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Background: Magnetic resonance imaging (MRI) has advantages in the diagnosis of prostate diseases, but there is also overdiagnosis. We compensate for this with fusion imaging and elastography. In this study, we want to evaluate Elastographic Q-analysis score (EQS) combined with Prostate Imaging Reporting and Data System (PI-RADS), based on transrectal ultrasound (TRUS)/multi-parameter magnetic resonance imaging (MP-MRI) fusion biopsy in differentiating benign and malignant prostate lesions. Methods: A total of 296 patients with 318 prostate lesions who underwent TRUS/MP-MRI fusion biopsy between October 2017 and October 2019 were retrospectively analysed. The performance of the EQS was evaluated on the sites of the suspicious areas of MP-MRI. The cut-off value of EQS was obtained according to receiver operating characteristic (ROC) curve, which was used to upgrade and downgrade the PI-RADS scores. The area under the curve (AUC), integrated discrimination improvement, and decision curve analysis were used to assess the new PI-RADS performance. Results: In total, 318 MP-MRI suspicious prostate lesions (94 malignant vs. 224 benign lesions). The EQS optimal threshold was 1.85, and the AUC was 0.816. All cases were constructed three models by using 1.85 as the cut-off value: upgrade-PI-RADS, downgrade-PI-RADS and complex-PI-RADS. The AUC of PI-RADS, upgrade-PI-RADS, downgrade-PI-RADS and complex-PI-RADS were 0.869, 0.867, 0.872 and 0.873 respectively. The diagnostic coincidence rate of PI-RADS was increased from 0.667 to 0.874 by using strain elastography, among which the diagnostic rate of prostate cancer was increased from 0.557 to 0.806, and the diagnostic rate of non-prostate cancer was increased from 0.775 to 0.967. The integrated discrimination improvement indicated that downgrade-PI-RADS had a better diagnostic capability (P<0.05). The net benefit of all models, which downgrade-PI-RADS can maximize the net benefit value of patients by decision curve analysis. Conclusions: The combination of PI-RADS and EQS with TRUS/MP-MRI fusion, particularly downgrade-PI-RADS, can reduce unnecessary biopsy procedures and prevent overdiagnosis.

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BACKGROUND: An accurate knowledge of the relocation of prostate neoplasms during biopsy is of great importance to reduce the number of false negative results. Prostate neoplasms are visible in magnetic resonance images (MRI) but it is difficult for the practitioner to locate them at the time of performing a transrectal ultrasound (TRUS) guided biopsy. In this study, we present a new methodology, based on simulation, that predicts both prostate deformation and lesion migration during the biopsy. METHODS: A three-dimensional (3-D) anatomy model of the pelvic region, based on medical images, is constructed. A finite element (FE) numerical simulation of the organs motion and deformation as a result of the pressure exerted by the TRUS probe is carried out using the Code-Aster open-source computer software. Initial positions of potential prostate lesions prior to biopsy are taken into consideration and the final location of each lesion is targeted in the FE simulation output. RESULTS: Our 3-D FE simulations show that the effect of the pressure exerted by the TRUS probe is twofold as the prostate experiences both a motion and a deformation of its original shape. We targeted the relocation of five small prostate lesions when the TRUS probe exerts a force of 30 N on the rectum inner wall. The distance travelled by these lesions ranged between 5.6 and 13.9 mm. CONCLUSIONS: Our new methodology can help to predict the location of neoplasms during a prostate biopsy but further studies are needed to validate our results. Moreover, the new methodology is completely developed on open-source software, which means that its implementation would be affordable to all healthcare providers.

