Search

Prostate magnetic resonance imaging (MRI) is increasingly being used to diagnose and stage prostate cancer. The Prostate Imaging and Data Reporting System (PI-RADS) version 2.1 is a consensus-based reporting system that provides a standardized and reproducible method for interpreting prostate MRI. This primer provides an overview of the PI-RADS system, focusing on its current role in clinical interpretation. It discusses the appropriate use of PI-RADS and how it should be applied by radiologists in clinical practice to assign and report PI-RADS assessments. We also discuss the changes from prior versions and published validation studies on PI-RADS accuracy and reproducibility., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: To elucidate the use of radiological studies, including nuclear medicine, and biopsy for the diagnosis and staging of prostate cancer (PCA) in clinical practice and understand the current status of PCA in Asian countries via an international survey., Materials and Methods: The Asian Prostate Imaging Working Group designed a survey questionnaire with four domains focused on prostate magnetic resonance imaging (MRI), other prostate imaging, prostate biopsy, and PCA backgrounds. The questionnaire was sent to 111 members of professional affiliations in Korea, Japan, Singapore, and Taiwan who were representatives of their working hospitals, and their responses were analyzed., Results: This survey had a response rate of 97.3% (108/111). The rates of using 3T scanners, antispasmodic agents, laxative drugs, and prostate imaging-reporting and data system reporting for prostate MRI were 21.6%-78.9%, 22.2%-84.2%, 2.3%-26.3%, and 59.5%-100%, respectively. Respondents reported using the highest b-values of 800-2000 sec/mm² and fields of view of 9-30 cm. The prostate MRI examinations per month ranged from 1 to 600, and they were most commonly indicated for biopsy-naïve patients suspected of PCA in Japan and Singapore and staging of proven PCA in Korea and Taiwan. The most commonly used radiotracers for prostate positron emission tomography are prostate-specific membrane antigen in Singapore and fluorodeoxyglucose in three other countries. The most common timing for prostate MRI was before biopsy (29.9%). Prostate-targeted biopsies were performed in 63.8% of hospitals, usually by MRI-ultrasound fusion approach. The most common presentation was localized PCA in all four countries, and it was usually treated with radical prostatectomy., Conclusion: This survey showed the diverse technical details and the availability of imaging and biopsy in the evaluation of PCA. This suggests the need for an educational program for Asian radiologists to promote standardized evidence-based imaging approaches for the diagnosis and staging of PCA., Competing Interests: Chan Kyo Kim, the editor board member of the Korean Journal of Radiology, was not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest., (Copyright © 2023 The Korean Society of Radiology.)

Prostate cancer is the most common malignancy and the third most common cause of death in Canadian men. In light of evolving diagnostic pathways for prostate cancer and the increased use of MRI, which now includes its use in men prior to biopsy, the Canadian Association of Radiologists established a Prostate MRI Working Group to produce a white paper to provide recommendations on establishing and maintaining a Prostate MRI Programme in the context of the Canadian healthcare system. The recommendations, which are based on available scientific evidence and/or expert consensus, are intended to maintain quality in image acquisition, interpretation, reporting and targeted biopsy to ensure optimal patient care. The paper covers technique, reporting, quality assurance and targeted biopsy considerations and includes appendices detailing suggested reporting templates, quality assessment tools and sample image acquisition protocols relevant to the Canadian healthcare context.

Purpose: Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by a high rate of false negatives and low diagnostic yield of the current systematic, non-targeted approaches. Developing machine learning models for accurately identifying cancerous tissue in ultrasound would help sample tissues from regions with higher cancer likelihood. A plausible approach for this purpose is to use individual ultrasound signals corresponding to a core as inputs and consider the histopathology diagnosis for the entire core as labels. However, this introduces significant amount of label noise to training and degrades the classification performance. Previously, we suggested that histopathology-reported cancer involvement can be a reasonable approximation for the label noise., Methods: Here, we propose an involvement-based label refinement (iLR) method to correct corrupted labels and improve cancer classification. The difference between predicted and true cancer involvements is used to guide the label refinement process. We further incorporate iLR into state-of-the-art methods for learning with noisy labels and predicting cancer involvement., Results: We use 258 biopsy cores from 70 patients and demonstrate that our proposed label refinement method improves the performance of multiple noise-tolerant approaches and achieves a balanced accuracy, correlation coefficient, and mean absolute error of 76.7%, 0.68, and 12.4, respectively., Conclusions: Our key contribution is to leverage a data-centric method to deal with noisy labels using histopathology reports, and improve the performance of prostate cancer diagnosis through a hierarchical training process with label refinement., (© 2022. CARS.)

