Metabolites in Serum Small Extracellular Vesicles Instead of Small Extracellular Vesicles-depleted Serum Have Better Diagnostic Value for Cancers at Early Stage

Serum is one of the most commonly used biofluids for biomarker exploration. Some studies examine serum directly, while others focus on specific components like small extracellular vesicles (sEVs), which are lipid-bilayer encapsulated particles carrying a variety of molecular cargos. However, the diagnostic value of serum sEVs versus sEVs-depleted fractions (EV-free serum) for early cancer detection are unclear. In the study, size exclusion chromatography (SEC) is employed to separate serum from prostate cancer (PCa) suspects into sEVs-enriched fractions (EV) and EV-free serum. Metabolic fingerprints are obtained using ferric nanoparticle-assisted laser ablation/ionization mass spectroscopy (FeNPALDI-MS), revealing heterogeneity in metabolic composition. Eleven key metabolites are identified in EV and two in EV-free serum that differentiate PCa from benign prostatic hyperplasia. The EV key metabolites showed higher diagnostic value in PCa patients with an area under the curve (AUC) of 0.76, p < 0.05 and improved diagnostic efficacy when combined with the prostate-specific antigen (PSA, AUC = 0.85).

HRS Improves Active Surveillance for Prostate Cancer by Timely Identification of Progression

RATIONALE AND OBJECTIVES

Active surveillance (AS) is the preferred management strategy for low-risk prostate cancer. This study aimed to evaluate the impact of Habitat Risk Score (HRS), an automated approach for mpMRI analysis, for early detection of progressors in a prospective AS clinical trial (MAST NCT02242773).

MATERIALS AND METHODS

The MAST protocol includes Confirmatory mpMRI ultrasound fusion (MRI-US) biopsy and yearly surveillance MRI-US biopsies for up to 3 years. Clinical and mpMRI data from patients that progressed based on protocol criteria at years 1-3 were reviewed. Patients were classified as "MRI/HRS Progressors" if the PI-RADS lesion(s) had been targeted throughout the surveillance and resulted in positive biopsies, or as "Missed Progressors" if the lesion(s) were not identified by PI-RADS ("PI-RADS Miss") or were missed by the biopsy ("Needle Miss"). HRS maps were generated for each patient and evaluated for association with histopathological progression.

RESULTS

Of the 34 patients, 15 were classified as "MRI/HRS Progressors" and 19 as "Missed Progressors" (12 "PI-RADS Miss", seven "Needle Miss"). In all cases, HRS confirmed the PI-RADS assessment. In the "PI-RADS Miss" group, HRS identified the lesions in all patients that were not targeted by biopsy and resulted in patient reclassification. HRS volumes showed clear association with tumor evolution both in terms of volume and aggressiveness over time.

CONCLUSION

HRS volumes can serve as a quantitative biomarker for early detection of progression and lead to timely conversion to treatment, thereby improving patient outcomes and reducing the burden of unnecessary surveillance.

Imaging Findings in Acute Traumatic Brain Injury: a National Institute of Neurological Disorders and Stroke Common Data Element-Based Pictorial Review and Analysis of Over 4000 Admission Brain Computed Tomography Scans from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study

In 2010, the National Institute of Neurological Disorders and Stroke (NINDS) created a set of common data elements (CDEs) to help standardize the assessment and reporting of imaging findings in traumatic brain injury (TBI). However, as opposed to other standardized radiology reporting systems, a visual overview and data to support the proposed standardized lexicon are lacking. We used over 4000 admission computed tomography (CT) scans of patients with TBI from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study to develop an extensive pictorial overview of the NINDS TBI CDEs, with visual examples and background information on individual pathoanatomical lesion types, up to the level of supplemental and emerging information (e.g., location and estimated volumes). We documented the frequency of lesion occurrence, aiming to quantify the relative importance of different CDEs for characterizing TBI, and performed a critical appraisal of our experience with the intent to inform updating of the CDEs. In addition, we investigated the co-occurrence and clustering of lesion types and the distribution of six CT classification systems. The median age of the 4087 patients in our dataset was 50 years (interquartile range, 29-66; range, 0-96), including 238 patients under 18 years old (5.8%). Traumatic subarachnoid hemorrhage (45.3%), skull fractures (37.4%), contusions (31.3%), and acute subdural hematoma (28.9%) were the most frequently occurring CT findings in acute TBI. The ranking of these lesions was the same in patients with mild TBI (baseline Glasgow Coma Scale [GCS] score 13-15) compared with those with moderate-severe TBI (baseline GCS score 3-12), but the frequency of occurrence was up to three times higher in moderate-severe TBI. In most TBI patients with CT abnormalities, there was co-occurrence and clustering of different lesion types, with significant differences between mild and moderate-severe TBI patients. More specifically, lesion patterns were more complex in moderate-severe TBI patients, with more co-existing lesions and more frequent signs of mass effect. These patients also had higher and more heterogeneous CT score distributions, associated with worse predicted outcomes. The critical appraisal of the NINDS CDEs was highly positive, but revealed that full assessment can be time consuming, that some CDEs had very low frequencies, and identified a few redundancies and ambiguity in some definitions. Whilst primarily developed for research, implementation of CDE templates for use in clinical practice is advocated, but this will require development of an abbreviated version. In conclusion, with this study, we provide an educational resource for clinicians and researchers to help assess, characterize, and report the vast and complex spectrum of imaging findings in patients with TBI. Our data provides a comprehensive overview of the contemporary landscape of TBI imaging pathology in Europe, and the findings can serve as empirical evidence for updating the current NINDS radiologic CDEs to version 3.0.

