Characterisation of Prostate Lesions Using Transrectal Shear Wave Elastography (SWE) Ultrasound Imaging: A Systematic Review

Multiparametric Ultrasound Imaging of Prostate Cancer Using Deep Neural Networks

OBJECTIVE

A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities (acoustic radiation force impulse [ARFI] imaging, shear wave elasticity imaging [SWEI], quantitative ultrasound-midband fit [QUS-MF], and B-mode) for the detection of prostate cancer.

METHODS

A DNN was trained using co-registered ARFI, SWEI, MF, and B-mode data obtained in men with biopsy-confirmed prostate cancer prior to radical prostatectomy (15 subjects, comprising 980,620 voxels). Data were obtained using a commercial scanner that was modified to allow user control of the acoustic beam sequences and provide access to the raw image data. For each subject, the index lesion and a non-cancerous region were manually segmented using visual confirmation based on whole-mount histopathology data.

RESULTS

In a prostate phantom, the DNN increased lesion contrast-to-noise ratio (CNR) compared to a previous approach that used a linear support vector machine (SVM). In the in vivo test datasets (n = 15), the DNN-based mpUS volumes clearly portrayed histopathology-confirmed prostate cancer and significantly improved CNR compared to the linear SVM (2.79 ± 0.88 vs. 1.98 ± 0.73, paired-sample t-test p < 0.001). In a sub-analysis in which the input modalities to the DNN were selectively omitted, the CNR decreased with fewer inputs; both stiffness- and echogenicity-based modalities were important contributors to the multiparametric model.

CONCLUSION

The findings from this study indicate that a DNN can be optimized to generate mpUS prostate volumes with high CNR from ARFI, SWEI, MF, and B-mode and that this approach outperforms a linear SVM approach.

Comparison of Testicular Sonography and Elastography Findings With Semen Parameters in Cases Investigated for Infertility

OBJECTIVES

This study aimed to investigate the correlation between testicular shear wave elastography (SWE) values and semen analysis results in men with infertility.

METHODS

This was a retrospective case-control study. Patients were categorized as normal, abnormal, or azoospermic based on sperm analysis results. Testicular volume was measured using B-mode ultrasonography using the Lambert formula. Subsequently, 40-80 regions of interest measuring 1.5 × 1.5 mm were manually positioned in both testicles based on their size, and two-dimensional SWE was applied through virtual touch imaging quantification software.

RESULTS

The patients had a mean age of 33.79 ± 6.3 years, with semen analysis revealing normal results in 15 patients (22.4%), pathological findings in 35 patients (52.2%), and azoospermia in 17 patients (25.4%). Right, left, total, and mean testicular volumes were significantly lower in patients with azoospermia compared to those in both normal and impaired semen parameters (P < .05). Conversely, testicular elastography scores were higher in patients with azoospermia than in the other groups (P < .05). The significant negative correlation between volume and elastographic findings remained independent of age (r = 0.4, P < .001). The accuracy rates for detecting impaired semen parameters and azoospermia were 94.3% and 94.1%, respectively, after considering factors such as age, testicular volume (right/left/total), and elastography (right/left/total). Notably, the total mean elastography score ranked first, with 100% in the independent normalized importance distribution of these variables.

CONCLUSION

SWE can be used effectively alone or in combination with other diagnostic tools to evaluate histopathological changes in the testicles of male patients with infertility.

Research on the mean maximum Young's modulus value as a new diagnostic parameter for prostate cancer

Ultrasound-based shear wave elastography (SWE) can non-invasively assess prostate tissue stiffness for the diagnosis of prostate cancer (PCa). So far, there is no widely recognized standard for the detection process and calculation method of Young's modulus value in transrectal SWE ultrasound imaging (TSWEUI). In our study, the mean maximum Young's modulus value (m-Emax) of the maximum cross-section of prostate is obtained by calculating the mean of 12 measured Emax in the four quadrants. This retrospective study included 209 suspected malignant prostate disease patients with pathological results in our hospital. Among the 209 patients, 75 patients completed TSWEUI, and 63 of the 75 patients completed magnetic resonance imaging (MRI). The area under the receiver operating characteristic (ROC) curve (AUC) of 75 patients for m-Emax was 0.754. The prostate volume, prostate-specific antigen, and m-Emax were used to develop a nomogram (AUC = 0.868). The nomogram could effectively predict the probability of PCa, thereby reducing the needle biopsy rate for diagnosing PCa. The AUC of 63 patients was not statistically different between m-Emax (AUC = 0.717) and MRI (AUC = 0.787) (P = 0.361). These indicate that m-Emax can be used as an innovative parameter in TSWEUI to diagnosis PCa. TSWEUI is more cost-effective than MRI in diagnosing PCa.

