Multiparametric ultrasound in the detection of prostate cancer: a systematic review

Clinical practice of the transrectal shear-wave elastography in benign prostatic hyperplasia

OBJECTIVE

To investigate the practical value of the transrectal two-dimensional shear-wave elastography (SWE) in benign prostatic hyperplasia (BPH).

METHODS

Consecutive male participants with and without BPH constituted the BPH and control group respectively were enrolled prospectively between March and December 2022. Transrectal conventional ultrasound and SWE examinations for the prostate were performed on these participants. Data of quantitative stiffness of the transitional zone (TZ) and peripheral zone (PZ) of prostate, volume of prostate (VP) and volume of TZ (VTZ) and prostate specific androgen (PSA), etc., were collected. Linear regression analyses were used to investigate the associations between quantitative stiffness data and other clinical parameters.

RESULTS

There were 200 participants evaluated, including 100 healthy participants and 100 BPH patients. For every one-year increment in age, it was correlated with 0.50 kPa increasement of TZ stiffness. VP and VTZ were correlated with TZ stiffness. Higher TZ stiffness was associated with higher free prostate specific antigen (PSA) and total PSA.

CONCLUSIONS

The prostate is stiffer and larger in BPH group compared to control group. Quantitative stiffness of the TZ was related with age, VP, VTZ and PSA.

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

OBJECTIVES

To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes.

METHODS

After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC).

RESULTS

Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value < 0.05) using the Gradient Boosting classifier.

CONCLUSIONS

Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.

The Performance of Different Parametric Ultrasounds in Prostate Cancer Diagnosis: Correlation with Radical Prostatectomy Specimens

BACKGROUND

Prostate cancer is a prevalent cancer among men. Multiparametric ultrasound [mpUS] is a diagnostic instrument that uses various types of ultrasounds to diagnose it. This systematic review aims to evaluate the performance of different parametric ultrasounds in diagnosing prostate cancer by associating with radical prostatectomy specimens.

METHODOLOGY

A review was performed on various ultrasound parameters using five databases. Systematic review tools were utilized to eliminate duplicates and identify relevant results. Reviewers used the Quality Assessment of Diagnostic Accuracy Results [QUADAS-2] to evaluate the bias and applicability of the study outcomes.

RESULT

Between 2012 and 2023, eleven studies were conducted to evaluate the performance of the different ultrasound parametric procedures in detecting prostate cancer using grayscale TRUS, SWE, CEUS, and mpUS. The high sensitivity of these procedures was found at 55%, 88.6%, 81%, and 74%, respectively. The specificity of these procedures was found to be 93.4%, 97%, 88%, and 59%, respectively. This high sensitivity and specificity may be associated with the large lesion size. The studies revealed that the sensitivity of these procedures in diagnosing clinically significant prostate cancer was 55%, 73%, 70%, and 74%, respectively, while the specificity was 61%, 78.2%, 62%, and 59%, respectively.

CONCLUSIONS

The mpUS procedure provides high sensitivity and specificity in PCa detection, especially for clinically significant prostate cancer.

Non-Invasive Diagnosis of Prostate Cancer and High-Grade Prostate Cancer Using Multiparametric Ultrasonography and Serological Examination

OBJECTIVES

This study aimed to assess the efficacy of multiparametric ultrasonography (mpUS) combined with serological examination, as a non-invasive method, in detecting prostate cancer (PCa) or high-grade prostate cancer (HGPCa) respectively.

METHODS

A cohort of 245 individuals with clinically suspected PCa were enrolled. All subjects underwent a comprehensive evaluation, including basic data collection, serological testing, mpUS and prostate biopsy. Random Forest (RF) models were developed, and the mean area under the curve (AUC) in 100 cross-validations was used to assess the performance in distinguishing PCa from HGPCa.

RESULTS

mpUS features showed significant differences (p < 0.001) between the PCa and non-PCa groups, as well as between the HGPCa and low-grade prostate cancer (LGPCa) groups including prostate-specific antigen density (PSAD), transrectal real-time elastography (TRTE) and intensity difference (ID). The RF model, based on these features, demonstrated an excellent discriminative ability for PCa with a mean area under the curve (AUC) of 0.896. Additionally, another model incorporating free prostate-specific antigen (FPSA) and color Doppler flow imaging (CDFI) achieved a high accuracy in predicting HGPCa with a mean AUC of 0.830. The nomogram derived from these models exhibited excellent individualized prediction of PCa and HGPCa.

CONCLUSION

The RF models incorporating mpUS and serological variables achieved satisfactory accuracies in predicting PCa and HGPCa.

Machine learning based on radiomics features combing B-mode transrectal ultrasound and contrast-enhanced ultrasound to improve peripheral zone prostate cancer detection

PURPOSE

To construct machine learning models based on radiomics features combing conventional transrectal ultrasound (B-mode) and contrast-enhanced ultrasound (CEUS) to improve prostate cancer (PCa) detection in peripheral zone (PZ).

METHODS

A prospective study of 166 men (72 benign, 94 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA (fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Time-intensity curves were obtained using SonoLiver software for all lesions in regions of interest. Four parameters were collected as risk factors: the maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT). Radiomics features were extracted from the target lesions from B-mode and CEUS imaging. Multivariable logistic regression analysis was used to construct the model.

RESULTS

A total of 3306 features were extracted from seven categories. Finally, 32 features were screened out from radiomics models. Five models were developed to predict PCa: the B-mode radiomics model (B model), CEUS radiomics model (CEUS model), B-CEUS combined radiomics model (B-CEUS model), risk factors model, and risk factors-radiomics combined model (combined model). Age, PSAD, tPSA, and RT were significant independent predictors in discriminating benign and malignant PZ lesions (P < 0.05). The risk factors model combing these four predictors showed better discrimination in the validation cohort (area under the curve [AUC], 0.84) than the radiomics images (AUC, 0.79 on B model; AUC, 0.78 on CEUS model; AUC, 0.83 on B-CEUS model), and the combined model (AUC: 0.89) achieved the greatest predictive efficacy.

CONCLUSION

The prediction model including B-mode and CEUS radiomics signatures and risk factors represents a promising diagnostic tool for PCa detection in PZ, which may contribute to clinical decision-making.

A current role status of micro-ultrasound imaging in prostate cancer diagnosis

 Recently diagnostic field in medicine was enriched by advances in ultrasonography (US) technology, which led to establishment of novel modalities, one of which is micro-ultrasound. Results demonstrated by early studies have been promising, simultaneously rising a question if those new modalities could become an alternative in diagnosis of prostatic carcinoma (PCa). To answer this question, several studies have been conducted where micro-ultrasound have been compared to standard diagnostic tools, such as conventional TRUS or mpMRI. Nevertheless, new technology presents with some limitations, which include inconsistent results, necessity for specialized equipment, need of training for investigators to understand the findings, and external validation. In this publication, we have identified studies that provided evaluation of the accuracy and efficiency of the micro-ultrasound technology. Additionally, analysis of the results provided a better understanding of the novel imaging tool when compared standard modalities in diagnosis of PCa. Increasing number of studies demonstrated that micro-ultrasound carries high detection rate of PCa and clinically significant prostatic cancer (csPCa), suggesting a similar performance to mpMRI and even showing superiority over conventional TRUS. Recent studies have also showed that micro-ultrasound takes active role in improving the detection of csPCa and guidance for prostate biopsy (PBx) as well as further treatment. Moreover, certain practical aspects such as lower costs, decreased waiting time, real-time imaging and application of the imaging tool for patients that are not suitable for mpMRI (contrast allergy, prosthetics etc.) are significant advantages. Analysis of the results still does not provide clear answer whether micro-ultrasound outperforms mpMRI. Further studies are necessary in order to completely understand the potential of this new technology.