Technical note: hydrogel-based mimics of prostate cancer with matched relaxation, diffusion and kurtosis for validating multi-parametric MRI

BACKGROUND

Protocol standardization and optimization for clinical translation of emerging quantitative multiparametric (mp)MRI biomarkers of high-risk prostate cancer requires imaging references that mimic realistic tissue value combinations for bias assessment in derived relaxation and diffusion parameters.

PURPOSE

This work aimed to develop a novel class of hydrogel-based synthetic materials with simultaneously controlled quantitative relaxation, diffusion, and kurtosis parameters that mimic in vivo prostate value combinations in the same spatial compartment and allow stable assemblies of adjacent structures.

METHODS

A set of materials with tunable T2, diffusion, and kurtosis were assembled to create quantitative biomimetic (mp)MRI references. T2 was controlled with variable agarose concentration, monoexponential diffusion by polyvinylpyrrolidone (PVP), and kurtosis by addition of lamellar vesicles. The materials were mechanically stabilized by UV cross-linked polyacrylamide gels (PAG) to allow biomimetic morphologies. The reference T2 were measured on a 3T scanner using multi-echo CPMG, and diffusion kurtosis-with multi-b DWI.

RESULTS

Agarose concentration controls T2 values which are nominally independent of PVP or vesicle concentration. For agarose PVP hydrogels, monoexponential diffusion values are a function of PVP concentration and independent of agarose concentration. Compared to free vesicles, for agarose-PAG combined with vesicles, diffusion was predominantly controlled by vesicles and PAG, while kurtosis was affected by agarose and vesicle concentration. Both hydrogel classes achieved image voxel parameter values (T2, Da, Ka) for relaxation (T2: 65-255 ms), apparent diffusion (Da: 0.8-1.7 μm2/ms), and kurtosis (Ka: 0.5-1.25) within the target literature ranges for normal prostate zones and cancer lesions. Relaxation and diffusion parameters remained stable for over 6 months for layered material assemblies.

CONCLUSION

A stable biomimetic mpMR reference based on hydrogels has been developed with a range of multi-compartment diffusion and relaxation parameter combinations observed in cancerous and healthy prostate tissue.

Comprehensive Assessment of MRI-based Artificial Intelligence Frameworks Performance in the Detection, Segmentation, and Classification of Prostate Lesions Using Open-Source Databases

Numerous MRI-based artificial intelligence (AI) frameworks have been designed for prostate cancer lesion detection, segmentation, and classification via MRI as a result of intrareader and interreader variability that is inherent to traditional interpretation. Open-source data sets have been released with the intention of providing freely available MRIs for the testing of diverse AI frameworks in automated or semiautomated tasks. Here, an in-depth assessment of the performance of MRI-based AI frameworks for detecting, segmenting, and classifying prostate lesions using open-source databases was performed. Among 17 data sets, 12 were specific to prostate cancer detection/classification, with 52 studies meeting the inclusion criteria.

Comparison of a Deep Learning-Accelerated vs. Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate

BACKGROUND

Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI).

PURPOSE

To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer.

STUDY TYPE

Retrospective.

POPULATION

Eighty consecutive men, mean age 66 years (47-84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow-up included prostate biopsy or stability of prostate-specific antigen (PSA) for 1 year.

FIELD STRENGTH AND SEQUENCES

A 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL-T2), 3-fold deep learning-accelerated axial and coronal T2-weighted sequence (DL-T2), diffusion weighted imaging (DWI) with b = 50 sec/mm2 , 1000 sec/mm2 , calculated b = 1500 sec/mm2 .

ASSESSMENT

CL-bpMRI and DL-bpMRI including the same conventional diffusion-weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer-assisted detection algorithm (DL-CAD). The readers evaluated image quality using a 4-point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI-RADS) v2.1. DL-CAD identified and assigned lesions of PI-RADS 3 or greater.

STATISTICAL TESTS

Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test.

SIGNIFICANCE

P = 0.05.

RESULTS

Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL-T2, DL-T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient-based analysis, the reader results of AUC are (CL-bpMRI, DL-bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL-CAD (CL-bpMRI, DL-bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48).