A prospective study on using shear wave elastography to predict the ypT0 stage of rectal cancer after neoadjuvant therapy: a new support for the watch-and-wait approach?

Introduction

The diagnostic accuracy of traditional imaging examination in predicting ypT stage of rectal cancer after neoadjuvant therapy is significantly reduced, which would affect patients' subsequent treatment choices. This study aimed to investigate the use of endorectal shear wave elastography (SWE) for diagnosing ypT0 stage of rectal cancer after neoadjuvant chemoradiotherapy (nCRT).

Methods

Sixty patients with rectal cancer were prospectively recruited in this study. Data on endorectal ultrasound (ERUS) and SWE parameters were collected before nCRT and 6-8 weeks after nCRT. Postoperative pathological results were the gold standard for evaluating the diagnostic accuracy of SWE and ERUS in predicting the ypT0 stage of rectal cancer after nCRT. Receiver operating characteristic (ROC) curve analysis was used to determine the cut-off values of the SWE parameters that best corresponded to the ypT0 stage and analyze the sensitivity, specificity, and accuracy.

Results

The diagnostic accuracies of using ERUS to predict the ypT and ypT0 stages of rectal cancer after nCRT were 58.1% (18/31) and 64.3% (9/14), respectively. The ROC curve was constructed with the lesion's Emean, Emean corrected (EC), Emean difference (ED), Emean corrected differencede (ECD), Emean descendding rate (EDR) and Emean corrected descendding rate (ECDR) values after nCRT, the cut-off values of diagnosing the ypT0 stage were 64.40 kPa, 55.45 kPa, 72.55 kPa, 73.75 kPa, 50.15%, and 55.93%, respectively; the area under the curve (AUC) for diagnosing the ypT0 stage was 0.924, 0.933, 0.748, 0.729, 0.857 and 0.861, respectively. The EC value showed the best diagnostic performance.

Conclusion

SWE could improve the accuracy of conventional ERUS in diagnosing the ypT0 stage of rectal cancer after nCRT. It is expected to become a new method to help predict pathological complete responses in clinical practice and provide new evidence for the watch-and-wait approach.

Passive Elastography for Clinical HIFU Lesion Detection

High-intensity Focused Ultrasound (HIFU) is a promising treatment modality for a wide range of pathologies including prostate cancer. However, the lack of a reliable ultrasound-based monitoring technique limits its clinical use. Ultrasound currently provides real-time HIFU planning, but its use for monitoring is usually limited to detecting the backscatter increase resulting from chaotic bubble appearance. HIFU has been shown to generate stiffening in various tissues, so elastography is an interesting lead for ablation monitoring. However, the standard techniques usually require the generation of a controlled push which can be problematic in deeper organs. Passive elastography offers a potential alternative as it uses the physiological wave field to estimate the elasticity in tissues and not an external perturbation. This technique was adapted to process B-mode images acquired with a clinical system. It was first shown to faithfully assess elasticity in calibrated phantoms. The technique was then implemented on the Focal One® clinical system to evaluate its capacity to detect HIFU lesions in vitro (CNR = 9.2 dB) showing its independence regarding the bubbles resulting from HIFU and in vivo where the physiological wave field was successfully used to detect and delineate lesions of different sizes in porcine liver. Finally, the technique was performed for the very first time in four prostate cancer patients showing strong variation in elasticity before and after HIFU treatment (average variation of 33.0 ± 16.0 % ). Passive elastography has shown evidence of its potential to monitor HIFU treatment and thus help spread its use.

Novel non-MRI imaging techniques for primary diagnosis of prostate cancer: micro-ultrasound, contrast-enhanced ultrasound, elastography, multiparametric ultrasound, and PSMA PET/CT

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) provides enhanced diagnostic accuracy in the detection of prostate cancer, but is not devoid of limitations. Given the recent evolution of non-MRI imaging techniques, this critical review of the literature aimed at summarizing the available evidence on ultrasound-based and nuclear medicine imaging technologies in the initial diagnosis of PCa.