The diagnostic accuracy of micro-ultrasound for prostate cancer diagnosis: a review

PURPOSE

Micro-UltraSound (microUS) is a new imaging modality capable of identifying and targeting suspicious areas, which might further increase the diagnostic yield of prostate biopsy (PBx). Aim of this review is to provide insights into the usefulness of microUS for the sub-stratification of prostate cancer (PCa), clinically significant PCa (i.e., any Gleason score ≥ 7 PCa; csPCa) along with non-organ-confined disease in patients undergoing PBx.

METHODS

A PubMed literature search was performed using keywords: prostate cancer diagnosis, prostate cancer diagnosis surveillance, systematic biopsy, target biopsy, micro-ultrasound, and prostate risk identification using micro-ultrasound.

RESULTS

MicroUS could significantly improve multiparametric magnetic resonance imaging (mpMRI) findings by adding valuable anatomical and pathological information provided by real-time examination. Furthermore, microUS target biopsy could replace systematic biopsy in clinical practice by reducing the detection of clinically insignificant (ciPCa) and increasing that of csPCa. Finally, microUS may be useful in predicting the presence of non-organ confined PCa before radical prostatectomy and it could also be an effective add-on tool for patient monitoring within the active surveillance program.

CONCLUSION

MicroUS may represent an attractive step forward for the management of csPCa as a complementary or alternative tool to mpMRI. Nevertheless, further longitudinal studies are warranted, and the strength of the evidence is still suboptimal to provide clear recommendations for daily clinical practice.

Assessing cancer risk in the anterior part of the prostate using micro-ultrasound: validation of a novel distinct protocol

PURPOSE

To develop and validate a micro-ultrasound risk score that predicts the likelihood of significant prostate cancer in the anterior zone.

METHODS

Patients were enrolled from three expert institutions familiar with micro-ultrasound. The study was conducted in two phases. First, the PRI-MUS anterior score was developed by assessing selected prostate videos from patients who subsequently underwent radical prostatectomy. Second, seven urology readers with varying levels of experience in micro-ultrasound examination evaluated prostate loops according to the PRI-MUS anterior score. Each reader watched the videos and recorded the likelihood of the presence of significant cancer in the anterior part of the prostate in a three-point scale. The coherence among the readers was calculated using the Fleiss kappa and the Cronbach alpha.

RESULTS

A total of 102 selected prostate scans were used to develop the risk assessment for anterior zone cancer in the prostate. The score comprised three categories: likely, equivocal, and unlikely. The median (IQR) sensitivity, specificity, positive predictive value, and negative predictive value for the seven readers were 72% (68-84), 68% (64-84), 75% (72-81), and 73% (71-80), respectively. The mean SD ROC AUC was 0.75 ± 2%, while the Fleiss kappa and the Cronbach alpha were 0.179 and 0.56, respectively.

CONCLUSION

Micro-ultrasound can detect cancerous lesions in the anterior part of the prostate. When combined with the PRI-MUS protocol to assess the peripheral part, it enables an assessment of the entire prostate gland. Pending external validation, the PRI-MUS anterior score developed in this study might be implemented in clinical practice.

3-D Ultrafast Shear Wave Absolute Vibro-Elastography Using a Matrix Array Transducer

Real-time ultrasound imaging plays an important role in ultrasound-guided interventions. The 3-D imaging provides more spatial information compared to conventional 2-D frames by considering the volumes of data. One of the main bottlenecks of 3-D imaging is the long data acquisition time, which reduces practicality and can introduce artifacts from unwanted patient or sonographer motion. This article introduces the first shear wave absolute vibro-elastography (S-WAVE) method with real-time volumetric acquisition using a matrix array transducer. In S-WAVE, an external vibration source generates mechanical vibrations inside the tissue. The tissue motion is then estimated and used in solving a wave equation inverse problem to provide the tissue elasticity. A matrix array transducer is used with a Verasonics ultrasound machine and a frame rate of 2000 volumes/s to acquire 100 radio frequency (RF) volumes in 0.05 s. Using plane wave (PW) and compounded diverging wave (CDW) imaging methods, we estimate axial, lateral, and elevational displacements over 3-D volumes. The curl of the displacements is used with local frequency estimation to estimate elasticity in the acquired volumes. Ultrafast acquisition extends substantially the possible S-WAVE excitation frequency range, now up to 800 Hz, enabling new tissue modeling and characterization. The method was validated on three homogeneous liver fibrosis phantoms and on four different inclusions within a heterogeneous phantom. The homogeneous phantom results show less than 8% (PW) and 5% (CDW) difference between the manufacturer values and the corresponding estimated values over a frequency range of 80-800 Hz. The estimated elasticity values for the heterogeneous phantom at 400-Hz excitation frequency show the average errors of 9% (PW) and 6% (CDW) compared to the provided average values by magnetic resonance elastography (MRE). Furthermore, both imaging methods were able to detect the inclusions within the elasticity volumes. An ex vivo study on a bovine liver sample shows less than 11% (PW) and 9% (CDW) difference between the estimated elasticity ranges by the proposed method and the elasticity ranges provided by MRE and acoustic radiation force impulse (ARFI).

Comparative Assessment of Different Ultrasound Technologies in the Detection of Prostate Cancer: A Systematic Review and Meta-Analysis

The present study aimed to assess the diagnostic test accuracy of different ultrasound scanning technologies in the detection of prostate cancer. A systematic search was conducted using the Cochrane Guidelines for Screening and Diagnostic Tests. We performed a systematic search in the international databases PubMed, Medline, Ovid, Embase and Cochrane Library. Searches were designed to find all studies that evaluated Micro-US, mpUS, SWE and CEUS as the main detection modalities for prostate cancer. This study was registered with Research Registry of systematic review and meta-analysis. The QUADAS-2 tool was utilized to perform quality assessment and bias analysis. The literature search generated 1376 studies. Of these, 320 studies were screened for eligibility, with 1056 studies being excluded. Overall, 26 studies with a total of 6370 patients met the inclusion criteria. The pooled sensitivity for grayscale, CEUS, SWE, Micro-US and mpUS modalities were 0.66 (95% CI 0.54-0.73) 0.73 (95% CI 0.58-0.88), 0.82 (95% CI 0.75-0.90), 0.85 (95% CI 0.76-0.94) and 0.87 (95% CI 0.71-1.03), respectively. Moreover, the pooled specificity for grayscale, CEUS, SWE, Micro-US and mpUS modalities were 0.56 (95% CI 0.21-0.90), 0.78 (95% CI 0.67-0.88), 0.76 (95% CI 0.65-0.88), 0.43 (95% CI 0.28-0.59) and 0.68 (95% CI 0.54-0.81), respectively. In terms of sensitivity, substantial heterogeneity between studies was detected (I2 = 72%, p = 0.000 < 0.05). In relation to specificity, extreme heterogeneity was detected (I2 = 93%, p = 0.000 < 0.05). Some studies proved that advanced ultrasound modalities such as mpUS, Micro-US, shear-wave elastography, contrast enhanced and micro-ultrasound are promising methods for the detection of prostate cancer.

Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study

Background

Identifying patients with clinically significant prostate cancer (csPCa) before biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic performance of traditional transrectal ultrasound (TRUS) for csPCa is relatively limited. This study was aimed to develop a high-performance convolutional neural network (CNN) model (P-Net) based on a TRUS video of the entire prostate and investigate its efficacy in identifying csPCa.

Methods

Between January 2021 and December 2022, this study prospectively evaluated 832 patients from four centres who underwent prostate biopsy and/or radical prostatectomy. All patients had a standardised TRUS video of the whole prostate. A two-dimensional CNN (2D P-Net) and three-dimensional CNN (3D P-Net) were constructed using the training cohort (559 patients) and tested on the internal validation cohort (140 patients) as well as on the external validation cohort (133 patients). The performance of 2D P-Net and 3D P-Net in predicting csPCa was assessed in terms of the area under the receiver operating characteristic curve (AUC), biopsy rate, and unnecessary biopsy rate, and compared with the TRUS 5-point Likert score system as well as multiparametric magnetic resonance imaging (mp-MRI) prostate imaging reporting and data system (PI-RADS) v2.1. Decision curve analyses (DCAs) were used to determine the net benefits associated with their use. The study is registered at https://www.chictr.org.cn with the unique identifier ChiCTR2200064545.

Findings

The diagnostic performance of 3D P-Net (AUC: 0.85-0.89) was superior to TRUS 5-point Likert score system (AUC: 0.71-0.78, P = 0.003-0.040), and similar to mp-MRI PI-RADS v2.1 score system interpreted by experienced radiologists (AUC: 0.83-0.86, P = 0.460-0.732) and 2D P-Net (AUC: 0.79-0.86, P = 0.066-0.678) in the internal and external validation cohorts. The biopsy rate decreased from 40.3% (TRUS 5-point Likert score system) and 47.6% (mp-MRI PI-RADS v2.1 score system) to 35.5% (2D P-Net) and 34.0% (3D P-Net). The unnecessary biopsy rate decreased from 38.1% (TRUS 5-point Likert score system) and 35.2% (mp-MRI PI-RADS v2.1 score system) to 32.0% (2D P-Net) and 25.8% (3D P-Net). 3D P-Net yielded the highest net benefit according to the DCAs.

Interpretation

3D P-Net based on a prostate grayscale TRUS video achieved satisfactory performance in identifying csPCa and potentially reducing unnecessary biopsies. More studies to determine how AI models better integrate into routine practice and randomized controlled trials to show the values of these models in real clinical applications are warranted.

Funding

The National Natural Science Foundation of China (Grants 82202174 and 82202153), the Science and Technology Commission of Shanghai Municipality (Grants 18441905500 and 19DZ2251100), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), Shanghai Science and Technology Innovation Action Plan (21Y11911200), and Fundamental Research Funds for the Central Universities (ZD-11-202151), Scientific Research and Development Fund of Zhongshan Hospital of Fudan University (Grant 2022ZSQD07).

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.

Ultrasound-based radiomics score for pre-biopsy prediction of prostate cancer to reduce unnecessary biopsies

BACKGROUND

Patients undergoing prostate biopsies (PBs) suffer from low positive rates and potential risk for complications. This study aimed to develop and validate an ultrasound (US)-based radiomics score for pre-biopsy prediction of prostate cancer (PCa) and subsequently reduce unnecessary PBs.

METHODS

Between December 2015 and March 2018, 196 patients undergoing initial transrectal ultrasound (TRUS)-guided PBs were retrospectively enrolled and randomly assigned to the training or validation cohort at a ratio of 7:3. A total of 1044 radiomics features were extracted from grayscale US images of each prostate nodule. After feature selection through the least absolute shrinkage and selection operator (LASSO) regression model, the radiomics score was developed from the training cohort. The prediction nomograms were developed using multivariate logistic regression analysis based on the radiomics score and clinical risk factors. The performance of the nomograms was assessed and compared in terms of discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics score consisted of five selected features. Multivariate logistic regression analysis demonstrated that the radiomics score, age, total prostate-specific antigen (tPSA), and prostate volume were independent factors for prediction of PCa (all p < 0.05). The integrated nomogram incorporating the radiomics score and three clinical risk factors reached an area under the curve (AUC) of 0.835 (95% confidence interval [CI], 0.729-0.941), thereby outperforming the clinical nomogram which based on only clinical factors and yielded an AUC of 0.752 (95% CI, 0.618-0.886) (p = 0.04). Both nomograms showed good calibration. Decision curve analysis indicated that using the integrated nomogram would add more benefit than using the clinical nomogram.

CONCLUSION

The radiomics score was an independent factor for pre-biopsy prediction of PCa. Addition of the radiomics score to the clinical nomogram shows incremental prognostic value and may help clinicians make precise decisions to reduce unnecessary PBs.

Ultrasonographic pathological grading of prostate cancer using automatic region-based Gleason grading network

The Gleason scoring system is a reliable method for quantifying the aggressiveness of prostate cancer, which provides an important reference value for clinical assessment on therapeutic strategies. However, to the best of our knowledge, no study has been done on the pathological grading of prostate cancer from single ultrasound images. In this work, a novel Automatic Region-based Gleason Grading (ARGG) network for prostate cancer based on deep learning is proposed. ARGG consists of two stages: (1) a region labeling object detection (RLOD) network is designed to label the prostate cancer lesion region; (2) a Gleason grading network (GNet) is proposed for pathological grading of prostate ultrasound images. In RLOD, a new feature fusion structure Skip-connected Feature Pyramid Network (CFPN) is proposed as an auxiliary branch for extracting features and enhancing the fusion of high-level features and low-level features, which helps to detect the small lesion and extract the image detail information. In GNet, we designed a synchronized pulse enhancement module (SPEM) based on pulse-coupled neural networks for enhancing the results of RLOD detection and used as training samples, and then fed the enhanced results and the original ones into the channel attention classification network (CACN), which introduces an attention mechanism to benefit the prediction of cancer grading. Experimental performance on the dataset of prostate ultrasound images collected from hospitals shows that the proposed Gleason grading model outperforms the manual diagnosis by physicians with a precision of 0.830. In addition, we have evaluated the lesions detection performance of RLOD, which achieves a mean Dice metric of 0.815.

Alternatives for MRI in Prostate Cancer Diagnostics-Review of Current Ultrasound-Based Techniques

The purpose of this review is to present the current role of ultrasound-based techniques in the diagnostic pathway of prostate cancer (PCa). With overdiagnosis and overtreatment of a clinically insignificant PCa over the past years, multiparametric magnetic resonance imaging (mpMRI) started to be recommended for every patient suspected of PCa before performing a biopsy. It enabled targeted sampling of the suspicious prostate regions, improving the accuracy of the traditional systematic biopsy. However, mpMRI is associated with high costs, relatively low availability, long and separate procedure, or exposure to the contrast agent. The novel ultrasound modalities, such as shear wave elastography (SWE), contrast-enhanced ultrasound (CEUS), or high frequency micro-ultrasound (MicroUS), may be capable of maintaining the performance of mpMRI without its limitations. Moreover, the real-time lesion visualization during biopsy would significantly simplify the diagnostic process. Another value of these new techniques is the ability to enhance the performance of mpMRI by creating the image fusion of multiple modalities. Such models might be further analyzed by artificial intelligence to mark the regions of interest for investigators and help to decide about the biopsy indications. The dynamic development and promising results of new ultrasound-based techniques should encourage researchers to thoroughly study their utilization in prostate imaging.