Purpose: Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false negatives. Development of a machine-learning model that can reliably identify suspicious cancer regions is highly desirable. However, the models proposed to-date do not consider the uncertainty present in their output or the data to benefit clinical decision making for targeting biopsy., Methods: We propose a deep network for improved detection of prostate cancer in systematic biopsy considering both the label and model uncertainty. The architecture of our model is based on U-Net, trained with temporal enhanced ultrasound (TeUS) data. We estimate cancer detection uncertainty using test-time augmentation and test-time dropout. We then use uncertainty metrics to report the cancer probability for regions with high confidence to help the clinical decision making during the biopsy procedure., Results: Experiments for prostate cancer classification includes data from 183 prostate biopsy cores of 41 patients. We achieve an area under the curve, sensitivity, specificity and balanced accuracy of 0.79, 0.78, 0.71 and 0.75, respectively., Conclusion: Our key contribution is to automatically estimate model and label uncertainty towards enabling targeted ultrasound-guided prostate biopsy. We anticipate that such information about uncertainty can decrease the number of unnecessary biopsy with a higher rate of cancer yield., (© 2021. CARS.)

OBJECTIVE. The purpose of this study was to report on the practice patterns and challenges of performing and interpreting prostate MRI. SUBJECTS AND METHODS. An electronic survey regarding prostate MRI practice patterns and challenges was sent to members of the Society of Abdominal Radiology. RESULTS. The response rate was 15% (212/1446). Most (65%) of the respondents were academic abdominal radiologists with 1-5 (52%), 6-10 (20%), 11-20 (15%), and more than 20 (5%) years of experience in reporting prostate MRI. The numbers of prostate MRI examinations reported per week were 0-5 (43%), 6-10 (38%), 11-20 (12%), 21-30 (5%), and more than 30 (2%). Imaging was performed at 3 T (58%), 1.5 T (20%), or either (21%), and most examinations (83%) were performed without an endorectal coil. Highest b values ranged from 800 to 5000 s/mm 2 ; 1400 s/mm 2 (26%) and 1500 s/mm 2 (30%) were the most common. Most respondents (79%) acquired dynamic contrast-enhanced images with temporal resolution of less than 10 seconds. Most (71%) of the prostate MRI studies were used for fusion biopsy. PI-RADS version 2 was used by 92% of the respondents and template reporting by 80%. Challenges to performing and interpreting prostate MRI were scored on a 1-5 Likert scale (1, easy; 2, somewhat easy; 3, neutral; 4, somewhat difficult; 5, very difficult). The median scores were 2 or 3 for patient preparatory factors. Image acquisition and reporting factors were scored 1-2, except for performing spectroscopy or using an endorectal coil, both of which scored 4. Acquiring patient history scored 2 and quality factors scored 3. CONCLUSION. Most radiologists perform prostate MRI at 3 T without an endorectal coil and interpret the images using PI-RADS version 2. Challenges include obtaining quality images, acquiring feedback, and variability in the interpretation of PI-RADS scores.