METHODS

Three databases (PubMed®, Web of Science™, and Scopus®) were queried for studies examining their diagnostic performance in the primary diagnosis of PCa, weighted against a histological confirmation of PCa diagnosis, using a free-text protocol. Retrospective and prospective studies, both comparative and non-comparative, systematic reviews (SR) and meta-analysis (MA) were included. Based on authors' expert opinion, studies were selected, data extracted, and results qualitatively described.

RESULTS

Micro-ultrasound (micro-US) appears as an appealing diagnostic strategy given its high accuracy in detection of PCa, apparently non-inferior to mpMRI. The use of multiparametric US (mpUS) likely gives an advantage in terms of effectiveness coming from the combination of different modalities, especially when certain modalities are combined. Prostate-specific membrane antigen (PSMA) PET/CT may represent a whole-body, one-step approach for appropriate diagnosis and staging of PCa. The direct relationship between lesions avidity of radiotracers and histopathologic and prognostic features, and its valid diagnostic performance represents appealing characteristics. However, intrinsic limits of each of these techniques exist and further research is needed before definitively considering them reliable tools for accurate PCa diagnosis. Other novel technologies, such as elastography and multiparametric US, currently relies on a limited number of studies, and therefore evidence about them remains preliminary.

CONCLUSION

Evidence on the role of non-MRI imaging options in the primary diagnosis of PCa is steadily building up. This testifies a growing interest towards novel technologies that might allow overcoming some of the limitations of current gold standard MRI imaging.

Quantifying the Impact of Cancer on the Viscoelastic Properties of the Prostate Gland using a Quasi-Linear Viscoelastic Model

Pathological disorders can alter the mechanical properties of biological tissues, and studying such changes can help to better understand the disease progression. The prostate gland is no exception, as previous studies have shown that cancer can affect its mechanical properties. However, most of these studies have focused on the elastic properties of the tissue and have overlooked the impact of cancer on its viscous response. To address this gap, we used a quasi-linear viscoelastic model to investigate the impact of cancer on both the elastic and viscous characteristics of the prostate gland. By comparing the viscoelastic properties of segments influenced by cancer and those unaffected by cancer in 49 fresh prostates, removed within two hours after prostatectomy surgery, we were able to determine the influence of cancer grade and tumor volume on the tissue. Our findings suggest that tumor volume significantly affects both the elastic modulus and viscosity of the prostate (p-value less than 2%). Specifically, we showed that cancer increases Young's modulus and shear relaxation modulus by 20%. These results have implications for using mechanical properties of the prostate as a potential biomarker for cancer. However, developing an in vivo apparatus to measure these properties remains a challenge that needs to be addressed in future research. STATEMENT OF SIGNIFICANCE: This study is the first to explore how cancer impacts the mechanical properties of prostate tissues using a quasi-linear viscoelastic model. We examined 49 fresh prostate samples collected immediately after surgery and correlated their properties with cancer presence identified in pathology reports. Our results demonstrate a 20% change in the viscoelastic properties of the prostate due to cancer. We initially validated our approach using tissue-mimicking phantoms and then applied it to differentiate between cancerous and normal prostate tissues. These findings offer potential for early cancer detection by assessing these properties. However, conducting these tests in vivo remains a challenge for future research.

Diagnostic accuracy of multiparametric ultrasound in the diagnosis of prostate cancer: systematic review and meta-analysis

OBJECTIVES

Ultrasound (US) technology has recently made advances that have led to the development of modalities including elastography and contrast-enhanced ultrasound. The use of different US modalities in combination may increase the accuracy of PCa diagnosis. This study aims to assess the diagnostic accuracy of multiparametric ultrasound (mpUS) in the PCa diagnosis.

METHODS

Through September 2023, we searched through Cochrane CENTRAL, PubMed, Embase, Scopus, Web of Science, ClinicalTrial.gov, and Google Scholar for relevant studies. We used standard methods recommended for meta-analyses of diagnostic evaluation. We plot the SROC curve, which stands for summary receiver operating characteristic. To determine how confounding factors affected the results, meta-regression analysis was used.