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 and micro-ultrasound in prostate cancer: a comprehensive review

Prostate cancer (PCa) is the most common non-cutaneous cancer diagnosed in males. Traditional tools for screening and diagnosis, such as prostate-specific antigen, digital rectal examination and conventional transrectal ultrasound (TRUS), present low accuracy for PCa detection. Multiparametric MRI has become a game changer in the PCa diagnosis pathway and MRI-targeted biopsies are currently recommended for males at risk of clinically significant PCa, even in biopsy-naïve patients. Recent advances in ultrasound have also emerged with the goal to provide a readily accessible and cost-effective tool for detection of PCa. These newer techniques include elastography and contrast-enhanced ultrasound, as well as improved B-mode and Doppler techniques. These modalities can be combined to define a novel ultrasound approach, multiparametric ultrasound. High frequency Micro-ultrasound has emerged as a promising imaging technology for PCa diagnosis. Initial results have shown high sensitivity of Micro-ultrasound in detecting PCa in addition to its potential in improving the accuracy of targeted biopsies, based on targeting under real-time visualization, rather than relying on cognitive/fusion software MRI-transrectal ultrasound-guided biopsy.

Optimal biopsy approach for detection of clinically significant prostate cancer

Prostate cancer (PCa) diagnostic and therapeutic work-up has evolved significantly in the last decade, with pre-biopsy multiparametric MRI now widely endorsed within international guidelines. There is potential to move away from the widespread use of systematic biopsy cores and towards an individualised risk-stratified approach. However, the evidence on the optimal biopsy approach remains heterogeneous, and the aim of this review is to highlight the most relevant features following a critical assessment of the literature. The commonest biopsy approaches are via the transperineal (TP) or transrectal (TR) routes. The former is considered more advantageous due to its negligible risk of post-procedural sepsis and reduced need for antimicrobial prophylaxis; the more recent development of local anaesthetic (LA) methods now makes this approach feasible in the clinic. Beyond this, several techniques are available, including cognitive registration, MRI-Ultrasound fusion imaging and direct MRI in-bore guided biopsy. Evidence shows that performing targeted biopsies reduces the number of cores required and can achieve acceptable rates of detection whilst helping to minimise complications and reducing pathologist workloads and costs to health-care facilities. Pre-biopsy MRI has revolutionised the diagnostic pathway for PCa, and optimising the biopsy process is now a focus. Combining MR imaging, TP biopsy and a more widespread use of LA in an outpatient setting seems a reasonable solution to balance health-care costs and benefits, however, local choices are likely to depend on the expertise and experience of clinicians and on the technology available.

The role of diagnostic ultrasound imaging for patients with known prostate cancer within an active surveillance pathway: A systematic review

BACKGROUND

The use of multiparametric magnetic resonance imaging (mpMRI) within active surveillance of prostate cancer programmes is identified by the UK National Institute for Health and Care Excellence (NICE guideline NG 131 2019) as having a role for monitoring disease. The widespread demands on mpMRI capacity may limit its use in surveillance. It is therefore timely to review the options that modern ultrasound imaging present to this cohort of patients in the monitoring of prostate cancer.

METHODS

Between April and September 2020, 10 databases were searched to recruit studies for the review. Three reviewers evaluated the publications for inclusion. Characteristics including the inclusion criteria for the study cohort, how disease was determined, identification of disease progression, and the modality and mode of imaging used were reviewed. Given the paucity of full text articles, a meta-analysis was not possible. A narrative review was undertaken.

RESULTS

In total, 12 studies, utilising the range of ultrasound parameters of B-mode, micro-ultrasound, colour Doppler, contrast ultrasound and elastography were included. The review demonstrated that micro-ultrasound offers promise as an imaging tool comparable with mpMRI. However, this is an emerging technology with limited availability. Analysis of the data further demonstrated that by combining the diagnostic features provided by multiple modes reviewed, ultrasound has a role in the diagnostic imaging of patients on active surveillance.

CONCLUSION

Providing a multiparametric approach is utilised, stable ultrasound findings may allow for increased intervals between biopsy for men on surveillance. The advent of micro-US offers promise as an imaging modality within an active surveillance pathway but requires further verification.

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.

Ultrasound elastography

Physicians have used palpation as a diagnostic examination to understand the elastic properties of pathology for a long time since they realized that tissue stiffness is closely related to its biological characteristics. US elastography provided new diagnostic information about elasticity comparing with the morphological feathers of traditional US, and thus expanded the scope of the application in clinic. US elastography is now widely used in the field of diagnosis and differential diagnosis of abnormality, evaluating the degree of fibrosis and assessment of treatment response for a range of diseases. The World Federation of Ultrasound Medicine and Biology divided elastographic techniques into strain elastography (SE), transient elastography and acoustic radiation force impulse (ARFI). The ARFI techniques can be further classified into point shear wave elastography (SWE), 2D SWE, and 3D SWE techniques. The SE measures the strain, while the shear wave-based techniques (including TE and ARFI techniques) measure the speed of shear waves in tissues. In this review, we discuss the various techniques separately based on their basic principles, clinical applications in various organs, and advantages and limitations and which might be most appropriate given that the majority of doctors have access to only one kind of machine.

Clinically significant prostate cancer (csPCa) detection with various prostate sampling schemes based on different csPCa definitions

Background

Combining targeted biopsy (TB) with systematic biopsy (SB) is currently recommended as the first-line biopsy method by the European Association of Urology (EAU) guidelines in patients diagnosed with prostate cancer (PCa) with an abnormal magnetic resonance imaging (MRI). The combined SB and TB indeed detected an additional number of patients with clinically significant prostate cancer (csPCa); however, it did so at the expense of a concomitant increase in biopsy cores. Our study aimed to evaluate if ipsilateral SB (ipsi-SB) + TB or contralateral SB (contra-SB) + TB could achieve almost equal csPCa detection rates as SB + TB using fewer cores based on a different csPCa definition.

Methods

Patients with at least one positive prostate lesion were prospectively diagnosed by MRI. The combination of TB and SB was conducted in all patients. We compared the csPCa detection rates of the following four hypothetical biopsy sampling schemes with those of SB + TB: SB, TB, ipsi-SB + TB, and contra-SB + TB.

Results

The study enrolled 279 men. The median core of SB, TB, ipsi-SB + TB, and contra-SB + TB was 10, 2, 7 and 7, respectively (P < 0.001). ipsi-SB + TB detected significantly more patients with csPCa than contra-SB + TB based on the EAU guidelines (P = 0.042). They were almost equal on the basis of the Epstein criteria (P = 1.000). Compared with SB + TB, each remaining method detected significantly fewer patients with csPCa regardless of the definition (P < 0.001) except ipsi-SB + TB on the grounds of D1 (P = 0.066). Ten additional subjects were identified with a higher Gleason score (GS) on contra-SB + TB, and only one was considered as significantly upgraded (GS = 6 on ipsi-SB + TB to a GS of 8 on contra-SB + TB).

Conclusions

Ipsi-SB + TB could acquire an almost equivalent csPCa detection value to SB + TB using significantly fewer cores when csPCa was defined according to the EAU guidelines. Given that there was only one significantly upgrading patient on contra-SB, our results suggested that contra-SB could be avoided.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-021-00949-7.

