PI-RADS v2 - What you need to know

Prostate cancer is the second most prevalent cancer in men worldwide and its incidence is expected to double by 2030. Multi-parametric magnetic resonance imaging (MRI) incorporating anatomical and functional imaging has now been validated as a means of detecting and characterising prostate tumours and can aid in risk stratification and treatment selection. The European Society of Urogenital Radiology (ESUR) in 2012 established the Prostate Imaging-Reporting and Data System (PI-RADS) guidelines aimed at standardising the acquisition, interpretation and reporting of prostate MRI. Subsequent experience and technical developments have highlighted some limitations, and a joint steering committee formed by the American College of Radiology, ESUR, and the AdMeTech Foundation have recently announced an updated version of the proposals. We summarise the main proposals of PI-RADS version 2, explore the evidence behind the recommendations, and highlight key differences for the benefit of those already familiar with the original.TB is supported the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.crad.2015.06.09

Update on the ICUD-SIU consultation on multi-parametric magnetic resonance imaging in localised prostate cancer

Introduction: Prostate cancer (PCa) imaging is a rapidly evolving field. Dramatic improvements in prostate MRI during the last decade will probably change the accuracy of diagnosis. This chapter reviews recent current evidence about MRI diagnostic performance and impact on PCa management. Materials and methods: The International Consultation on Urological Diseases nominated a committee to review the literature on prostate MRI. A search of the PubMed database was conducted to identify articles focussed on MP-MRI detection and staging protocols, reporting and scoring systems, the role of MP-MRI in diagnosing PCa prior to biopsy, in active surveillance, in focal therapy and in detecting local recurrence after treatment. Results: Differences in opinion were reported in the use of the strength of magnets [1.5 Tesla (T) vs. 3T] and coils. More agreement was found regarding the choice of pulse sequences; diffusion-weighted MRI (DW-MRI), dynamic contrast-enhanced MRI (DCE MRI), and/or MR spectroscopy imaging (MRSI) are recommended in addition to conventional T2-weighted anatomical sequences. In 2015, the Prostate Imaging Reporting and Data System (PI-RADS version 2) was described to standardize image acquisition and interpretation. MP-MRI improves detection of clinically significant PCa (csPCa) in the repeat biopsy setting or before the confirmatory biopsy in patients considering active surveillance. It is useful to guide focal treatment and to detect local recurrences after treatment. Its role in biopsy-naive patients or during the course of active surveillance remains debated. Conclusion: MP-MRI is increasingly used to improve detection of csPCa and for the selection of a suitable therapeutic approach

The Role of Magnetic Resonance Imaging and Positron Emission Tomography/Computed Tomography in the Primary Staging of Newly Diagnosed Prostate Cancer: A Systematic Review of the Literature

Context Management of newly diagnosed prostate cancer (PCa) is guided in part by accurate clinical staging. The role of imaging, including magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT), in initial staging remains controversial. Objective To systematically review the studies of MRI and/or PET/CT in the staging of newly diagnosed PCa with respect to tumor (T), nodal (N), and metastatic (M) staging (TNM staging). Evidence acquisition We performed a systematic review of the literature using MEDLINE and Web of Science databases between 2012 and 2020 following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement guidelines. Evidence synthesis A total of 139 studies (83 on T, 47 on N, and 24 on M status) were included. Ninety-nine (71%) were retrospective, 39 (28%) were prospective, and one was a randomized controlled trial (RCT). Most studies on T staging examined MRI, while PET/CT was used primarily for N and M staging. Sensitivity for the detection of extraprostatic extension, seminal vesicle invasion, or lymph node invasion ranged widely. When imaging was incorporated into existing risk tools, gain in accuracy was observed in some studies, although these findings have not been replicated. For M staging, most favorable results were reported for prostate-specific membrane antigen (PSMA) PET/CT, which demonstrated significantly better performance than conventional imaging. Conclusions A variety of studies on modern imaging techniques for TNM staging in newly diagnosed PCa exist. For T and N staging, reported sensitivity of imaging modalities such as MRI or PET/CT varied widely due to data heterogeneity, small sample size, and low event rates resulting in large confidence intervals and a high level of uncertainty. Therefore, uniformity in data presentation and standardization on this topic are needed. The most promising technique for M staging, which was evaluated recently in an RCT, is PSMA-PET/CT. Patient summary We performed a systematic review of currently available imaging modalities to stage newly diagnosed prostate cancer. With respect to local tumor and lymph node assessment, performance of imaging ranged widely. However, prostate-specific membrane antigen positron emission tomography/computed tomography showed favorable results for the detection of distant metastases