Purpose: Ultrasound imaging is routinely used in prostate biopsy, which involves obtaining prostate tissue samples using a systematic, yet, non-targeted approach. This approach is blinded to individual patient intraprostatic pathology, and unfortunately, has a high rate of false negatives., Methods: In this paper, we propose a deep network for improved detection of prostate cancer in systematic biopsy. We address several challenges associated with training such network: (1) Statistical labels: Since biopsy core's pathology report only represents a statistical distribution of cancer within the core, we use multiple instance learning (MIL) networks to enable learning from ultrasound image regions associated with those data; (2) Limited labels: The number of biopsy cores are limited to at most 12 per patient. As a result, the number of samples available for training a deep network is limited. We alleviate this issue by effectively combining Independent Conditional Variational Auto Encoders (ICVAE) with MIL. We train ICVAE to learn label-invariant features of RF data, which is subsequently used to generate synthetic data for improved training of the MIL network., Results: Our in vivo study includes data from 339 prostate biopsy cores of 70 patients. We achieve an area under the curve, sensitivity, specificity, and balanced accuracy of 0.68, 0.77, 0.55 and 0.66, respectively., Conclusion: The proposed approach is generic and can be applied to several other scenarios where unlabeled data and noisy labels in training samples are present.

Purpose: To determine the relationship between parameters measured from luminal water imaging (LWI), a new magnetic resonance imaging (MRI) T 2 mapping technique, and the corresponding tissue composition in prostate., Materials and Methods: In all, 17 patients with prostate cancer were examined with a 3D multiecho spin echo sequence at 3T prior to undergoing radical prostatectomy. Maps of seven MR parameters, called N, T 2-short , T 2-long , A short , A long , geometric mean T 2 time (gmT 2 ), and luminal water fraction (LWF), were generated using nonnegative least squares (NNLS) analysis of the T 2 decay curves. MR parametric maps were correlated to digitized whole-mount histology sections. Percentage area of tissue components, including luminal space, nuclei, and cytoplasm plus stroma, was measured on the histology sections by using color-based image segmentation. Spearman's rank correlation test was used to evaluate the correlation between MR parameters and the corresponding tissue components, with particular attention paid to the correlation between LWF and percentage area of luminal space., Results: N, T 2-short , A long , gmT 2 , and LWF showed significant correlation (P < 0.05) with percentage area of luminal space and stroma plus cytoplasm. T 2-short and gmT 2 also showed significant correlation (P < 0.05) with percentage area of nuclei. Overall, the strongest correlation was observed between LWF and luminal space (Spearman's coefficient of rank correlation = 0.75, P < 0.001)., Conclusion: Results of this study show that LWF measured with MRI is strongly correlated with the fractional amount of luminal space in prostatic tissue. This result suggests that LWI can potentially be applied for evaluation of prostatic diseases in which the extent of luminal space differs between normal and abnormal tissues., Level of Evidence: 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:861-869., (© 2017 International Society for Magnetic Resonance in Medicine.)

A common challenge when performing surface-based registration of images is ensuring that the surfaces accurately represent consistent anatomical boundaries. Image segmentation may be difficult in some regions due to either poor contrast, low slice resolution, or tissue ambiguities. To address this, we present a novel non-rigid surface registration method designed to register two partial surfaces, capable of ignoring regions where the anatomical boundary is unclear. Our probabilistic approach incorporates prior geometric information in the form of a statistical shape model (SSM), and physical knowledge in the form of a finite element model (FEM). We validate results in the context of prostate interventions by registering pre-operative magnetic resonance imaging (MRI) to 3D transrectal ultrasound (TRUS). We show that both the geometric and physical priors significantly decrease net target registration error (TRE), leading to TREs of 2.35 ± 0.81 mm and 2.81 ± 0.66 mm when applied to full and partial surfaces, respectively. We investigate robustness in response to errors in segmentation, varying levels of missing data, and adjusting the tunable parameters. Results demonstrate that the proposed surface registration method is an efficient, robust, and effective solution for fusing data from multiple modalities.

Unlabelled: This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo., Methods: We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient., Results: In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework., Conclusion: Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.