Imaging of Neuroendocrine Prostatic Carcinoma

Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of prostate cancer that typically has a high metastatic potential and poor prognosis in comparison to the adenocarcinoma subtype. Although it can arise de novo, NEPC much more commonly occurs as a mechanism of treatment resistance during therapy for conventional prostatic adenocarcinoma, the latter is also termed as castration-resistant prostate cancer (CRPC). The incidence of NEPC increases after hormonal therapy and they represent a challenge, both in the radiological and pathological diagnosis, as well as in the clinical management. This article provides a comprehensive imaging review of prostatic neuroendocrine tumors.

Robotic Transrectal Computed Tomographic Ultrasound with Artificial Neural Network Analysis: First Validation and Comparison with MRI-Guided Biopsies and Radical Prostatectomy

INTRODUCTION

There is still a lack of availability of high-quality multiparametric magnetic resonance imaging (mpMRI) interpreted by experienced uro-radiologists to rule out clinically significant PC (csPC). Consequently, we developed a new imaging method based on computed tomographic ultrasound (US) supported by artificial neural network analysis (ANNA).

METHODS

Two hundred and two consecutive patients with visible mpMRI lesions were scanned and recorded by robotic CT-US during mpMRI-TRUS biopsy. Only significant index lesions (ISUP ≥2) verified by whole-mount pathology were retrospectively analyzed. Their visibility was reevaluated by 2 blinded investigators by grayscale US and ANNA.

RESULTS

In the cohort, csPC was detected in 105 cases (52%) by mpMRI-TRUS biopsy. Whole-mount histology was available in 44 cases (36%). In this subgroup, mean PSA level was 8.6 ng/mL, mean prostate volume was 33 cm3, and mean tumor volume was 0.5 cm3. Median PI-RADS and ISUP of index lesions were 4 and 3, respectively. Index lesions were visible in grayscale US and ANNA in 25 cases (57%) and 30 cases (68%), respectively. Combining CT-US-ANNA, we detected index lesions in 35 patients (80%).

CONCLUSIONS

The first results of multiparametric CT-US-ANNA imaging showed promising detection rates in patients with csPC. US imaging with ANNA has the potential to complement PC diagnosis.

Tumor contact length of prostate cancer determined by a three-dimensional method on multiparametric magnetic resonance imaging predicts extraprostatic extension and biochemical recurrence

OBJECTIVE

To evaluate the clinical benefit of tumor contact length as a predictor of pathological extraprostatic extension and biochemical recurrence in patients undergoing prostatectomy.

METHODS

A total of 91 patients who underwent 3T multiparametric magnetic resonance imaging before prostatectomy from April 2014 to July 2019 were included. A total of 94 prostate cancer foci were analyzed retrospectively. We evaluated maximum tumor contact length, which was determined to be the maximum value in the three-dimensional directions, as a predictor of pathological extraprostatic extension and biochemical recurrence.

RESULTS

A total of 19 lesions (20.2%) had positive pathological extraprostatic extension. Areas under the curves showed maximum tumor contact length to be a significantly better parameter to predict pathological extraprostatic extension than the Prostate Imaging Reporting and Data System (P = 0.002), tumor maximal diameter (P = 0.001), prostate-specific antigen (P = 0.020), Gleason score (P < 0.001), and clinical T stage (P < 0.001). Multivariate analysis showed maximum tumor contact length (P = 0.003) to be an independent risk factor for predicting biochemical recurrence. We classified the patients using preoperative factors (prostate-specific antigen >10, Gleason score >3 + 4 and maximum tumor contact length >10 mm) into three groups: (i) high-risk group (patients having all factors); (ii) intermediate-risk group (patients having two of three factors); and (iii) low-risk group (patients having only one or none of the factors). Kaplan-Meier curves showed that the high-risk group had significantly worse biochemical recurrence than the intermediate-risk group (P = 0.042) and low-risk group (P < 0.001).

CONCLUSIONS

Our findings suggest that maximum tumor contact length is an independent predictor of pathological extraprostatic extension and biochemical recurrence. A risk stratification system using prostate-specific antigen, Gleason score and maximum tumor contact length might be useful for preoperative assessment of prostate cancer patients.

[Clinical aspects in the diagnosis and treatment of prostate cancer]

BACKGROUND

Diagnosis and treatment of primary prostate cancer (PCA) have undergone significant changes in the last few years due to modern imaging.

OBJECTIVES

Established and modern diagnostic and therapeutic modalities for detection and treatment of primary PCA are presented and discussed critically.

MATERIALS AND METHODS

Background knowledge and guideline recommendations on primary PCA are summarized and additional information from relevant publications is given.

RESULTS

Modern imaging, in particular multiparametric magnetic resonance imaging (mpMRI), has revolutionized the diagnostic work-up of primary PCA. Due to mpMRI, tumors are detected significantly better in both initial and re-biopsy with a significant reduction of overdiagnosis of clinically insignificant PCA. Therapeutic approaches such as active surveillance, radical prostatectomy and focal therapies are increasingly being planned and carried out relying on MR-imaging information concerning tumor extent and tumor aggressiveness. In addition, prostate-specific membrane antigen-positron emission tomography/computed tomography (PSMA-PET/CT) has shown superiority in assessing patients with suspected biochemical recurrence and in primary staging of PCA compared to conventional imaging in terms of detection of metastases.

CONCLUSIONS

Modern imaging, especially mpMRI and PSMA-PET/CT, has added substantial benefits in modern diagnosis and treatment of primary PCA. Moreover, multiparametric ultrasound is also a promising addition to the radiological armamentarium in the management of primary PCA.

Prostate Cancer Detection Using 3-D Shear Wave Elasticity Imaging

Transrectal ultrasound (TRUS) B-mode imaging provides insufficient sensitivity and specificity for prostate cancer (PCa) targeting when used for biopsy guidance. Shear wave elasticity imaging (SWEI) is an elasticity imaging technique that has been commercially implemented and is sensitive and specific for PCa. We have developed a SWEI system capable of 3-D data acquisition using a dense acoustic radiation force (ARF) push approach that leads to enhanced shear wave signal-to-noise ratio compared with that of the commercially available SWEI systems and facilitates screening of the entire gland before biopsy. Additionally, we imaged and assessed 36 patients undergoing radical prostatectomy using 3-D SWEI and determined a shear wave speed threshold separating PCa from healthy prostate tissue with sensitivities and specificities akin to those for multiparametric magnetic resonance imaging fusion biopsy. The approach measured the mean shear wave speed in each prostate region to be 4.8 m/s (Young's modulus E = 69.1 kPa) in the peripheral zone, 5.3 m/s (E = 84.3 kPa) in the central gland and 6.0 m/s (E = 108.0 kPa) for PCa with statistically significant (p < 0.0001) differences among all regions. Three-dimensional SWEI receiver operating characteristic analyses identified a threshold of 5.6 m/s (E = 94.1 kPa) to separate PCa from healthy tissue with a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of 81%, 82%, 69%, 89% and 0.84, respectively. Additionally, a shear wave speed ratio was assessed to normalize for tissue compression and patient variability, which yielded a threshold of 1.11 to separate PCa from healthy prostate tissue and was accompanied by a substantial increase in specificity, PPV and AUC, where the sensitivity, specificity, PPV, NPV and AUC were 75%, 90%, 79%, 88% and 0.90, respectively. This work illustrates the feasibility of using 3-D SWEI data to detect and localize PCa and demonstrates the benefits of normalizing for applied compression during data acquisition for use in biopsy targeting studies.