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The standard diagnostic procedure for prostate cancer (PCa) is transrectal ultrasound (TRUS)-guided needle biopsy. However, due to the low sensitivity of TRUS to cancerous tissue in the prostate, small yet clinically significant tumors can be missed. Magnetic resonance imaging (MRI) with TRUS fusion biopsy has recently been introduced as a way to improve the identification of clinically significant PCa in men. However, the spatial errors in coregistering the preprocedural MRI with the real-time TRUS causes false negatives. A real-time and intraprocedural imaging modality that can sensitively detect PCa tumors and, more importantly, differentiate aggressive from nonaggressive tumors could largely improve the guidance of biopsy sampling to improve diagnostic accuracy and patient risk stratification. In this work, we seek to fill this long-standing gap in clinical diagnosis of PCa via the development of a dual-modality imaging device that integrates the emerging photoacoustic imaging (PAI) technique with the established TRUS for improved guidance of PCa needle biopsy. Unlike previously published studies on the integration of TRUS with PAI capabilities, this work introduces a novel approach for integrating a focused light delivery mechanism with a clinical-grade commercial TRUS probe, while assuring much-needed ease of operation in the transrectal space. We further present the clinical potential of our device by (i) performing rigorous characterization studies, (ii) examining the acoustic and optical safety parameters for human prostate imaging, and (iii) demonstrating the structural and functional imaging capabilities using deep-tissue-mimicking phantoms. Our TRUSPA experimental studies demonstrated a field-of-view in the range of 130 to 150 degrees and spatial resolutions in the range of 300 µm to 400 µm at a soft tissue imaging depth of 5 cm.

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We consider the current and future role of transrectal ultrasound imaging in the diagnosis of prostate cancer, with a particular focus on the pre-biopsy localization and targeting role that multiparametric MRI (mpMRI) has come to occupy for some men in recent years. We draw a distinction between transrectal ultrasound (TRUS) used only as a means of distributing zonal biopsies with its employment as a means for identifying and targeting sonographically abnormal lesions. The role of AI in lesion identification and targeting will be reviewed. Comparisons of cost and availability, frequency of contraindications and diagnostic accuracy between these two imaging modalities will be drawn.

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OBJECTIVES: To identify the associated factors determining prostate cancer detection using transrectal ultrasound (TRUS)-guided prostate biopsy, within a multi-ethnic Malaysian population with prostate specific antigen (PSA) between 4.0 and 10.0 ng/ml. METHODS: Study subjects included men with initial PSA between 4.0 and 10.0 ng/ml that have undergone 12-core TRUS-guided prostate biopsy between 2009 and 2016. The prostate cancer detection rate was calculated, while potential factors associated with detection were investigated via univariable and multivariable analysis. RESULTS: A total of 617 men from a multi-ethnic background encompassing Chinese (63.5%), Malay (23.1%) and Indian (13.3%) were studied. The overall cancer detection rate was 14.3% (88/617), which included cancers detected at biopsy 1 (first biopsy), biopsy 2 (second biopsy with previous negative biopsy) and biopsy ≥ 3 (third or more biopsies with prior negative biopsies). Indian men displayed higher detection rate (23.2%) and increased risk of prostate cancer development (OR 1.85, 95% CI 1.03-3.32, p < 0.05), compared to their Malay (9.8%) and Chinese (14.0%) counterparts. Multivariable analysis revealed that ethnicity and PSA density (PSAD) are independent factors associated with overall prostate cancer detection rate. A unit increase of PSAD reflected an increase in PSA after controlling for prostate volume. CONCLUSION: Prostate cancer detection in Malaysia is comparatively lower. Our study suggests that ethnicity and PSA density should be considered when recommending first or repeat TRUS-guided prostate biopsy for prostate cancer detection in a multi-ethnic Malaysian population.

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Transrectal ultrasound (TRUS) guided biopsy of the prostate has been a standard diagnostic approach for prostate cancer over the past thirty years. Today, the role of TRUS biopsy is being challenged by transperineal (TP) prostate biopsy due to concerns over the safety and diagnostic yield of TRUS biopsy. TRUS biopsy still offers a convenient, reliable and accessible tool for diagnosing prostate cancer in the majority of patients. It continues to play a role in prostate cancer diagnosis, especially where hospital resource allocation is limited, including the public sector. TRUS biopsy has low rates of severe complications, although there remains room for improvement in current practice to improve the tolerability and reduce the incidence of post-biopsy infection.