The purpose of this work was to test whether fractional anisotropy (FA) can contribute to the diagnosis and grading of prostate cancer. Turbo spin echo T2-weighted (T2W) and single shot echo planar imaging diffusion tensor imaging (EPI DTI) data were collected from 13 subjects with biopsy proven prostate cancer prior to surgical removal of the gland. Rapid acquisition with relaxation enhancement (RARE) T2W and spin-echo DTI data were acquired ex-vivo from the fixed prostatectomy specimens. Digitized whole mount histology sections, examined and annotated by a pathologist, were registered to the in-vivo and ex-vivo DTI data, and the average values of apparent diffusion coefficient (ADC) and FA were calculated from ROIs encompassing normal and cancerous peripheral zone (PZ). In addition, Monte Carlo simulations were carried out to assess the dependence of the apparent FA on the ADC values for different signal to noise ratios (SNRs). ADC values were significantly lower in tumors than in normal PZ both in-vivo and ex-vivo, while the difference in FA values between tumors and normal PZ was significant only in-vivo. Paired t-test showed significant difference between in-vivo and ex-vivo FA values in tumors, but not in the normal PZ. The simulations showed that lower SNR results in an increasing overestimation of the FA values with decreasing ADC. These results suggest that the in-vivo increase in FA values in tumors is due to low SNR, rather than the presence of cancer. The results of this study suggest that FA does not contribute significantly to the diagnostic capabilities of DTI in prostate cancer., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Registration of histological slices to volumetric imaging of the prostate is an important task that can be used to optimize imaging for cancer detection. Such registration is challenging due to physical changes of the specimen during excision and fixation, and misalignment of the histological slices during preparation and digital scanning. In this work, we consider a multi-slice to volume registration method in which a stack of sparse, unaligned 2-D whole-mount histological slices is registered to a 3-D volumetric imaging of the prostate. We propose a particle filtering framework to contend with the high dimensionality of the search space and multimodal nature of the optimization. Such framework allows modeling of the uncertainty in the pose of the slices and in the imaged information, in order to derive optimal registration parameters in a Bayesian approach. Intensity-, region-, and point-based similarity metrics were incorporated into the registration algorithm to account for different imaging modalities. We demonstrate and evaluate our method on a diverse set of data that includes a synthetic volume, ex vivo and in vivo magnetic resonance imaging, and in vivo ultrasound.

Registration of histopathology to in vivo magnetic resonance imaging (MRI) of the prostate is an important task that can be used to optimize in vivo imaging for cancer detection. Such registration is challenging due to the change in volume and deformation of the prostate during excision and fixation. One approach towards this problem involves the use of an ex vivo MRI of the excised prostate specimen, followed by in vivo to ex vivo MRI registration of the prostate. We propose a novel registration method that uses a patient-specific biomechanical model acquired using magnetic resonance elastography to deform the in vivo volume and match it to the surface of the ex vivo specimen. The forces that drive the deformations are derived from a region-based energy, with the elastic potential used for regularization. The incorporation of elastography data into the registration framework allows inhomogeneous elasticity to be assigned to the in vivo volume. We show that such inhomogeneity improves the registration results by providing a physical regularization of the deformation map. The method is demonstrated and evaluated on six clinical cases.

Background Prostate MRI is used widely in clinical care for guiding tissue sampling, active surveillance, and staging. The Prostate Imaging Reporting and Data System (PI-RADS) helps provide a standardized probabilistic approach for identifying clinically significant prostate cancer. Despite widespread use, the variability in performance of prostate MRI across practices remains unknown. Purpose To estimate the positive predictive value (PPV) of PI-RADS for the detection of high-grade prostate cancer across imaging centers. Materials and Methods This retrospective cross-sectional study was compliant with the HIPAA. Twenty-six centers with members in the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel submitted data from men with suspected or biopsy-proven untreated prostate cancer. MRI scans were obtained between January 2015 and April 2018. This was followed with targeted biopsy. Only men with at least one MRI lesion assigned a PI-RADS score of 2-5 were included. Outcome was prostate cancer with Gleason score (GS) greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2). A mixed-model logistic regression with institution and individuals as random effects was used to estimate overall PPVs. The variability of observed PPV of PI-RADS across imaging centers was described by using the median and interquartile range. Results The authors evaluated 3449 men (mean age, 65 years ± 8 [standard deviation]) with 5082 lesions. Biopsy results showed 1698 cancers with GS greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2) in 2082 men. Across all centers, the estimated PPV was 35% (95% confidence interval [CI]: 27%, 43%) for a PI-RADS score greater than or equal to 3 and 49% (95% CI: 40%, 58%) for a PI-RADS score greater than or equal to 4. The interquartile ranges of PPV at these same PI-RADS score thresholds were 27%-44% and 27%-48%, respectively. Conclusion The positive predictive value of the Prostate Imaging and Reporting Data System was low and varied widely across centers. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Milot in this issue.