A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

Objectives

To evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa).

Methods

A total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve.

Results

The multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model.

Conclusions

Clinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.

Molecular imaging of orthotopic prostate cancer with nanobubble ultrasound contrast agents targeted to PSMA

Ultrasound imaging is routinely used to guide prostate biopsies, yet delineation of tumors within the prostate gland is extremely challenging, even with microbubble (MB) contrast. A more effective ultrasound protocol is needed that can effectively localize malignancies for targeted biopsy or aid in patient selection and treatment planning for organ-sparing focal therapy. This study focused on evaluating the application of a novel nanobubble ultrasound contrast agent targeted to the prostate specific membrane antigen (PSMA-targeted NBs) in ultrasound imaging of prostate cancer (PCa) in vivo using a clinically relevant orthotopic tumor model in nude mice. Our results demonstrated that PSMA-targeted NBs had increased extravasation and retention in PSMA-expressing orthotopic mouse tumors. These processes are reflected in significantly different time intensity curve (TIC) and several kinetic parameters for targeted versus non-targeted NBs or LUMASON MBs. These, may in turn, lead to improved image-based detection and diagnosis of PCa in the future.

Multiparametric Ultrasound for Targeting Prostate Cancer: Combining ARFI, SWEI, QUS and B-Mode

Diagnosing prostate cancer through standard transrectal ultrasound (TRUS)-guided biopsy is challenging because of the sensitivity and specificity limitations of B-mode imaging. We used a linear support vector machine (SVM) to combine standard TRUS imaging data with acoustic radiation force impulse (ARFI) imaging data, shear wave elasticity imaging (SWEI) data and quantitative ultrasound (QUS) midband fit data to enhance lesion contrast into a synthesized multiparametric ultrasound volume. This SVM was trained and validated using a subset of 20 patients and tested on a second subset of 10 patients. Multiparametric US led to a statistically significant improvements in contrast, contrast-to-noise ratio (CNR) and generalized CNR (gCNR) when compared with standard TRUS B-mode and SWEI; in contrast and CNR when compared with MF; and in CNR when compared with ARFI. ARFI, MF and SWEI also outperformed TRUS B-mode in contrast, with MF outperforming B-mode in CNR and gCNR as well. ARFI, although only yielding statistically significant differences in contrast compared with TRUS B-mode, captured critical qualitative features for lesion identification. Multiparametric US enhanced lesion visibility metrics and is a promising technique for targeted TRUS-guided prostate biopsy in the future.

Advances in the diagnostic options for prostate cancer

Over the past decade, despite the controversies surrounding prostate cancer screening, significant refinements have improved its application. PSA screening, although it has been questioned, appears to confer a mortality benefit and remains the most effective way to identify the possible presence of prostate cancer. Methods to improve the specificity of PSA screening and limit overdiagnosis of indolent cancers, including risk-stratified screening regimens, are currently being utilized. Certain imaging modalities, such as multiparametric MRI, have proven to be excellent adjuncts providing improved risk stratification and the ability for targeted biopsies; however, concerns over variability in interpretation and generalizability persist. A number of novel biomarkers have become available with nearly all demonstrating the ability to improve upon the specificity of PSA screening; however, optimal timing, direct comparisons, and usefulness in conjunction with imaging modalities remain to be elucidated. With the improvement in testing options and recognition of the risk/benefit ratio for men undergoing screening for prostate cancer, the increasing role of shared decision making in the process is emphasized.

Diagnostic Accuracy of Microultrasound in Patients with a Suspicion of Prostate Cancer at Magnetic Resonance Imaging: A Single-institutional Prospective Study

BACKGROUND

Multiparametric magnetic resonance imaging (MRI) represents the gold standard for the diagnosis of clinically significant prostate cancer (csPCa). The search for alternative diagnostic techniques is still ongoing.

OBJECTIVE

To determine the accuracy of microultrasound (microUS) for the diagnosis of csPCa within prospectively collected cohort of patients with a suspicion of prostate cancer (PCa) according to MRI.

DESIGN, SETTING, AND PARTICIPANTS

A total of 320 consecutive patients with at least one Prostate Imaging Reporting and Data System (PIRADS) ≥3 lesion according to MRI were prospectively enrolled.

INTERVENTION

All patients received microUS before prostate biopsy using the ExactVu system; the Prostate Risk Identification using microUS (PRI-MUS) protocol was used to identify targets. The urologists were blinded to MRI results until after the microUS targeting was completed. All patients received both targeted (based on either microUS or MRI findings) and randomized biopsies.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The sensitivity and specificity of microUS to determine the presence of csPCa (defined as at least one core with a Gleason score ≥7 PCa) were determined. Multivariable logistic regression analysis was fitted to determine the predictors of csPCa.

RESULTS AND LIMITATIONS

Clinically significant PCa was diagnosed in 116 (36.3%) patients. The sensitivity and negative predictive value of microUS for csPCa diagnosis were 89.7% and 81.5%, while specificity and positive predictive value were 26.0% and 40.8%, respectively. A combination of microUS-targeted and randomized biopsies would allow diagnosing the same proportion of csPCa as that diagnosed by an approach combining MRI-targeted and randomized biopsies (n = 113; 97.4%), with only three (2.6%) csPCa cases diagnosed by a microUS-targeted and three (2.6%) by an MRI-targeted approach. In a logistic regression model, an increasing PRI-MUS score was an independent predictor of csPCa (p ≤ 0.005). The main limitation of the current study is represented by the fact that all patients had suspicious MRI.

CONCLUSIONS

Microultrasound is a promising imaging modality for targeted prostate biopsies. Our results suggest that a microUS-based biopsy strategy may be capable of diagnosing the great majority of cancers, while missing only few patients with csPCa.

PATIENT SUMMARY

According to our results, microultrasound (microUS) may represent an effective diagnostic alternative to magnetic resonance imaging for the diagnosis of clinically significant prostate cancer, providing high sensitivity and a high negative predictive value. Further randomized studies are needed to confirm the potential role of microUS in the diagnostic pathway of patients with a suspicion of prostate cancer.

Use of Imaging to Optimise Prostate Cancer Tumour Volume Assessment for Focal Therapy Planning

PURPOSE OF REVIEW

Rapid advances in imaging of the prostate have facilitated the development of focal therapy and provided a non-invasive method of estimating tumour volume. Focal therapy relies on an accurate estimate of tumour volume for patient selection and treatment planning so that the optimal energy dose can be delivered to the target area(s) of the prostate while minimising toxicity to surrounding structures. This review provides an overview of different imaging modalities which may be used to optimise tumour volume assessment and critically evaluates the published evidence for each modality.

RECENT FINDINGS

Multi-parametric MRI (mp-MRI) has become the standard tool for patient selection and guiding focal therapy treatment. The current evidence suggests that mp-MRI may underestimate tumour volume, although there is a large variability in results. There remain significant methodological challenges associated with pathological processing and accurate co-registration of histopathological data with mp-MRI. Advances in different ultrasound modalities are showing promise but there has been limited research into tumour volume estimation. The role of PSMA PET/CT is still evolving and further investigation is needed to establish if this is a viable technique for prostate tumour volumetric assessment. mp-MRI provides the necessary tumour volume information required for selecting patients and guiding focal therapy treatment. The potential for underestimation of tumour volume should be taken into account and an additional margin applied to ensure adequate treatment coverage. At present, there are no other viable image-based alternatives although advances in new technologies may refine volume estimations in the future.