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.Comment: This article will be presented at MICCAI (Medical Image Computing and Computer-Assisted Intervention) 201

Ct angiography evaluation of the renal vascular pathologies: a pictorial review

The emergence of CT angiography (CTA) has a groundbreaking impact on the evaluation of renal vessels and is gradually replacing the conventional catheter angiography as the standard imaging procedure. In this review, we aimed to describe the renal CTA technique and imaging findings of several renal arterial (i.e. atherosclerosis, fibromuscular dysplasia, aneurysms of the renal arteries, dissection, vasculitidis, follow-up of patients with renal arterial stent) and venous (i.e. nut-cracker syndrome, pelvic congestion syndrome) pathologies

Assessing and testing anomaly detection for finding prostate cancer in spatially registered multi-parametric MRI

BackgroundEvaluating and displaying prostate cancer through non-invasive imagery such as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research quantitatively applied supervised target algorithms using vectoral tumor signatures to spatially registered T1, T2, Diffusion, and Dynamic Contrast Enhancement images. This is the first study to apply the Reed-Xiaoli (RX) multi-spectral anomaly detector (unsupervised target detector) to prostate cancer, which searches for voxels that depart from the background normal tissue, and detects aberrant voxels, presumably tumors.MethodsMP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images, or seven components) were prospectively collected from 26 patients and then resized, translated, and stitched to form spatially registered multi-parametric cubes. The covariance matrix (CM) and mean μ were computed from background normal tissue. For RX, noise was reduced for the CM by filtering out principal components (PC), regularization, and elliptical envelope minimization. The RX images were compared to images derived from the threshold Adaptive Cosine Estimator (ACE) and quantitative color analysis. Receiver Operator Characteristic (ROC) curves were used for RX and reference images. To quantitatively assess algorithm performance, the Area Under the Curve (AUC) and the Youden Index (YI) points for the ROC curves were computed.ResultsThe patient average for the AUC and [YI] from ROC curves for RX from filtering 3 and 4 PC was 0.734[0.706] and 0.727[0.703], respectively, relative to the ACE images. The AUC[YI] for RX from modified Regularization was 0.638[0.639], Regularization 0.716[0.690], elliptical envelope minimization 0.544[0.597], and unprocessed CM 0.581[0.608] using the ACE images as Reference Image. The AUC[YI] for RX from filtering 3 and 4 PC was 0.742[0.711] and 0.740[0.708], respectively, relative to the quantitative color images. The AUC[YI] for RX from modified Regularization was 0.643[0.648], Regularization 0.722[0.695], elliptical envelope minimization 0.508[0.605], and unprocessed CM 0.569[0.615] using the color images as Reference Image. All standard errors were less than 0.020.ConclusionsThis first study of spatially registered MP-MRI applied anomaly detection using RX, an unsupervised target detection algorithm for prostate cancer. For RX, filtering out PC and applying Regularization achieved higher AUC and YI using ACE and color images as references than unprocessed CM, modified Regularization, and elliptical envelope minimization

MRI robot for prostate focal laser ablation : An ex vivo study in human prostate

Purpose: A novel grid-template-mimicking MR-compatible robot was developed for in-gantry MRI-guided focal laser ablation of prostate cancer. Method: A substantially compact robot was designed and prototyped to meet in-gantry lithotomy ergonomics and allow for accommodation in the perineum. The controller software was reconfigured and integrated with the custom-designed navigation and multi-focal ablation software. Three experiments were conducted: (1) free space accuracy test; (2) phantom study under computed tomography (CT) guidance for image-guided accuracy test and overall workflow; and (3) magnetic resonance imaging (MRI)-guided focal laser ablation of an ex vivo prostate. The free space accuracy study included five targets that were selected across the workspace. The robot was then commanded five times to each target. The phantom study used a gel phantom made with color changing thermos-chromic ink, and four spherical metal fiducials were deployed with the robot. Then, laser ablation was applied, and the phantom was sliced for gross observation. For an MR-guided ex vivo test, a prostate from a donor who died of prostate cancer was obtained and multi-focally ablated using the system within the MRI gantry. The tissue was sliced after ablation for validation. Results: free-space accuracy was 0.38 ± 0.27 mm. The overall system targeting accuracy under CT guidance (including robot, registration, and insertion error) was 2.17 ± 0.47 mm. The planned ablation zone was successfully covered in both acrylamide gel phantom and in human prostate tissue. Conclusions: The new robot can accurately facilitate fiber targeting for MR-guided focal laser ablation of targetable prostate cancer