Purpose: This study evaluates the impact of second-opinion review of multiparametric prostate MRI for cancer detection by a multidisciplinary review board at a tertiary care center when compared with the initial community radiologist interpretation. Methods: Cases were collected retrospectively from multidisciplinary prostate MRI rounds from 2017 to 2020 at a single tertiary care center. Patients with suspected prostate cancer or on active surveillance were referred for consideration of TRUS/MRI-fusion biopsy based on community-read prostate MRIs. All MRIs were re-read by subspecialized abdominal radiologists and a PI-RADS score assigned. Targeted fusion and 8–12 core systematic biopsy was performed in patients with PIRADS ≥ 3 lesions. Cohen kappa values were used to quantify interobserver agreement. Positive predictive value (PPV) was used to determine accuracy of PI-RADS score for detection of clinically significant prostate cancer (csPCa) (ISUP Grade Group ≥ 2). Results: Three hundred and thirty-two lesions in 303 patients were reviewed and 252 lesions in 198 patients biopsied. The PI-RADS score was concordant in 60.5% of lesions, downgraded in 17.8%, and upgraded in 7.8%. Agreement between community and tertiary center interpretation was fair (κ = 0.354), with greater agreement for PI-RADS ≥ 4 (κ = 0.523) than PI-RADS ≥ 3 (κ = 0.456), and peripheral zone (κ = 0.419) than transition zone lesions (κ = 0.251). Prevalence of csPCa in biopsied lesions was 40.9%. Conclusion: There is variability in community and tertiary care center interpretation of prostate MRI in cancer detection, with higher concordance rates for higher grade and peripheral zone lesions. These differences demonstrate the added value of multidisciplinary round review and highlight the need for ongoing education and feedback. [ABSTRACT FROM AUTHOR]

The Coronavirus disease 2019 (COVID-19) pandemic has significantly affected health care systems throughout the world. A Qualtrics survey was targeted for radiologists around the world to study its effect on the operations of prostate MRI studies and biopsies. Descriptive statistics were reported. A total of 60 complete responses from five continents were included in the analysis. 70% of the responses were from academic institutions. Among all participants, the median (range) number of prostate MRI was 20 (0, 135) per week before the COVID-19 pandemic versus 10 (0, 30) during the lockdown period; the median (range) number of prostate biopsies was 4.5 (0, 60) per week before the COVID-19 versus 0 (0, 12) during the lockdown period. Among the 30% who used bowel preparation for their patients prior to MRI routinely, 11% stopped the bowel preparation due to the pandemic. 47% reported that their radiology departments faced staff disruptions, while 68% reported changes in clinic schedules in other clinical departments, particularly urology, genitourinary medical oncology, and radiation oncology. Finally, COVID-19 pandemic was found to disrupt not only the clinical prostate MRI operations but also impacted prostate MRI/biopsy research in up to 50% of institutions. The impact of this collateral damage in delaying diagnosis and treatment of prostate cancer is yet to be explored. [ABSTRACT FROM AUTHOR]

Multiparametric magnetic resonance imaging (MRI) of the prostate is a powerful and increasingly utilized imaging study for the diagnosis, staging, and surveillance of prostate cancer. With greater adoption by clinicians, it is becoming more common for incidental findings to be first detected on prostate MRI. Inadequate description of clinically significant findings may not prompt appropriate patient management, while over-reporting of indolent findings comes at increased patient anxiety, cost of workup, and iatrogenic risk. This review article aims to improve awareness, review pathophysiology, and present key imaging features of incidental findings seen on prostate MRI, ranging from common to rare and from benign to clinically significant. [ABSTRACT FROM AUTHOR]