CONCLUSION

Deep learning-accelerated T2-weighted imaging may potentially be used to decrease acquisition time for bpMRI.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Imaging features of the PI-RADS for predicting extraprostatic extension of prostate cancer: systematic review and meta-analysis

OBJECTIVES

To systematically determine the diagnostic performance of each MRI feature of the PI-RADS for predicting extraprostatic extension (EPE) in prostate cancer.

METHODS

A literature search in the MEDLINE and EMBASE databases was conducted to identify original studies reporting the accuracy of each feature on MRI for the dichotomous diagnosis of EPE. The meta-analytic pooled diagnostic odds ratio (DOR), sensitivity, specificity, and their 95% confidence intervals (CIs) were obtained using a bivariate random-effects model.

RESULTS

After screening 1955 studies, 17 studies with a total of 3062 men were included. All six imaging features, i.e., bulging prostatic contour, irregular or spiculated margin, asymmetry or invasion of neurovascular bundle, obliteration of rectoprostatic angle, tumor-capsule interface > 10 mm, and breach of the capsule with evidence of direct tumor extension, were significantly associated with EPE. Breach of the capsule with direct tumor extension demonstrated the highest pooled DOR (15.6, 95% CI [7.7-31.5]) followed by tumor-capsule interface > 10 mm (10.5 [5.4-20.2]), asymmetry or invasion of neurovascular bundle (7.6 [3.8-15.2]), and obliteration of rectoprostatic angle (6.1 [3.8-9.8]). Irregular or spiculated margin showed the lowest pooled DOR (2.3 [1.3-4.2]). Breach of the capsule with direct tumor extension and tumor-capsule interface > 10 mm showed the highest pooled specificity (98.0% [96.2-99.0]) and sensitivity (86.3% [70.0-94.4]), respectively.

CONCLUSIONS

Among the six MRI features of prostate cancer, breach of the capsule with direct tumor extension and tumor-capsule interface > 10 mm were the most predictive of EPE with the highest specificity and sensitivity, respectively.

Utility of dual read in the setting of prostate MRI interpretation

PURPOSE

The purpose of this study is to assess the utility of dual reader interpretation of prostate MRI in the evaluation/detection of prostate cancer, using the PI-RADS v2.1 scoring system.

METHODS

We performed a retrospective study to assess the utility of dual reader interpretation for prostate MRI. All MRI cases compiled for analysis were accompanied with prostate biopsy pathology reports that included Gleason scores to correlate to the MRI PI-RADS v2.1 score, tissue findings and location of pathology within the prostate gland. To assess for dual reader utility, two fellowship trained abdominal imagers (each with > 5 years of experience) provided independent and concurrent PI-RADS v2.1 scores on all included MRI examinations, which were then compared to the biopsy proven Gleason scores.

RESULTS

After application of inclusion criteria, 131 cases were used for analysis. The mean age of the cohort was 63.6 years. Sensitivity, specificity and positive/negative predictive values were calculated for each reader and concurrent scores. Reader 1 demonstrated 71.43% sensitivity, 85.39% specificity, 69.77% PPV and 86.36% NPV. Reader 2 demonstrated 83.33% sensitivity, 78.65% specificity, 64.81% PPV and 90.91% NPV. Concurrent reads demonstrated 78.57% sensitivity, 80.9% specificity, 66% PPV and 88.89% NPV. There was no statistically significant difference between the individual readers or concurrent reads (p = 0.79).

CONCLUSION

Our results highlight that dual reader interpretation in prostate MRI is not needed to detect clinically relevant tumor and that radiologists with experience and training in prostate MRI interpretation establish acceptable sensitivity and specificity marks on PI-RADS v2.1 assessment.

Improving the Effective Spatial Resolution in 1H-MRSI of the Prostate with Three-Dimensional Overdiscretized Reconstructions

In in vivo ¹H-MRSI of the prostate, small matrix sizes can cause voxel bleeding extending to regions far from a voxel, dispersing a signal of interest outside that voxel and mixing extra-prostatic residual lipid signals into the prostate. To resolve this problem, we developed a three-dimensional overdiscretized reconstruction method. Without increasing the acquisition time from current 3D MRSI acquisition methods, this method is aimed to improve the localization of metabolite signals in the prostate without compromising on SNR. The proposed method consists of a 3D spatial overdiscretization of the MRSI grid, followed by noise decorrelation with small random spectral shifts and weighted spatial averaging to reach a final target spatial resolution. We successfully applied the three-dimensional overdiscretized reconstruction method to 3D prostate ¹H-MRSI data at 3T. Both in phantom and in vivo, the method proved to be superior to conventional weighted sampling with Hamming filtering of k-space. Compared with the latter, the overdiscretized reconstructed data with smaller voxel size showed up to 10% less voxel bleed while maintaining higher SNR by a factor of 1.87 and 1.45 in phantom measurements. For in vivo measurements, within the same acquisition time and without loss of SNR compared with weighted k-space sampling and Hamming filtering, we achieved increased spatial resolution and improved localization in metabolite maps.

