Three-dimensional greyscale transrectal ultrasound-guidance and biopsy core preembedding for detection of prostate cancer:Dutch clinical cohort study

Background: To overcome the limitations regarding two dimensional (2D) greyscale (GS) transrectal ultrasound (TRUS)-guided biopsy in prostate cancer (PCa) detection and tissue packaging in biopsy processing, there is an ongoing focus on new imaging and pathology techniques. A three-dimensional (3D) model of the prostate with biopsy needle guidance can be generate by the Navigo™ workstation (UC-care, Israel). The SmartBX™ system (UC-care, Israel) provides a prostate biopsy core preembedding method. The aim of this study was to compare cancer detection rates between the 3D TRUS-guidance and preembedding method with conventional 2D GS TRUS-guidance among patients undergoing prostate biopsies. Methods: We retrospectively analyzed the records of all patients who underwent prostate biopsies for PCa detection at our institution from 2007 to 2016. The cohort was divided into a 2D GS TRUS-guidance cohort (from 2007 to 2013, n = 1149) and a 3D GS TRUS-guidance with preembedding cohort (from 2013 to 2016, n = 469). Effect of 3D GS TRUS-guidance with preembedding on detection rate of PCa and clinically significant PCa (Gleason score ≥ 7 or &gt; 2 biopsy cores with a Gleason score 6) was compared to 2D GS TRUS-guidance using regression models. Results: Detection rate of PCa and clinically significant PCa was 39.0 and 24.9% in the 3D GS TRUS cohort compared to 33.5 and 19.0% in the 2D GS TRUS cohort, respectively. On multivariate regression analysis the use of 3D GS TRUS-guidance with preembedding was associated with a significant increase in detection rate of PCa (aOR = 1.33; 95% CI: 1.03-1.72) and clinically significant PCa (aOR = 1.47; 95% CI: 1.09-1.98). Conclusion: Our results suggest that 3D GS TRUS-guidance with biopsy core preembedding improves PCa and clinically significant PCa detection compared to 2D GS TRUS-guidance. Additional studies are needed to justify the application of these systems in clinical practice.</p

The ladies trial: laparoscopic peritoneal lavage or resection for purulent peritonitisA and Hartmann's procedure or resection with primary anastomosis for purulent or faecal peritonitisB in perforated diverticulitis (NTR2037)

Background: Recently, excellent results are reported on laparoscopic lavage in patients with purulent perforated diverticulitis as an alternative for sigmoidectomy and ostomy. The objective of this study is to determine whether LaparOscopic LAvage and drainage is a safe and effective treatment for patients with purulent peritonitis (LOLA-arm) and to determine the optimal resectional strategy in patients with a purulent or faecal peritonitis (DIVA-arm: perforated DIVerticulitis: sigmoidresection with or without Anastomosis). Methods/Design: In this multicentre randomised trial all patients with perforated diverticulitis are included. Upon laparoscopy, patients with purulent peritonitis are treated with laparoscopic lavage and drainage, Hartmann's procedure or sigmoidectomy with primary anastomosis in a ratio of 2:1:1 (LOLA-arm). Patients with faecal peritonitis will be randomised 1:1 between Hartmann's procedure and resection with primary anastomosis (DIVA-arm). The primary combined endpoint of the LOLA-arm is major morbidity and mortality. A sample size of 132:66:66 patients will be able to detect a difference in the primary endpoint from 25% in resectional groups compared to 10% in the laparoscopic lavage group (two sided alpha = 5%, power = 90%). Endpoint of the DIVA-arm is stoma free survival one year after initial surgery. In this arm 212 patients are needed to significantly demonstrate a difference of 30% (log rank test two sided alpha = 5% and powe

Machine learning for multiparametric ultrasound classification of prostate cancer using B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

The diagnosis of prostate cancer (PCa) is still based on systematic biopsy, but is increasingly developing towards an imaging-driven approach. In particular, multiparametric magnetic resonance imaging (MRI) is receiving increasing attention over the last few years. In light of MRI-related issues concerning costs, practicality, and availability, we investigate a multiparametric ultrasound (mpUS) approach. We propose and test a machine-learning-based strategy that automatically combines B-mode ultrasound, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) features. To this end, automatic zonal segmentation by deep learning, model-based feature estimation (related to contrast-agent perfusion and dispersion), radiomic feature extraction, and a random-forest-based pixel-wise classification were combined. The method was trained and validated against histopathologically-confirmed benign and malignant regions of interest in 48 PCa patients, in a leave-one-patient-out cross-correlation fashion. The mpUS classification algorithm yielded a region-wise area under the Receiver Operating Characteristics (ROC) curve of 0.75 and 0.90 for PCa and significant (i.e., Gleason ≥4+3) PCa, respectively. In comparison, the best-performing single parameter (i.e., DCE-US-based contrast velocity) reached a performance of 0.69 and 0.76 in terms of the ROC curve area. In particular the combination of perfusion-, dispersion-, and elasticity-related features were favored in the classification. Even though validation on a larger patient group is required, we have demonstrated the technical feasibility of automatic mpUS for PCa localization. Further development of mpUS might lead to a valuable clinical option for robust, ultrasound-driven PCa diagnosis