RESULTS

Finally, 1004 patients from 8 studies that were included in this research were examined. The diagnostic odds ratio for PCa was 20 (95% confidence interval (CI), 8-49) and the pooled estimates of mpUS for diagnosis were as follows: sensitivity, 0.88 (95% CI, 0.81-0.93); specificity, 0.72 (95% CI, 0.59-0.83); positive predictive value, 0.75 (95% CI, 0.63-0.87); and negative predictive value, 0.82 (95% CI, 0.71-0.93). The area under the SROC curve was 0.89 (95% CI, 0.86-0.92). There was a significant heterogeneity among the studies (p < 0.01). According to meta-regression, both the sensitivity and specificity of mpUS in the diagnosis of clinically significant PCa (csPCa) were inferior to any PCa.

CONCLUSION

The diagnostic accuracy of mpUS in the diagnosis of PCa is moderate, but the accuracy in the diagnosis of csPCa is significantly lower than any PCa. More relevant research is needed in the future.

CRITICAL RELEVANCE STATEMENT

This study provides urologists and sonographers with useful data by summarizing the accuracy of multiparametric ultrasound in the detection of prostate cancer.

KEY POINTS

• Recent studies focused on the role of multiparametric ultrasound in the diagnosis of prostate cancer. • This meta-analysis revealed that multiparametric ultrasound has moderate diagnostic accuracy for prostate cancer. • The diagnostic accuracy of multiparametric ultrasound in the diagnosis of clinically significant prostate cancer is significantly lower than any prostate cancer.

Canine prostatic serum esterase and strain and 2D-shear wave sonoelastography for evaluation of normal prostate in dogs: Preliminary results

Canine prostatic serum esterase (CPSE) is considered a useful tool to identify prostate disorders in dogs, with increasing interest in ultrasound (US)-based sonoelastography to non-invasively detect prostate disorders. Since no report is available about a possible correlation between these diagnostic tools, we aimed to investigate a possible correlation between strain elastography (SE) and 2D-shear wave elastography (SWE) and CPSE. Twenty-one dogs were included and, on each animal, CPSE was evaluated followed by a complete US examination and SE and 2D-SWE application. Healthy dogs were identified based on the CPSE results. All the dogs included were characterized by normal CPSE values (<52.3 ng/mL) and normal US prostate appearance. The prostate was characterized by intermediate stiffness with SE (pattern III - 84.7% for the left lobe and 79.27% for the right lobe) and softer than the abdominal wall (SR 0.6 for the left lobe and 0.56 for the right lobe), with low values for both m/s and kilopascals (kPa) for 2D-SWE, pointing that the healthy tissue is not hard. 2D-SWE results were, respectively, 13.51 ± 5.55 kPa and 2.31 ± 0.42 m/s for the left lobe and 18.05 ± 6.47 kPa and 2.39 ± 0.43 m/s for the right lobe. The significant difference between the right and left measurements expressed with kPa, not evidenced with m/s, can be considered indicative of m/s as the most reliable measurement to be considered regarding the prostate parenchyma. Even though no linear correlation was detected between CPSE and elastography values, these preliminary results evidence that the healthy prostates were characterized by a similar elastographic pattern, thus pointing that these techniques can be potentially useful to be applied in case of prostatic disorders to improve the accuracy of the final diagnosis in a non-invasive way.

Clinical Trial Protocol: Developing an Image Classification Algorithm for Prostate Cancer Diagnosis on Three-dimensional Multiparametric Transrectal Ultrasound

Introduction and hypothesis

The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately.

Design

This is a phase 2 prospective multicenter diagnostic accuracy study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs).

Protocol overview

Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to study participation, and (serious) adverse events will be reported.

Statistical analysis

The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ≥2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors.

Discussion and summary

This study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa.

Variability of Biceps Muscle Stiffness Measured Using Shear Wave Elastography at Different Anatomical Locations With Different Ultrasound Machines

Shear wave elastography is an emerging diagnostic tool used to assess for changes in the stiffness of muscle. Each region of the muscle may have a different stiffness; therefore, the anatomical region should be carefully selected. Machine vendors each have unique methods for calculating the returned stiffness values and, consequently, a high level of agreement in measurement between machines (quantified using the intraclass correlation coefficient [ICC] and Bland-Altman analysis) will allow research findings to be translated to the clinic. This study assessed three locations within the biceps muscle (50% and 75% of the distance between the acromioclavicular joint and antecubital fossa, and superior to distal myotendinous junction [MTJ]) of 32 healthy volunteers with two different machines, the Canon Aplio i600 and SuperSonic Imagine Aixplorer (SSI), to compare the reported shear wave velocities and the variability by coefficient of variation (CV) and ICC. There was no difference in the CV between machines, but a significant difference in the CV at muscle regions, with the 75% location having a 40.2% reduction in CV. The 75% location had the highest ICC values with good posterior mean ICCs of 0.84 on the Canon and 0.83 on the SSI. The 50% and MTJ locations had poor ICC values. The 75% location provided the lowest CV and highest ICC and should be used for future stiffness assessments.