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection

The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e., the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91 percent specificity and 90 percent average accuracy over the targeted CEUS images for prostate cancer detection, which was superior ( ) than previously reported approaches and implementations.

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

OBJECTIVES

The aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound.

METHODS

This study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation.

RESULTS

The multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored.

CONCLUSIONS

In this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa.

KEY POINTS

• Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.

3-D Multi-parametric Contrast-Enhanced Ultrasound for the Prediction of Prostate Cancer

Trans-rectal ultrasound-guided 12-core systematic biopsy (SBx) is the standard diagnostic pathway for prostate cancer (PCa) because of a lack of sufficiently accurate imaging. Quantification of 3-D dynamic contrast-enhanced ultrasound (US) might open the way for a targeted procedure in which biopsies are directed at lesions suspicious on imaging. This work describes the expansion of contrast US dispersion imaging algorithms to 3-D and compares its performance against malignant and benign disease. Furthermore, we examined the feasibility of a multi-parametric approach to predict SBx-core outcomes using machine learning. An area under the receiver operating characteristic (ROC) curve of 0.76 and 0.81 was obtained for all PCa and significant PCa, respectively, an improvement over previous US methods. We found that prostatitis, in particular, was a source of false-positive readings.

A comparison of magnetic resonance imaging techniques used to secure biopsies in prostate cancer patients

Introduction: Prostate cancer (PCa) is the most common diagnosed malignancy among the male population in the United States. The incidence is increasing with an estimated amount of 175.000 cases in 2019. Areas covered: Primarily, PCa is generally detected by an elevated or rising serum prostate-specific antigen (PSA) and digital rectal examination (DRE) followed by pathological examination. Histopathology ultimately confirms the presence of PCa and determines a Gleason score. However, PSA and DRE have low specificity and sensitivity, respectively. Subsequently, accurate assessment of the aggressiveness of PCa is essential to prevent overdiagnosis and thus overtreatment of low-risk or indolent cancers. By visualizing PCa suspicious lesions and sampling them during the targeted biopsy, it is likely that the diagnostic accuracy of significant PCa improves. This article reviews the current imaging techniques used to secure biopsies in patients with a suspicion of PCa. The advantages and limitations of each technique are described. Expert opinion: Multiparametric magnetic resonance imaging (mpMRI) and subsequent-targeted biopsy have improved the diagnostic accuracy of PCa detection in men with an elevated or rising serum PSA. Prostate lesions visible on mpMRI are easily targeted during either in-bore MRI-guided biopsy, cognitive fusion biopsy or MRI-TRUS fusion biopsy.

Photoacoustic imaging as a tool to probe the tumour microenvironment

The tumour microenvironment (TME) is a complex cellular ecosystem subjected to chemical and physical signals that play a role in shaping tumour heterogeneity, invasion and metastasis. Studying the roles of the TME in cancer progression would strongly benefit from non-invasive visualisation of the tumour as a whole organ in vivo, both preclinically in mouse models of the disease, as well as in patient tumours. Although imaging techniques exist that can probe different facets of the TME, they face several limitations, including limited spatial resolution, extended scan times and poor specificity from confounding signals. Photoacoustic imaging (PAI) is an emerging modality, currently in clinical trials, that has the potential to overcome these limitations. Here, we review the biological properties of the TME and potential of existing imaging methods that have been developed to analyse these properties non-invasively. We then introduce PAI and explore the preclinical and clinical evidence that support its use in probing multiple features of the TME simultaneously, including blood vessel architecture, blood oxygenation, acidity, extracellular matrix deposition, lipid concentration and immune cell infiltration. Finally, we highlight the future prospects and outstanding challenges in the application of PAI as a tool in cancer research and as part of a clinical oncologist's arsenal.

Summary: This Review details the potential of photoacoustic imaging to visualise features of the tumour microenvironment such as blood vessels, hypoxia, fibrosis and immune infiltrate to provide unprecedented insight into tumour biology.

Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net

PURPOSE

Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle placement, brachytherapy treatment planning, and motion management. Manual segmentation during these interventions is time-consuming and subject to inter- and intraobserver variation. To address these drawbacks, we aimed to develop a deep learning-based method which integrates deep supervision into a three-dimensional (3D) patch-based V-Net for prostate segmentation.

METHODS AND MATERIALS

We developed a multidirectional deep-learning-based method to automatically segment the prostate for ultrasound-guided radiation therapy. A 3D supervision mechanism is integrated into the V-Net stages to deal with the optimization difficulties when training a deep network with limited training data. We combine a binary cross-entropy (BCE) loss and a batch-based Dice loss into the stage-wise hybrid loss function for a deep supervision training. During the segmentation stage, the patches are extracted from the newly acquired ultrasound image as the input of the well-trained network and the well-trained network adaptively labels the prostate tissue. The final segmented prostate volume is reconstructed using patch fusion and further refined through a contour refinement processing.

RESULTS

Forty-four patients' TRUS images were used to test our segmentation method. Our segmentation results were compared with the manually segmented contours (ground truth). The mean prostate volume Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and residual mean surface distance (RMSD) were 0.92 ± 0.03, 3.94 ± 1.55, 0.60 ± 0.23, and 0.90 ± 0.38 mm, respectively.

CONCLUSION

We developed a novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for diagnostic and therapeutic applications in prostate cancer.

Photoacoustic tomography for imaging the prostate: a transurethral illumination probe design and application

In vivo imaging of prostate cancer with photoacoustic tomography is currently limited by the lack of sufficient local fluence for deep tissue penetration and the risk of over-irradiation near the laser-tissue contact surface. We propose the design of a transurethral illumination probe that addresses those limitations. A high energy of 50 mJ/pulse is coupled into a 1000-µm-core diameter multimode fiber. A 2 cm diffusing end is fabricated, which delivers light in radial illumination. The radial illumination is then reflected and reshaped by a parabolic cylindrical mirror to obtain nearly parallel side illumination with a doubled fluence. The fiber assembly is housed in a 25 Fr cystoscope sheath to provide protection of the fiber and maintain a minimal laser-tissue contact distance of 5 mm. A large laser-tissue contact surface area of 4 cm² is obtained and the fluence on the tissue surface is kept below the maximum permissible exposure. By imaging a prostate mimicking phantom, a penetration depth of 3.5 cm at 10 mJ/cm² fluence and 700 nm wavelength is demonstrated. The results indicate that photoacoustic tomography with the proposed transurethral probe has the potential to image the entire prostate while satisfying the fluence maximum permissible exposure and delivering a high power to the tissue.

Cylindrical illumination with angular coupling for whole-prostate photoacoustic tomography

Current diagnosis of prostate cancer relies on histological analysis of tissue samples acquired by biopsy, which could benefit from real-time identification of suspicious lesions. Photoacoustic tomography has the potential to provide real-time targets for prostate biopsy guidance with chemical selectivity, but light delivered from the rectal cavity has been unable to penetrate to the anterior prostate. To overcome this barrier, a urethral device with cylindrical illumination is developed for whole-prostate imaging, and its performance as a function of angular light coupling is evaluated with a prostate-mimicking phantom.