Synthetic Elastography from B-Mode ultrasound through Deep Learning

Tissue elasticity can be locally estimated using shear-wave elastography (SWE), an advanced technique that measures the speed of laterally-traveling shear waves induced by a sequence of acoustic radiation force "push" pulses. However, SWE is not available on all ultrasound machines due to e.g. power, equipment, and procedural requirements; in particular, wireless devices would face challenges delivering the required power. Here, we propose a fully-convolutional deep neural network for the synthesis of an SWE image given the corresponding B-mode (side-by-side-view) image. Fifty patients diagnosed with prostate cancer underwent a transrectal SWE examination with SWE imaging regions chosen such that they covered the entire or parts of the prostate. The network was trained with the images of 40 patients and subsequently tested using 30 image planes from the remaining 10 patients. The neural network was able to accurately map the B-mode images to sSWE images with a pixel-wise mean absolute error of 4.8 kPa in terms of Young's modulus. Qualitatively, tumour sites characterized by high stiffness were mostly preserved (as validated by histopathology). Despite the need for further validation, our results already suggest that deep learning is a viable way to retrieve elasticity values from conventional B-mode images and can potentially provide valuable information for cancer diagnosis using devices on which no SWE imaging is available

Machine learning for multiparametric ultrasound classification of prostate cancer using B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

\u3cp\u3eThe diagnosis of prostate cancer (PCa) is still based on systematic biopsy, but is increasingly developing towards an imaging-driven approach. In particular, multiparametric magnetic resonance imaging (MRI) is receiving increasing attention over the last few years. In light of MRI-related issues concerning costs, practicality, and availability, we investigate a multiparametric ultrasound (mpUS) approach. We propose and test a machine-learning-based strategy that automatically combines B-mode ultrasound, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) features. To this end, automatic zonal segmentation by deep learning, model-based feature estimation (related to contrast-agent perfusion and dispersion), radiomic feature extraction, and a random-forest-based pixel-wise classification were combined. The method was trained and validated against histopathologically-confirmed benign and malignant regions of interest in 48 PCa patients, in a leave-one-patient-out cross-correlation fashion. The mpUS classification algorithm yielded a region-wise area under the Receiver Operating Characteristics (ROC) curve of 0.75 and 0.90 for PCa and significant (i.e., Gleason ≥4+3) PCa, respectively. In comparison, the best-performing single parameter (i.e., DCE-US-based contrast velocity) reached a performance of 0.69 and 0.76 in terms of the ROC curve area. In particular the combination of perfusion-, dispersion-, and elasticity-related features were favored in the classification. Even though validation on a larger patient group is required, we have demonstrated the technical feasibility of automatic mpUS for PCa localization. Further development of mpUS might lead to a valuable clinical option for robust, ultrasound-driven PCa diagnosis.\u3c/p\u3

Synthetic elastography from B-mode ultrasound through deep learning

\u3cp\u3eTissue elasticity can be locally estimated using shear-wave elastography (SWE), an advanced technique that measures the speed of laterally-traveling shear waves induced by a sequence of acoustic radiation force push pulses. However, SWE is not available on all ultrasound machines due to e.g. power, equipment, and procedural requirements; in particular, wireless devices would face challenges delivering the required power. Here, we propose a fully-convolutional deep neural network for the synthesis of an SWE image given the corresponding B-mode (side-by-side-view) image. Fifty patients diagnosed with prostate cancer underwent a transrectal SWE examination with SWE imaging regions chosen such that they covered the entire or parts of the prostate. The network was trained with the images of 40 patients and subsequently tested using 30 image planes from the remaining 10 patients. The neural network was able to accurately map the B-mode images to sSWE images with a pixel-wise mean absolute error of 4.8 kPa in terms of Young's modulus. Qualitatively, tumour sites characterized by high stiffness were mostly preserved (as validated by histopathology). Despite the need for further validation, our results already suggest that deep learning is a viable way to retrieve elasticity values from conventional B-mode images and can potentially provide valuable information for cancer diagnosis using devices on which no SWE imaging is available.\u3c/p\u3

Prediction of prostate cancer: external validation of the ERSPC risk calculator in a contemporary Dutch clinical cohort

The validity of prediction models needs external validation to assess their value beyond the original development setting. To report the diagnostic accuracy of the European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator (RC)3 and RC4 in a contemporary Dutch clinical cohort. We retrospectively identified all men who underwent prostate biopsy (PBx) in the Jeroen Bosch Hospital, The Netherlands, between 2007 and 2016. Patients were included if they met ERSPC RC requirements of age (50-80 yr), prostate-specific antigen (PSA) (0.4-50 ng/ml), and prostate volume (10-150ml). The probability of a positive biopsy for prostate cancer (PCa) and significant PCa (Gleason score ≥7 and/or higher than T2b) were calculated and compared with PBx pathology results. Evaluation was performed by calibration, discrimination, and clinical usefulness using calibration plots, area under the receiver operating characteristic curves (AUCs), and decision curve analyses (DCAs), respectively. A total of 2270 PBx sessions were eligible for final analysis. Discriminative ability of RC3 (AUC) was 0.78 and 0.90 for any PCa and significant PCa, respectively. For RC4 the calculated AUCs were 0.62 (any PCa) and 0.76 (significant PCa). The calibration plots of RC3 showed good results for both any PCa risk and significant PCa risk. In the repeat PBx group, RC4 tended to underestimate outcomes for PCa and showed moderate calibration for significant PCa. DCA showed an overall net benefit compared with PSA and digital rectal examination (DRE) alone. Limitations of this study are its retrospective single-institution design, retrospectively assessed DRE outcomes, no time restrictions between the first and repeat biopsy sessions, and no anterior sampling in the repeat PBx protocol. The ERSPC RCs performed well in a contemporary clinical setting. Most pronounced in the biopsy-naive group, both RCs should be favoured over a PSA plus DRE-based stratification in the decision whether or not to perform PBx. We looked at the ability of the existing European Randomized Study of Screening for Prostate Cancer risk calculator (RC), using different clinical data to predict the presence of prostate cancer in Dutch men. The RC performed well and should be favoured in the decision of whether or not to perform prostate biopsies over the conventional diagnostic pathwa

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

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

Abstract 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 NCT0309123