Quantitative Assessment of Breast-Tumor Stiffness Using Shear-Wave Elastography Histograms

Purpose: Shear-wave elastography (SWE) measures tissue elasticity using ultrasound waves. This study proposes a histogram-based SWE analysis to improve breast malignancy detection. Methods: N = 22/32 (patients/tumors) benign and n = 51/64 malignant breast tumors with histological ground truth. Colored SWE heatmaps were adjusted to a 0−180 kPa scale. Normalized, 250-binned RGB histograms were used as image descriptors based on skewness and area under curve (AUC). The histogram method was compared to conventional SWE metrics, such as (1) the qualitative 5-point scale classification and (2) average stiffness (SWEavg)/maximal tumor stiffness (SWEmax) within the tumor B-mode boundaries. Results: The SWEavg and SWEmax did not discriminate malignant lesions in this database, p > 0.05, rank sum test. RGB histograms, however, differed between malignant and benign tumors, p < 0.001, Kolmogorov−Smirnoff test. The AUC analysis of histograms revealed the reduction of soft-tissue components as a significant SWE biomarker (p = 0.03, rank sum). The diagnostic accuracy of the suggested method is still low (Se = 0.30 for Se = 0.90) and a subject for improvement in future studies. Conclusions: Histogram-based SWE quantitation improved the diagnostic accuracy for malignancy compared to conventional average SWE metrics. The sensitivity is a subject for improvement in future studies.

[Image-guided biopsy of the prostate gland]

The recommendations on carrying out a multiparametric magnetic resonance imaging (mpMRI) for the primary diagnostics and during active surveillance of prostate cancer, include as a consequence an image-guided sampling from conspicuous areas. In doing so, the information on the localization provided by mpMRI is used for a targeted biopsy of the area suspected of being a tumor. The targeted sampling is mainly performed under sonographic control and after fusion of MRI and ultrasound but can also be (mostly in special cases) carried out directly in the MRI scanner. In an ultrasound-guided biopsy, it is vital to coregister the MR images with the ultrasound images (segmentation of the contour of the prostate and registration of suspect findings). This coregistration can either be carried out cognitively (transfer by the person performing the biopsy alone) or software based. Each method shows specific advantages and disadvantages in the prioritization between diagnostic accuracy and resource expenditure.

Tunable three-dimensional engineered prostate cancer tissues for in vitro recapitulation of heterogeneous in vivo prostate tumor stiffness

In this manuscript we report the establishment and characterization of a three-dimensional in vitro, coculture engineered prostate cancer tissue (EPCaT) disease model based upon and informed by our characterization of in vivo prostate cancer (PCa) xenograft tumor stiffness. In prostate cancer, tissue stiffness is known to impact changes in gene and protein expression, alter therapeutic response, and be positively correlated with an aggressive clinical presentation. To inform an appropriate stiffness range for our in vitro model, PC-3 prostate tumor xenografts were established. Tissue stiffness ranged from 95 to 6,750 Pa. Notably, xenograft cell seeding density significantly impacted tumor stiffness; a two-fold increase in the number of seeded cells not only widened the tissue stiffness range throughout the tumor but also resulted in significant spatial heterogeneity. To fabricate our in vitro EPCaT model, PC-3 castration-resistant prostate cancer cells were co-encapsulated with BJ-5ta fibroblasts within a poly(ethylene glycol)-fibrinogen matrix augmented with excess poly(ethylene glycol)-diacrylate to modulate the matrix mechanical properties. Encapsulated cells temporally remodeled their in vitro microenvironment and enrichment of gene sets associated with tumorigenic progression was observed in response to increased matrix stiffness. Through variation of matrix composition and culture duration, EPCaTs were tuned to mimic the wide range of biomechanical cues provided to PCa cells in vivo; collectively, a range of 50 to 10,000 Pa was achievable. Markedly, this also encompasses published clinical PCa stiffness data. Overall, this study serves to introduce our bioinspired, tunable EPCaT model and provide the foundation for future PCa progression and drug development studies. STATEMENT OF SIGNIFICANCE: The development of cancer models that mimic the native tumor microenvironment (TME) complexities is critical to not only develop effective drugs but also enhance our understanding of disease progression. Here we establish and characterize our 3D in vitro engineered prostate cancer tissue model with tunable matrix stiffness, that is inspired by this study's spatial characterization of in vivo prostate tumor xenograft stiffness. Notably, our model's mimicry of the TME is further augmented by the inclusion of matrix remodeling fibroblasts to introduce cancer-stromal cell-cell interactions. This study addresses a critical unmet need in the field by elucidating the prostate tumor xenograft stiffness range and establishing a foundation for recapitulating the biomechanics of site-of-origin and soft tissue metastatic prostate tumors in vitro.

Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements

(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms.

[Sonography of the prostate : Relevance for urologists in daily clinical routine]

Despite the continuous technical progress regarding the transrectal ultrasonography of the prostate (TRUS) and its successful use in combination with magnetic resonance imaging (MRI) in MRI-targeted biopsy, there is no radiologic modality being able to rule out clinically significant prostate cancer without the need of systematic biopsy. In the past few years, TRUS regained more attention due to the development of high frequency ultrasound as well as the combination of different ultrasonic modalities like shear wave elastography and contrast-enhanced sonography (CEUS). Currently, multiparametric MRI (mpMRI)-targeted biopsy shows the best results concerning detection rates, sensitivity and specificity of clinically significant prostate cancer compared to systematic biopsy. In the future, transperineal biopsy is probably going to increasingly replace the transrectal biopsy approach. For both approaches, transrectal ultrasonography is necessary to display the prostate and to detect suspicious lesions. Therefore future improvements in transrectal ultrasonography can be expected.

The challenge of prostate biopsy guidance in the era of mpMRI detected lesion: ultrasound-guided versus in-bore biopsy

The current recommendation in patients with a clinical suspicion for prostate cancer is to perform systematic biopsies extended with targeted biopsies, depending on mpMRI results. Following a positive mpMRI [i.e. Prostate Imaging Reporting and Data System (PI-RADS) ≥3], three targeted biopsy approaches can be performed: visual registration of the MRI images with real-time ultrasound imaging; software-assisted fusion of the MRI images and real-time ultrasound images, and in-bore biopsy within the MR scanner. This collaborative review discusses the advantages and disadvantages of each targeting approach and elaborates on future developments. Cancer detection rates seem to mostly depend on practitioner experience and selection criteria (biopsy naïve, previous negative biopsy, prostate-specific antigen (PSA) selection criteria, presence of a lesion on MRI), and to a lesser extent dependent on biopsy technique. There is no clear consensus on the optimal targeting approach. The choice of technique depends on local experience and availability of equipment, individual patient characteristics, and onsite cost-benefit analysis. Innovations in imaging techniques and software-based algorithms may lead to further improvements in this field.

Multiparametric ultrasound of prostate: role in prostate cancer diagnosis

Recent advances in ultrasonography (US) technology established modalities, such as Doppler-US, HistoScanning, contrast-enhanced ultrasonography (CEUS), elastography, and micro-ultrasound. The early results of these US modalities have been promising, although there are limitations including the need for specialized equipment, inconsistent results, lack of standardizations, and external validation. In this review, we identified studies evaluating multiparametric ultrasonography (mpUS), the combination of multiple US modalities, for prostate cancer (PCa) diagnosis. In the past 5 years, a growing number of studies have shown that use of mpUS resulted in high PCa and clinically significant prostate cancer (CSPCa) detection performance using radical prostatectomy histology as the reference standard. Recent studies have demonstrated the role mpUS in improving detection of CSPCa and guidance for prostate biopsy and therapy. Furthermore, some aspects including lower costs, real-time imaging, applicability for some patients who have contraindication for magnetic resonance imaging (MRI) and availability in the office setting are clear advantages of mpUS. Interobserver agreement of mpUS was overall low; however, this limitation can be improved using standardized and objective evaluation systems such as the machine learning model. Whether mpUS outperforms MRI is unclear. Multicenter randomized controlled trials directly comparing mpUS and multiparametric MRI are warranted.