized on
imaging [21, 22]. Tumor volume assessment
based on individual sequences of mpMRI demonstrates that DWI and ADC maps are highly
accurate in predicting histologic tumor volume
[23], whereas lesions characterized by T2W and
DCE sequences were found to correlate poorly
[24, 25]. Additionally, the anatomic location of
lesions, specically those located within the transition zone, may yield more variability in
predicting histologic tumor volume [26].
Comparing lesion sizes on mpMRI to coregistered radical prostatectomy specimens, Le
Nobin et al. propose a 9 mm margin extending
from the periphery of visible lesions to ensure the
true tumor volume is adequately ablated [21].
Several panels of key opinion leaders within the
focal therapy community have attempted to
dene such an optimal treatment margin based on
lesion characteristics on imaging; however, consensus among these panels was unable to be
reached [27, 28].
invasion (SVI) on radical prostatectomy pathology and reports a sensitivity of 75.9%, specicity
of 94.7%, PPV of 62%, and NPV of 97% with
improvements in predictive capabilities when
imaging features were combined with serum
PSA, presence of high-grade disease, and Partin
table estimates [31].
Lastly, a large systematic review and metaanalysis including 75 studies and nearly 9800
patients performed by de Rooij etal. revealed a
sensitivity of 0.57 and 0.58, as well as a specicity of 0.91 and 0.96, for mpMRI to predict ECE
and SVI on radical prostatectomy pathology,
respectively [32]. Given this high specicity, the
absence of features concerning for locally
advanced disease on mpMRI is likely reassuring
when completing staging imaging and determining patient candidacy for focal therapy. However,
the presence of features concerning for potential
pathologic ECE or SVI on mpMRI likely warrants reconsideration as to whether focal ablative
approaches will offer optimal oncologic control.
Identication ofLocally Advanced
Disease
In addition to characterizing the presence, visibility, size, and anatomic extent of intraprostatic
lesions, mpMRI has also demonstrated utility in
identifying features consistent with locally
advanced disease. Baco etal. reported increasing
tumor contact length with the prostatic capsule
on mpMRI, especially contact length>20mm,
as predictive of microscopic extracapsular extension (ECE) on nal radical prostatectomy pathology [29]. Additionally, Mehralivand et al.
proposed a mpMRI-based grading system for
ECE as: contact length > 15 mm or capsular
bulge/irregularity as grade 1, both features as
grade 2, and capsular breach as grade 3 [30].
Combining this grading system with serum PSA
and biopsy grade yielded superior diagnostic
accuracy for pathologic ECE compared to the
mpMRI-based grading system alone (AUC 0.81
vs. 0.77, respectively; p<0.001) [30]. A study by
Grivas etal. sought to determine the diagnostic
accuracy of mpMRI to predict seminal vesical
Prostate-Specic Membrane
Antigen PET/CT Imaging
Positron emission tomography (PET) imaging has
emerged as an alternative and potentially complementary imaging modality to mpMRI for the
characterization of intraprostatic lesions
(Fig.17.1). While PET tracers such as 18F-FDG
(18F-uorodeoxyglucose), 18F-NaF, 11C-choline,
and 18F-uciclovine have been reported on previously for locoregional and metastatic staging of
prostate cancer, they have poor diagnostic accuracies for index lesion identication and tumor volume estimation [33]. Prostate-specic membrane
antigen (PSMA) is a transmembrane protein
upregulated on the surface of most PCa cells [34]
and has more recently been utilized for molecular
PET imaging techniques when bound to various
radiotracers, including 68Ga and 18F.PSMA-avid
lesions have been reliably demonstrated in over
90% of intraprostatic clinically signicant primary tumors [35–37]. Moreover, investigations
from Bahler etal. and Koseoglu etal. report that
PSMA PET/CT imaging identies 100% of index