Multiparametric ultrasound: evaluation of greyscale, shear wave elastography and contrast-enhanced ultrasound for prostate cancer detection and localization in correlation to radical prostatectomy specimens

BACKGROUND

The diagnostic pathway for prostate cancer (PCa) is advancing towards an imaging-driven approach. Multiparametric magnetic resonance imaging, although increasingly used, has not shown sufficient accuracy to replace biopsy for now. The introduction of new ultrasound (US) modalities, such as quantitative contrast-enhanced US (CEUS) and shear wave elastography (SWE), shows promise but is not evidenced by sufficient high quality studies, especially for the combination of different US modalities. The primary objective of this study is to determine the individual and complementary diagnostic performance of greyscale US (GS), SWE, CEUS and their combination, multiparametric ultrasound (mpUS), for the detection and localization of PCa by comparison with corresponding histopathology.

METHODS/DESIGN

In this prospective clinical trial, US imaging consisting of GS, SWE and CEUS with quantitative mapping on 3 prostate imaging planes (base, mid and apex) will be performed in 50 patients with biopsy-proven PCa before planned radical prostatectomy using a clinical ultrasound scanner. All US imaging will be evaluated by US readers, scoring the four quadrants of each imaging plane for the likelihood of significant PCa based on a 1 to 5 Likert Scale. Following resection, PCa tumour foci will be identified, graded and attributed to the imaging-derived quadrants in each prostate plane for all prostatectomy specimens. Primary outcome measure will be the sensitivity, specificity, negative predictive value and positive predictive value of each US modality and mpUS to detect and localize significant PCa evaluated for different Likert Scale thresholds using receiver operating characteristics curve analyses.

DISCUSSION

In the evaluation of new PCa imaging modalities, a structured comparison with gold standard radical prostatectomy specimens is essential as first step. This trial is the first to combine the most promising ultrasound modalities into mpUS. It complies with the IDEAL stage 2b recommendations and will be an important step towards the evaluation of mpUS as a possible option for accurate detection and localization of PCa.

TRIAL REGISTRATION

The study protocol for multiparametric ultrasound was prospectively registered on Clinicaltrials.gov on 14 March 2017 with the registry name 'Multiparametric Ultrasound-Study for the Detection of Prostate Cancer' and trial registration number NCT03091231.

Evaluation of Hepatocellular Carcinoma Transarterial Chemoembolization using Quantitative Analysis of 2D and 3D Real-time Contrast Enhanced Ultrasound

Quantitative 2D and 3D contrast-enhanced ultrasound (CEUS) was assessed to evaluate early transarterial chemoembolization (TACE) treatment response. Seventeen patients scheduled for TACE for the treatment of hepatocellular carcinoma participated in the study. 2D and 3D CEUS were performed for each patient at three time points: prior to TACE, 1-2 weeks post TACE, and 1 month post TACE. Peak-intensities of the tumor and surrounding liver tissue were calculated from 2D and 3D data before and after TACE and used to evaluate tumor treatment response. Residual tumor percentages were calculated from 2D and 3D CEUS acquired 1-2 weeks and 1 month post TACE and compared with results from MRI 1 month post TACE. Nine subjects had complete response while 8 had incomplete response. Peak-intensities of the tumor from 3D CEUS prior to TACE were similar between the complete and incomplete treatment groups (p=0.70), while 1-2 weeks (p<0.01) and 1 month post treatment (p<0.01) were significantly lower in the complete treatment group than in the incomplete treatment group. For 2D CEUS, only the peak-intensity values of the tumor from1 month post TACE were significantly different (p<0.01). The correlation coefficients between 2D and 3D residual tumor estimates 1-2 weeks post TACE and the estimates from MRI were 0.73 and 0.94, respectively, while those from 2D and 3D CEUS 1 month post TACE were 0.66 and 0.91, respectively. Quantitative analysis on 2D and 3D CEUS shows potential to differentiate patients with complete vs. incomplete response to TACE as early as 1-2 weeks post treatment.

Validation of the current eligibility criteria for focal therapy in men with localized prostate cancer and the role of MRI

PURPOSE

To validate current eligibility criteria for focal therapy (FT) in prostate cancer men undergoing radical prostatectomy (RP) and to assess the role of magnetic resonance imaging (MRI).

METHODS

Retrospective analysis of 217 RP patients (2009-2016) with preoperative MRI (almost all in external institutions) and fulfillment of different FT eligibility criteria: unilateral tumor, clinical tumor stage ≤ cT2a, prostate volume ≤ 60 mL and either biopsy Gleason 3 + 3 or ≤ 3 + 4 and PSA ≤ 10 or ≤ 15 ng/mL. Multivariable logistic regression analyses (MVA) assessed the role of MRI to predict the presence of significant contralateral tumor or extracapsular extension (ECE), including seminal vesicle invasion. To quantify model accuracy, Receiver Operating Characteristics-derived area under the curve (AUC) was used.

RESULTS

Of 217 patients fulfilling widest biopsy criteria and 113 fulfilling additional MRI criteria, 64 (29.7%) and 37 (32.7%) remained eligible for FT according to histopathological results. In MVA, fulfillment of MRI criteria reached independent predictor status for prediction of contralateral tumor but not for ECE. Addition of MRI resulted in AUC gain (57.5-64.6%). Sensitivity, specificity, PPV and NPV for MRI to predict contralateral tumor were: 41.8, 71.6, 70.9 and 42.6%, respectively. Virtually the same results were recorded for Gleason 3 + 3 and/or PSA ≤ 10 ng/mL.

CONCLUSIONS

Patient eligibility criteria for FT using biopsy criteria remained insufficient with respect to contralateral tumor disease. Although, MRI improves accuracy, it cannot safely exclude or minimize chance of significant cancer on contralateral prostate side. To date, stricter eligibility criteria are needed to provide more diagnostic reliability.

Novel application of three-dimensional shear wave elastography in the detection of clinically significant prostate cancer

The present study evaluated three-dimensional shear wave elastography (3D SWE) in the detection of clinically significant prostate cancer. Clinically significant prostate cancer was defined by a minimum of one biopsy core with a Gleason score of 3+4 or 6 with a maximum cancer core length >4 mm. Patients with serum prostate-specific antigen levels of 4.0-20.0 ng/ml who were suspected of having prostate cancer from multi-parametric magnetic resonance imaging (mpMRI) were prospectively recruited. The 3D SWE was performed pre-biopsy, after which patients underwent MRI-transrectal ultrasound image-guided targeted biopsies for cancer-suspicious lesions and 12-core systematic biopsies. The pathological biopsy results were compared with the mpMRI and 3D SWE images. A total of 12 patients who were suspected of having significant cancer on mpMRI were included. The median pre-biopsy PSA value was 5.65 ng/ml. Of the 12 patients, 10 patients were diagnosed as having prostate cancer. In the targeted biopsy lesions, there was a significant difference in Young's modulus between the cancer-detected area (median 64.1 kPa, n=20) and undetected area (median 30.8 kPa, n=8; P<0.0001). On evaluation of receiver operating characteristics, a cut-off value of the Young's modulus of 41.0 kPa was used for the detection of clinically significant cancer, with which the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of cancer detection were 58, 97, 86 and 87%, respectively. When combining this cut-off tissue elasticity value with Prostate Imaging Reporting and Data System (PI-RADS) scores, the sensitivity, specificity, positive predictive value and negative predictive value of cancer detection were improved to 70, 98, 91 and 92%, respectively. In the cancer-detected lesions, a significant correlation was identified between the tissue elasticity value of the lesions and Gleason score (r=0.898, P<0.0001). In conclusion, PI-RADS combined with measurement of Young's modulus by 3D SWE may improve the diagnosis of clinically significant prostate cancer.