Correlation of multiparametric MRI with histopathological grade of peripheral zone prostate carcinoma

BACKGROUND

Prostatic cancer is the second most common malignant tumor in men. Preoperative grading of prostate cancer is important for its management. Our objective is to compare individual and combined detection rates of T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy (MRS) for prostate cancer with histopathological diagnosis as its golden standard.

METHODS

Forty-four patients with positive digital rectal examination (DRE) findings and elevated prostate specific antigen (PSA), underwent multiparametric MRI (Mp-MRI). T2WI, DWI, DCE-MRI and MRS were done in all the patients. Cognitive magnetic resonance-transrectal ultrasound (MR-TRUS) fusion biopsy was done in all the patients. Sensitivity and specificity of T2WI, DWI, DCE-MRI, and Prostate Imaging - Reporting and Data System PIRADS version 2 was obtained. Apparent diffusion coefficient (ADC) value and choline/citrate ratio were obtained for each lesion and correlated with histopathological grade.

RESULTS

The mean age of the patients was 68.7 ± 10.1 years, and the mean serum PSA level was 58.1 ± 22.4 ng/dL. Of the 38 lesions in peripheral zone, 33 (87%) had histopathologically proven prostate cancer. T2WI had a sensitivity and specificity of 75.8% and 80% and DWI had a sensitivity and specificity of 90.9% and 80%, respectively, for detection of malignant prostatic lesion. The mean ADC values for prostate cancer, prostatitis, and normal prostatic parenchyma were 0.702 ± 0.094 × 10-3 mm2/sec, 0.959 ± 0.171 × 10-3 mm2/sec, and 1.31 ± 0.223 × 10-3 mm2/sec, respectively. Type 3 curve has lower sensitivity (45.5%) but high specificity (80%) for diagnosing prostate cancer.

CONCLUSION

DWI can be useful to differentiate benign from malignant prostatic lesions, and low-grade from high-grade prostate carcinoma. ADC value has a positive correlation with histopathological grade of prostate cancer.

Cost-Effectiveness Analysis of Prostate Cancer Screening in the UK: A Decision Model Analysis Based on the CAP Trial

BACKGROUND AND OBJECTIVE

Most guidelines in the UK, Europe and North America do not recommend organised population-wide screening for prostate cancer. Prostate-specific antigen-based screening can reduce prostate cancer-specific mortality, but there are concerns about overdiagnosis, overtreatment and economic value. The aim was therefore to assess the cost effectiveness of eight potential screening strategies in the UK.

METHODS

We used a cost-utility analysis with an individual-based simulation model. The model was calibrated to data from the 10-year follow-up of the Cluster Randomised Trial of PSA Testing for Prostate Cancer (CAP). Treatment effects were modelled using data from the Prostate Testing for Cancer and Treatment (ProtecT) trial. The participants were a hypothetical population of 10 million men in the UK followed from age 30 years to death. The strategies were: no screening; five age-based screening strategies; adaptive screening, where men with an initial prostate-specific antigen level of < 1.5 ng/mL are screened every 6 years and those above this level are screened every 4 years; and two polygenic risk-stratified screening strategies. We assumed the use of pre-biopsy multi-parametric magnetic resonance imaging for men with prostate-specific antigen ≥ 3 ng/mL and combined transrectal ultrasound-guided and targeted biopsies. The main outcome measures were projected lifetime costs and quality-adjusted life-years from a National Health Service perspective.

RESULTS

All screening strategies increased costs compared with no screening, with the majority also increasing quality-adjusted life-years. At willingness-to-pay thresholds of £20,000 or £30,000 per quality-adjusted life-year gained, a once-off screening at age 50 years was optimal, although this was sensitive to the utility estimates used. Although the polygenic risk-stratified screening strategies were not on the cost-effectiveness frontier, there was evidence to suggest that they were less cost ineffective than the alternative age-based strategies.

CONCLUSIONS

Of the prostate-specific antigen-based strategies compared, only a once-off screening at age 50 years was potentially cost effective at current UK willingness-to-pay thresholds. An additional follow-up of CAP to 15 years may reduce uncertainty about the cost effectiveness of the screening strategies.