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Recent studies show that rectal colonization with low-level ciprofloxacin-resistant Escherichia coli (ciprofloxacin minimal inhibitory concentration (MIC) above the epidemiological cutoff point, but below the clinical breakpoint for resistance), i.e., in the range > 0.06-0.5 mg/L is an independent risk factor for febrile urinary tract infection after transrectal ultrasound-guided biopsy (TRUS-B) of the prostate, adding to the other risk posed by established ciprofloxacin resistance in E. coli (MIC > 0.5 mg/L) as currently defined. We aimed to identify the quinolone that by disk diffusion best discriminates phenotypic wild-type isolates (ciprofloxacin MIC ≤ 0.06 mg/L) of E. coli from isolates with acquired resistance, and to determine the resistance genotype of each isolate. The susceptibility of 108 E. coli isolates was evaluated by ciprofloxacin, levofloxacin, moxifloxacin, nalidixic acid, and pefloxacin disk diffusion and correlated to ciprofloxacin MIC (broth microdilution) using EUCAST methodology. Genotypic resistance was identified by PCR and DNA sequencing. The specificity was 100% for all quinolone disks. Sensitivity varied substantially, as follows: ciprofloxacin 59%, levofloxacin 46%, moxifloxacin 59%, nalidixic acid 97%, and pefloxacin 97%. We suggest that in situations where low-level quinolone resistance might be of importance, such as when screening for quinolone resistance in fecal samples pre-TRUS-B, a pefloxacin (S ≥ 24 mm) or nalidixic acid (S ≥ 19 mm) disk, or a combination of the two, should be used. In a setting where plasmid-mediated resistance is prevalent, pefloxacin might perform better than nalidixic acid.

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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.

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Traditional prostate imaging is fairly limited, and only a few imaging modalities have been used for this purpose. Until today, grey scale ultrasound was the most widely used method for the characterization of the prostatic gland, however its limitations for prostate cancer (PCa) detection are well known and hence ultrasound is primarily used to localize the prostate and facilitate template prostate biopsies. In the past decade, multiparametric magnetic resonance imaging (mpMRI) of the prostate has emerged as a promising tool for the detection of PCa. Evidence has shown the value of mpMRI in the active surveillance (AS) population, given its ability to detect more aggressive disease, with data building up and supporting its use for the selection of patients suitable for surveillance. Additionally, mpMRI targeted biopsies have shown an improved detection rate of aggressive PCa when compared to regular transrectal ultrasound (TRUS) guided biopsies. Current data supports the use of mpMRI in patients considered for AS for reclassification purposes; with a negative mpMRI indicating a decreased risk of reclassification. However, a percentage of patients with negative imaging or low suspicion lesions can experience reclassification, highlighting the importance of repeat confirmatory biopsy regardless of mpMRI findings. At present, no robust data is available to recommend the substitution of regular biopsies with mpMRI in the follow-up of patients on AS and efforts are being made to determine the role of integrating genomic markers with imaging with the objective of minimizing the need of biopsies during the follow up period.