• In the appropriate patient group, bi-parametric prostate MRI improves MRI accessibility. • The recall system is a safety net and ensures contrast is not excluded when necessary. • The recall system can save costs and scanner time while maintaining the quality of patient care. • The abbreviated MRI protocol improves throughput in centres with resource constraints. To review the efficacy of a recall system for bi-parametric non-contrast prostate MRI (bp-MRI). A bi-parametric protocol was instituted in July 2020 for all patients who had a prostate MRI requested, excluding those after treatment of prostate cancer, patients with hip prosthesis or pacemaker, and those who lived out-of-town. The protocol consisted of tri-planar T2-weighted and diffusion weighted images (DWI) (b = 50, 800 s/mm2 for ADC map; b = 1,500 s/mm2 acquired separately) in accordance with the Prostate Imaging Reporting & Data system (PI-RADS) v2.1 guidelines. After interpretation of bp-MRI exams, patients with equivocal (PI-RADS 3) lesions in peripheral zone (PZ) or any technical limitations were recalled for contrast administration. Out of 909 bp-MRI scans performed from July 2020 to April 2021, only 52 (5.7 %) were recalled, of which 46 (88.5 %) attended. Amongst these, 41/52 (78.8 %) were recalled for PZ PI-RADS 3 lesions, while the rest of 11 (21.2 %) cases were recalled for technical reasons. Mean time to subsequent recall scan was 11.6 days. On assessment of post-contrast imaging, 29/46 (63 %) cases were upgraded to PI-RADS 4 while 17/46 (37 %) remained PI-RADS 3. This system avoided contrast-agent use in 857 patients, with contrast cost savings of €64,620 (US$68,560) and 214 hours 15 minutes of scanner time was saved. This allowed 255 additional bp-MRI scans to be performed, reducing the waitlist from 1 year to 2–3 weeks. A bi-parametric prostate MRI protocol with a robust recall system for contrast administration not only saved time eliminating the marked backlog but was also more cost efficient without compromising the quality of patient care. [ABSTRACT FROM AUTHOR]

Purpose We aimed to determine the performance of multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer (PCa) in patients with prior negative transrectal ultrasound–guided prostate biopsy (TRUS-B) results. Materials and methods Between 2010 and 2013, 2,416 men underwent TRUS-B or an mpMRI or both at Vancouver General Hospital. Among these, 283 men had persistent suspicion of PCa despite prior negative TRUS-B finding. An MRI was obtained in 112, and a lesion (prostate imaging reporting and data system score ≥3) was identified in 88 cases (78%). A subsequent combined MRI-targeted and standard template biopsy was performed in 86 cases. A matching cohort of 86 patients was selected using a one-nearest neighbor method without replacement. The end points were the rate of diagnosis of PCa and significant PCa (sPCa) (Gleason>6, or>2 cores, or>50% of any core). Results MRI-targeted TRUS-B detected PCa and sPCa in 36 (41.9%) and 30 (34.9%) men when compared with 19 (22.1%) and 14 (16.3%), respectively, men without mpMRI ( P = 0.005 for both). In 9 cases (10.4%), MRI-targeted TRUS-B detected sPCa that was missed on standard cores. sPCa was present in 6 cases (6.9%) on standard cores but not the targeted cores. Multivariate analysis revealed that prostate imaging reporting and data system score and prostate-specific antigen density>0.15 ng/ml 2 were statistically significant predictors of significant cancer detection (odds ratio = 14.93, P <0.001 and odds ratio = 6.19, P = 0.02, respectively). Conclusion In patients with prior negative TRUS-B finding, MRI-targeted TRUS-B improves the detection rate of all PCa and sPCa. [ABSTRACT FROM AUTHOR]