Ultra-high-field MR in Prostate cancer: Feasibility and Potential

Multiparametric MRI of the prostate at clinical magnetic field strengths (1.5/3 Tesla) has emerged as a reliable noninvasive imaging modality for identifying clinically significant cancer, enabling selective sampling of high-risk regions with MRI-targeted biopsies, and enabling minimally invasive focal treatment options. With increased sensitivity and spectral resolution, ultra-high-field (UHF) MRI (≥ 7 Tesla) holds the promise of imaging and spectroscopy of the prostate with unprecedented detail. However, exploiting the advantages of ultra-high magnetic field is challenging due to inhomogeneity of the radiofrequency field and high local specific absorption rates, raising local heating in the body as a safety concern. In this work, we review various coil designs and acquisition strategies to overcome these challenges and demonstrate the potential of UHF MRI in anatomical, functional and metabolic imaging of the prostate and pelvic lymph nodes. When difficulties with power deposition of many refocusing pulses are overcome and the full potential of metabolic spectroscopic imaging is used, UHF MR(S)I may aid in a better understanding of the development and progression of local prostate cancer. Together with large field-of-view and low-flip-angle anatomical 3D imaging, 7 T MRI can be used in its full strength to characterize different tumor stages and help explain the onset and spatial distribution of metastatic spread.

Transrectal ultrasound features and biopsy outcomes of transition PI-RADS 5

BACKGROUND

Transition Prostate Imaging and Reporting and Data System (PI-RADS) 5 is easily detected owing to typical magnetic resonance imaging features. However, it is unclear as to how transition PI-RADS 5 appears on transrectal ultrasound (TRUS).

PURPOSE

To assess TRUS features of transition PI-RADS 5 and outcomes of TRUS-guided target biopsy.

MATERIAL AND METHODS

Between March 2014 and November 2018, 186 male patients underwent TRUS-guided biopsy of PI-RADS 5. Of them, 82 and 104were transition and peripheral PI-RADS 5, respectively. Transition and peripheral PI-RADS 5 were compared according to echogenicity (hyperechoic or hypoechoic) and hypoechoic rim (present or absent). Each tumor was targeted with TRUS based on TRUS features. Significant (Gleason score ≥7) and insignificant (Gleason score 6) cancer detection rates (CDRs) were compared between transition and peripheral PI-RADS 5. Standard reference was biopsy examination. Fisher's exact test was used for statistical analysis.

RESULTS

Transition PI-RADS 5 was hyperechoic in 89.0% (73/82) and had a hypoechoic rim in 97.6% (80/82), whereas peripheral PI-RADS 5 was hypoechoic in 99.0% (103/104) and had a hypoechoic rim in 26.9% (28/104) (both, P<0.0001). The significant CDRs of transition and peripheral PI-RADS 5 were 56.1% (46/82) and 65.4% (68/104), respectively (P=0.2263). However, the insignificant CDRs of these categories were 22.0% (18/82) and 8.7% (9/104), respectively (P=0.0123).

CONCLUSION

Transition PI-RADS 5 tends to have hyperechoic echogenicity and a hypoechoic rim. These findings help to target the transition PI-RADS 5 using TRUS. However, transition PI-RADS 5 is confirmed more frequently as insignificant cancer than peripheral PI-RADS 5.

The impeded diffusion fraction quantitative imaging assay demonstrated in multi-exponential diffusion phantom and prostate cancer

PURPOSE

To demonstrate a method for quantification of impeded diffusion fraction (IDF) using conventional clinical DWI protocols.

METHODS

The IDF formalism is introduced to quantify contribution from water coordinated by macromolecules to DWI voxel signal based on fundamentally different diffusion constants in vascular capillary, bulk free, and coordinated water compartments. IDF accuracy was studied as a function of b-value set. The IDF scaling with restricted compartment size and polyvinylpirrolidone (PVP) macromolecule concentration was compared to conventional apparent diffusion coefficient (ADC) and isotropic kurtosis model parameters for a diffusion phantom. An in vivo application was demonstrated for six prostate cancer (PCa) cases with low and high grade lesions annotated from whole mount histopathology.

RESULTS

IDF linearly scaled with known restricted (vesicular) compartment size and PVP concentration in phantoms and increased with histopathologic score in PCa (from median 9% for atrophy up to 60% for Gleason 7). IDF via non-linear fit was independent of b-value subset selected between b = 0.1 and 2 ms/µm2 , including standard-of-care (SOC) PCa protocol. With maximum sensitivity for high grade PCa, the IDF threshold below 51% reduced false positive rate (FPR = 0/6) for low-grade PCa compared to apparent diffusion coefficient (ADC > 0.81 µm2 /ms) of PIRADS PCa scoring (FPR = 3/6).

CONCLUSION

The proposed method may provide quantitative imaging assays of cancer grading using common SOC DWI protocols.

Interobserver Agreement and Accuracy in Interpreting mpMRI of the Prostate: a Systematic Review

PURPOSE OF REVIEW

To present the latest evidence related to interobserver agreement and accuracy; evaluate the strengths, weaknesses, and implications of use; and outline opportunities for improvement and future development of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) for detection of prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI).