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OBJECTIVES: To examine the role of quantitative real-time elastography (RTE) features on differentiation between high-risk prostate cancer (PCA) and non-high-risk prostatic diseases in the initial transperineal biopsy setting. METHODS: We retrospectively included 103 patients with suspicious PCA who underwent both RTE and initial transperineal prostate biopsy. Patients were grouped into high-risk and non-high-risk categories according to the D'Amico's risk stratification. With computer assistance based on MATLAB programming, three features were extracted from RTE, i.e., the median hardness within peripheral gland (PG) (H med), the ratio of the median hardness within PG to that outside PG (H ratio), and the ratio of the hard area within PG to the total PG area (H ar). A multiple regression model incorporating an RTE feature, age, transrectal ultrasound finding, and prostate volume was used to identify markers for high-risk PCA. RESULTS: Forty-seven patients (45.6%) were diagnosed with PCA and 34 (33.0%) were diagnosed with high-risk PCA. Three RTE features were all statistically higher in high-risk PCA than in non-high-risk diseases (p < 0.001), indicating that the PGs in high-risk PCA patients were harder than those in non-high-risk patients. A high H ratio, high age, and low prostate volume were found to be independent markers for PCAs (p < 0.05), among which the high H ratio was the only independent marker for high-risk PCAs (p = 0.012). When predicting high-risk PCAs, the multiple regression achieved an area under receiver operating characteristic curve of 0.755, sensitivity of 73.5%, and specificity of 71.0%. CONCLUSIONS: The elevated hardness of PG identified high-risk PCA and served as an independent marker of high-risk PCA. As a non-invasive imaging modality, the RTE could be potentially used in routine clinical practice for the detection of high-risk PCA to decrease unnecessary biopsies and reduce overtreatment.

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We reviewed the role of multiparametric magnetic resonance imaging (MP-MRI) and methods of MRI guided biopsy including in-bore, cognitive fusion, and software-based fusion. MP-MRI has been developed, optimized, and studied as a means of improving prostate cancer detection beyond the standard evaluation that utilizes digital rectal examinations and serum prostate specific antigen (PSA). MP-MRI has been proven to be an excellent diagnostic imaging modality that improves prostate cancer detection and risk stratification by guiding biopsy samples. The co-registration between MRI and ultrasound has allowed for software-based fusion which enables office-based biopsy procedures while still benefiting from the detailed prostate characterization of MRI. MP-MRI/ultrasound fusion guided biopsy has been studied in detail as this technology has been developed, tested, and validated in the past decade. The imaging to pathology correlation supporting the use of MP-MRI/ultrasound fusion is well documented in the literature. As the indication for the use of prostate MP-MRI becomes more widespread, it is important to continue to evaluate the correlation between imaging and pathologic findings.

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PURPOSE: Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images. METHODS: The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance. RESULTS: We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist. CONCLUSIONS: The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures.

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Accurate prostate delineation is essential to ensure proper target coverage and normal-tissue sparing in prostate HDR brachytherapy. We have developed a prostate HDR brachytherapy technology that integrates intraoperative TRUS-based prostate contour into HDR treatment planning through TRUS-CT deformable registration (TCDR) to improve prostate contour accuracy. In a perspective study of 16 patients, we investigated the clinical feasibility as well as the performance of this TCDR-based HDR approach. We compared the performance of the TCDR-based approach with the conventional CT-based HDR in terms of prostate contour accuracy using MRI as the gold standard. For all patients, the average Dice prostate volume overlap was 91.1 ± 2.3% between the TCDR-based and the MRI-defined prostate volumes. In a subset of eight patients, inter and intro-observer reliability study was conducted among three experienced physicians (two radiation oncologists and one radiologist) for the TCDR-based HDR approach. Overall, a 10 to 40% improvement in prostate volume accuracy can be achieved with the TCDR-based approach as compared with the conventional CT-based prostate volumes. The TCDR-based prostate volumes match closely to the MRI-defined prostate volumes for all 3 observers (mean volume difference: 0.5 ± 7.2%, 1.8 ± 7.2%, and 3.5 ± 5.1%); while CT-based contours overestimated prostate volumes by 10.9 ± 28.7%, 13.7 ± 20.1%, and 44.7 ± 32.1%. This study has shown that the TCDR-based HDR brachytherapy is clinically feasible and can significantly improve prostate contour accuracy over the conventional CT-based prostate contour. We also demonstrated the reliability of the TCDR-based prostate delineation. This TCDR-based HDR approach has the potential to enable accurate dose planning and delivery, and potentially enhance prostate HDR treatment outcome.