Factors Influencing Variability in the Performance of Multiparametric Magnetic Resonance Imaging in Detecting Clinically Significant Prostate Cancer: A Systematic Literature Review

CONTEXT: There is a lack of comprehensive data regarding the factors that influence the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) to detect and localize clinically significant prostate cancer (csPCa). OBJECTIVE: To systematically review the current literature assessing the factors influencing the variability of mpMRI performance in csPCa diagnosis. EVIDENCE ACQUISITION: A computerized bibliographic search of Medline/PubMed database was performed for all studies assessing magnetic field strength, use of an endorectal coil, assessment system used by radiologists and inter-reader variability, experience of radiologists and urologists, use of a contrast agent, and use of computer-aided diagnosis (CAD) tools in relation to mpMRI diagnostic accuracy. EVIDENCE SYNTHESIS: A total of 77 articles were included. Both radiologists' reading experience and urologists'/radiologists' biopsy experience were the main factors that influenced diagnostic accuracy. Therefore, it is mandatory to indicate the experience of the interpreting radiologists and biopsy-performing urologists to support the reliability of the findings. The most recent Prostate Imaging Reporting and Data System (PI-RADS) guidelines are recommended for use as the main assessment system for csPCa, given the simplified and standardized approach as well as its particular added value for less experienced radiologists. Biparametric MRI had similar accuracy to mpMRI; however, biparametric MRI performed better with experienced readers. The limited data available suggest that the combination of CAD and radiologist readings may influence diagnostic accuracy positively. CONCLUSIONS: Multiple factors affect the accuracy of mpMRI and MRI-targeted biopsy to detect and localize csPCa. The high heterogeneity across the studies underlines the need to define the experience of radiologists and urologists, implement quality control, and adhere to the most recent PI-RADS assessment guidelines. Further research is needed to clarify which factors impact the accuracy of the MRI pathway and how. PATIENT SUMMARY: We systematically reported the factors influencing the accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting clinically significant prostate cancer (csPCa). These factors are significantly related to each other, with the experience of the radiologists being the dominating factor. In order to deliver the benefits of mpMRI to diagnose csPCa, it is necessary to develop expertise for both radiologists and urologists, implement quality control, and adhere to the most recent Prostate Imaging Reporting and Data System assessment guidelines

Intra- and interreader reproducibility of PI-RADSv2: A multireader study

Background: The Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) has been in use since 2015; while interreader reproducibility has been studied, there has been a paucity of studies investigating the intrareader reproducibility of PI-RADSv2. Purpose: To evaluate both intra- and interreader reproducibility of PI-RADSv2 in the assessment of intraprostatic lesions using multiparametric magnetic resonance imaging (mpMRI). Study Type: Retrospective. Population/Subjects: In all, 102 consecutive biopsy-naïve patients who underwent prostate MRI and subsequent MR/transrectal ultrasonography (MR/TRUS)-guided biopsy. Field Strength/Sequences: Prostate mpMRI at 3T using endorectal with phased array surface coils (TW MRI, DW MRI with ADC maps and b2000 DW MRI, DCE MRI). Assessment: Previously detected and biopsied lesions were scored by four readers from four different institutions using PI-RADSv2. Readers scored lesions during two readout rounds with a 4-week washout period. Statistical Tests: Kappa (κ) statistics and specific agreement (Po) were calculated to quantify intra- and interreader reproducibility of PI-RADSv2 scoring. Lesion measurement agreement was calculated using the intraclass correlation coefficient (ICC). Results: Overall intrareader reproducibility was moderate to substantial (κ = 0.43–0.67, Po = 0.60–0.77), while overall interreader reproducibility was poor to moderate (κ = 0.24, Po = 46). Readers with more experience showed greater interreader reproducibility than readers with intermediate experience in the whole prostate (P = 0.026) and peripheral zone (P = 0.002). Sequence-specific interreader agreement for all readers was similar to the overall PI-RADSv2 score, with κ = 0.24, 0.24, and 0.23 and Po = 0.47, 0.44, and 0.54 in T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE), respectively. Overall intrareader and interreader ICC for lesion measurement was 0.82 and 0.71, respectively. Data Conclusion: PI-RADSv2 provides moderate intrareader reproducibility, poor interreader reproducibility, and moderate interreader lesion measurement reproducibility. These findings suggest a need for more standardized reader training in prostate MRI. Level of Evidence: 2. Technical Efficacy: Stage 2