RECENT FINDINGS

Our review of currently available evidence suggests that recent improvements to the PI-RADS system with PI-RADS v2.1 slightly improved interobserver agreement, with generally high sensitivity and moderate specificity for the detection of clinically significant PCa. Recent evidence additionally demonstrates substantial improvement in diagnostic specificity with PI-RADS v2.1 compared with PI-RADS v2. However, results of studies examining the comparative performance of v2.1 are limited by small sample sizes and retrospective cohorts, potentially introducing selection bias. Some studies suggest a substantial improvement between v2.1 and v2, while others report no statistically significant difference. Additionally, in PI-RADS v2.1, the interpretation and reporting of certain findings remain subjective, particularly for category 2 lesions, and reader experience continues to vary significantly. These factors further contribute to a remaining degree of interobserver variability and findings of improved performance among more experienced readers. PI-RADS v2.1 appears to show at least minimal improvement in interobserver agreement, diagnostic performance, and both sensitivity and specificity, with greater improvements seen among more experienced readers. However, given the decrescent nature of these improvements and the limited power of all studies examined, the clinical impact of this progress may be marginal. Despite improvements in PI-RADS v2.1, practitioner experience in interpreting mpMRI of the prostate remains the most important factor in prostate cancer detection.

Use of Web-Based Calculator for the Implementation of ACR TI-RADS Risk-Stratification System

In this article, we demonstrate the use of a software-based radiologist reporting tool for the implementation of American College of Radiology Thyroid Imaging, Reporting and Data System thyroid nodule risk-stratification. The technical details are described with emphasis on addressing the information security and patient privacy issues while allowing it to integrate with the electronic health record and radiology reporting dictation software. Its practical implementation is assessed in a quality improvement project in which guideline adherence and recommendation congruence were measured pre and post implementation. The descriptions of our solution and the release of the open-sourced codes may be helpful in future implementation of similar web-based calculators.

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Usefulness of grayscale values measuring hypoechoic lesions for predicting prostate cancer: An experimental pilot study

Background

Methods

Results

Conclusions

Imaging temporomandibular disorders: Reliability of a novel MRI-based scoring system

The aim of this study was to assess the inter- and intrarater reliability of a recently proposed scoring system for temporomandibular disorders (TMD), based upon radiological findings from magnetic resonance imaging (MRI). Patients with clinically suspected uni- or bilateral TMD, and subsequently conducted MRI examination of both temporomandibular joints, were included in this study. MRI data were independently evaluated by two experienced radiologists according to the DLJ scoring system proposed by Wurm et al., which includes assessment of the following categories: articular disk (prefix 'D'), direction of disk luxation (prefix 'L'), and osseous joint alterations (prefix 'J'). 60 patients (49 female and 11 male) were eligible for analysis. No significant differences were found between both observers regarding 'D' and 'L' scores (p = 0.13 and p = 0.59, respectively). Significant differences were found for the assessment of subtle osseous changes ('J0' category: p = 0.041; 'J1' category: p = 0.018). Almost perfect intra- and interrater agreements were found for 'D' and 'L' categories (intrarater and interrater agreements for 'D': κ = 0.92 and κ = 0.84, respectively; intrarater and interrater agreements for 'L': κ = 0.93 and κ = 0.89, respectively). However, the assessment of 'J' categories revealed only moderate interrater agreement (κ = 0.49). The DLJ scoring system based upon MRI findings is feasible for routine clinical TMD assessment, and may help to simplify interdisciplinary communication between radiologists and clinicians.

Is transrectal ultrasound-guided systematic biopsy necessary after PI-RADS 4 is targeted?

Purpose: Target biopsy is usually performed in Prostate Imaging Reporting and Data System (PI-RADS) 4. Still, it is unclear if adding systematic biopsy to target biopsy influences cancer detection. The aim was to assess the role of systematic biopsy for detecting significant cancer after PI-RADS 4 is targeted.Methods: Between March 2014 and November 2018, 182 men with PI-RADS 4 underwent transrectal ultrasound (TRUS)-guided biopsy. Systematic biopsy was added to target biopsy in 128 men (Group I) by May 2018 because PI-RADS 4 was not completely visible on TRUS, while it was done in 54 men (Group II) from June 2018 regardless of lesion visibility. Significant cancer detection rates (CDRs) were compared between the groups regarding target and systematic biopsies. Major complication rate was also compared. Significant cancer was defined as a Gleason score ≥7 tumor. Standard reference was biopsy examination. Fisher’s exact were used for statistical analysis.Results: The significant CDRs were 21.9% (28/128) in the Group I and 38.9% (21/54) in the Group II (P= 0.0273). The significant cancers of Group I and II were missed in two (1.6%) and in one (1.9%) by target biopsy, respectively. Major complication rates of these groups were 0.8% (1/128) and 0% (0/54), respectively (P= 0.999).Conclusion: Systematic biopsy should be added to target biopsy even though PI-RADS 4 is clearly visible on ultrasound. A significant number of significant cancers are detected with systematic biopsy.

Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning

Purpose To develop a deep learning model to delineate the transition zone (TZ) and peripheral zone (PZ) of the prostate on MR images. Materials and Methods This retrospective study was composed of patients who underwent a multiparametric prostate MRI and an MRI/transrectal US fusion biopsy between January 2013 and May 2016. A board-certified abdominal radiologist manually segmented the prostate, TZ, and PZ on the entire data set. Included accessions were split into 60% training, 20% validation, and 20% test data sets for model development. Three convolutional neural networks with a U-Net architecture were trained for automatic recognition of the prostate organ, TZ, and PZ. Model performance for segmentation was assessed using Dice scores and Pearson correlation coefficients. Results A total of 242 patients were included (242 MR images; 6292 total images). Models for prostate organ segmentation, TZ segmentation, and PZ segmentation were trained and validated. Using the test data set, for prostate organ segmentation, the mean Dice score was 0.940 (interquartile range, 0.930-0.961), and the Pearson correlation coefficient for volume was 0.981 (95% CI: 0.966, 0.989). For TZ segmentation, the mean Dice score was 0.910 (interquartile range, 0.894-0.938), and the Pearson correlation coefficient for volume was 0.992 (95% CI: 0.985, 0.995). For PZ segmentation, the mean Dice score was 0.774 (interquartile range, 0.727-0.832), and the Pearson correlation coefficient for volume was 0.927 (95% CI: 0.870, 0.957). Conclusion Deep learning with an architecture composed of three U-Nets can accurately segment the prostate, TZ, and PZ. Keywords: MRI, Genital/Reproductive, Prostate, Neural Networks Supplemental material is available for this article. © RSNA, 2021.

Pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI in PI-RADS category 3 peripheral zone lesions: preliminary study evaluating DCE-MRI as an imaging biomarker for detection of clinically significant prostate cancers

PURPOSE

To determine if pharmacokinetic modeling of DCE-MRI can diagnose CS-PCa in PI-RADS category 3 PZ lesions with subjective negative DCE-MRI.

MATERIALS AND METHODS

In the present IRB approved, bi-institutional, retrospective, case-control study, we identified 73 men with 73 PZ PI-RADS version 2.1 category 3 lesions with MRI-directed-TRUS-guided targeted biopsy yielding: 12 PZ CS-PCa (ISUP Grade Group 2; N = 9, ISUP 3; N = 3), 27 ISUP 1 PCa and 34 benign lesions. An expert blinded radiologist segmented lesions on ADC and DCE images; segmentations were overlayed onto pharmacokinetic DCE-MRI maps. Mean values were compared between groups using univariate analysis. Diagnostic accuracy was assessed by ROC.

RESULTS

There were no differences in age, PSA, PSAD or clinical stage between groups (p = 0.265-0.645). Mean and 10th percentile ADC did not differ comparing CS-PCa to ISUP 1 PCa and benign lesions (p = 0.376 and 0.598) but was lower comparing ISUP ≥ 1 PCa to benign lesions (p < 0.001). Mean Ktrans (p = 0.003), Ve (p = 0.003) but not Kep (p = 0.387) were higher in CS-PCa compared to ISUP 1 PCa and benign lesions. There were no differences in DCE-MRI metrics comparing ISUP ≥ 1 PCa and benign lesions (p > 0.05). AUC for diagnosis of CS-PCa using Ktrans and Ve were: 0.69 (95% CI 0.52-0.87) and 0.69 (0.49-0.88).

CONCLUSION

Pharmacokinetic modeling of DCE-MRI parameters in PI-RADS category 3 lesions with subjectively negative DCE-MRI show significant differences comparing CS-PCa to ISUP 1 PCa and benign lesions, in this study outperforming ADC. Studies are required to further evaluate these parameters to determine which patients should undergo targeted biopsy for PI-RADS 3 lesions.

Fully automated detection of prostate transition zone tumors on T2-weighted and apparent diffusion coefficient (ADC) map MR images using U-Net ensemble

PURPOSE

Accurate detection of transition zone (TZ) prostate cancer (PCa) on magnetic resonance imaging (MRI) remains challenging using clinical subjective assessment due to overlap between PCa and benign prostatic hyperplasia (BPH). The objective of this paper is to describe a deep-learning-based framework for fully automated detection of PCa in the TZ using T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images.

METHOD

This was a single-center IRB-approved cross-sectional study of men undergoing 3T MRI on two systems. The dataset consisted of 196 patients (103 with and 93 without clinically significant [Grade Group 2 or higher] TZ PCa) to train and test our proposed methodology, with an additional 168 patients with peripheral zone PCa used only for training. We proposed an ensemble of classifiers in which multiple U-Net-based models are designed for prediction of TZ PCa location on ADC map MR images, with initial automated segmentation of the prostate to guide detection. We compared accuracy of ADC alone to T2W and combined ADC+T2W MRI for input images, and investigated improvements using ensembles over their constituent models with different methods of diversity in individual models by hyperparameter configuration, loss function and model architecture.