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PURPOSE: Transperineal template prostate (TPB) biopsy has been shown to improve prostate cancer detection in men with rising PSA and previous negative TRUS biopsies. Diagnostic performance of this approach especially MR imaging and using reliable reference standard remains scantly reported. MATERIALS AND METHODS: A total of 200 patients, who were previously TRUS biopsy negative, were recruited in this study. All the participants had at least 28-core TPB under general anesthetic within 8 weeks of previous negative TRUS biopsies. In 15 men undergoing laparoscopic radical prostatectomy, prostate specimens were sectioned using custom-made molds and analyzed by experienced pathologist as a feasibility study. RESULTS: In total, 120 of 200 patients (60 %) had positive TPB biopsy results. All of these men had at least one negative biopsy from transrectal route. T2 diffusion-weighted MR imaging showed no lesion in almost one-third of these men (61/200; 30.5 %). Out of these, 33 (33/61; 54 %) showed malignancy on TPB including high-grade tumors (>Gleason 7). Out of 15 patients underwent surgery with a total of 52 lesions (mean 3.5) on radical prostatectomy histology analyses, TPB detected 36 (70 %) lesions only. Some of these lesions were Gleason 7 and more mostly located in the posterior basal area of prostate. CONCLUSIONS: Transperineal template biopsy technique is associated with significantly high prostate cancer detection rate in men with previous negative TRUS biopsies, however compared to radical prostatectomy histology map, a significant number of lesions can still be missed in the posterior and basal area of prostate.

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High-dose-rate (HDR) brachytherapy has become a popular treatment modality for localized prostate cancer. Prostate HDR treatment involves placing 10 to 20 catheters (needles) into the prostate gland, and then delivering radiation dose to the cancerous regions through these catheters. These catheters are often inserted with transrectal ultrasound (TRUS) guidance and the HDR treatment plan is based on the CT images. The main challenge for CT-based HDR planning is to accurately segment prostate volume in CT images due to the poor soft tissue contrast and additional artifacts introduced by the catheters. To overcome these limitations, we propose a novel approach to segment the prostate in CT images through TRUS-CT deformable registration based on the catheter locations. In this approach, the HDR catheters are reconstructed from the intra-operative TRUS and planning CT images, and then used as landmarks for the TRUS-CT image registration. The prostate contour generated from the TRUS images captured during the ultrasound-guided HDR procedure was used to segment the prostate on the CT images through deformable registration. We conducted two studies. A prostate-phantom study demonstrated a submillimeter accuracy of our method. A pilot study of 5 prostate-cancer patients was conducted to further test its clinical feasibility. All patients had 3 gold markers implanted in the prostate that were used to evaluate the registration accuracy, as well as previous diagnostic MR images that were used as the gold standard to assess the prostate segmentation. For the 5 patients, the mean gold-marker displacement was 1.2 mm; the prostate volume difference between our approach and the MRI was 7.2%, and the Dice volume overlap was over 91%. Our proposed method could improve prostate delineation, enable accurate dose planning and delivery, and potentially enhance prostate HDR treatment outcome.

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We developed two similar structure manipulators for medical endocavity ultrasound probes with 3 and 4 degrees of freedom (DoF). These robots allow scanning with ultrasound for 3-D imaging and enable robot-assisted image-guided procedures. Both robots use remote center of motion kinematics, characteristic of medical robots. The 4-DoF robot provides unrestricted manipulation of the endocavity probe. With the 3-DoF robot the insertion motion of the probe must be adjusted manually, but the device is simpler and may also be used to manipulate external-body probes. The robots enabled a novel surgical approach of using intraoperative image-based navigation during robot-assisted laparoscopic prostatectomy (RALP), performed with concurrent use of two robotic systems (Tandem, T-RALP). Thus far, a clinical trial for evaluation of safety and feasibility has been performed successfully on 46 patients. This paper describes the architecture and design of the robots, the two prototypes, control features related to safety, preclinical experiments, and the T-RALP procedure.

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We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 ± 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.