RESULTS

Our developed algorithm reported sensitivity and precision of 0.829 and 0.617 in 56 test cases containing 31 instances of TZ PCa and in 25 patients without clinically significant TZ tumors. Patient-wise classification accuracy had an area under receiver operator characteristic curve (AUROC) of 0.974. Single U-Net models using ADC alone (sensitivity 0.829, precision 0.534) outperformed assessment using T2W (sensitivity 0.086, precision 0.081) and assessment using combined ADC+T2W (sensitivity 0.687, precision 0.489). While the ensemble of U-Nets with varying hyperparameters demonstrated the highest performance, all ensembles improved PCa detection compared to individual models, with sensitivities and precisions close to the collective best of constituent models.

CONCLUSION

We describe a deep-learning-based method for fully automated TZ PCa detection using ADC map MR images that outperformed assessment by T2W and ADC+T2W.

Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging

Introduction

Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, “DLR” in improving image quality and mitigating artifacts, which is now commercially available as AIR^TM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts.

Methods

This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERC_DLR, ERC_Conv, Non-ERC_DLR, and Non-ERC_Conv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor.

Results

The Non-ERC_DLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor.

Conclusion

Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.

Interobserver agreement of Prostate Imaging–Reporting and Data System (PI-RADS–v2)

Background

A retrospective study was conducted on 71 consecutive patients with suspected prostate cancer (PCa) with a mean age of 56 years and underwent mp-MRI of the prostate at 3 Tesla MRI. Two readers recognized all prostatic lesions, and each lesion had a score according to Prostate Imaging–Reporting and Data System version 2 (PI-RADS-v2).

Purpose of the study

To evaluate the interobserver agreement of PI-RADS-v2 in characterization of prostatic lesions using multiparametric MRI (mp-MRI) at 3 Tesla MRI.

Results

The overall interobserver agreement of PI-RADS-v2 for both zones was excellent (k = 0.81, percent agreement = 94.9%). In the peripheral zone (PZ) lesions are the interobserver agreement for PI-RADS II (k = 0.78, percent agreement = 83.9%), PI-RADS III (k = 0.66, percent agreement = 91.3 %), PI-RADS IV (k = 0.69, percent agreement = 93.5%), and PI-RADS V (k = 0.91, percent agreement = 95.7 %). In the transitional zone (TZ) lesions are the interobserver agreement for PI-RADS I (k = 0.98, percent of agreement = 96%), PI-RADS II (k = 0.65, percent agreement = 96%), PI-RADS III (k = 0.65, percent agreement = 88%), PI-RADS IV (k = 0.83, percent agreement = 96%), and PI-RADS V (k = 0.82, percent agreement = 92%).

Conclusion

We concluded that PI-RADS-v2 is a reliable and a reproducible imaging modality for the characterization of prostatic lesions and detection of PCa.

Clinical experience with active surveillance protocol using regular magnetic resonance imaging instead of regular repeat biopsy for monitoring: A study at a high-volume center in Korea

Background

Here, we report the experience of a multiparameter magnetic resonance imaging (MRI)–based active surveillance (AS) protocol that did not include performing a repeat biopsy after the diagnosis of prostate cancer by prostate biopsy or transurethral resection of prostate.

Methods

From January 2010 to December 2017, we reviewed 193 patients with newly diagnosed prostate cancer who were eligible for AS. The patients were divided into AS group (n = 122) and definitive treatment group (n = 71) based on initial treatment. Disease progression was defined as a remarkable change in MRI findings. To confirm the stability of protocol, we compared the clinicopathological characteristics of patients who initially underwent radical prostatectomy (RP) (n = 58) and RP after termination of AS (n = 20).

Results

Among patients who initially selected AS (median adherence duration = 31.4 months), 70 (57.3%) subsequently changed their treatment options. Disease progression (n = 30) was the main cause for termination. No significant differences were found in the clinicopathologic characteristics at initial diagnosis and pathologic outcomes between patients who initially underwent RP and those who chose RP after termination of AS. In a comparative analysis of diagnostic methods, the patients with incidental prostate cancer by transurethral resection of prostate had higher age, lower prostate-specific antigen level and density, as well as longer AS adherence duration and follow-up duration compared with those diagnosed by prostate biopsy.

Conclusions

Our AS monitoring protocol, which depends on MRI instead of regular repeat biopsy, was feasible. Patients with incidental prostate cancer continued AS more compared with patients diagnosed by prostate biopsy.

PI-RADS: what is new and how to use it

The prostate imaging reporting and data system (PI-RADS) has revolutionized the use of magnetic resonance imaging (MRI) for the management of prostate cancer (PCa). The most recent version 2.1, PI-RADS v2.1, provides specific refinements in the performance, relaxing some recommendations which were not found to be helpful, while reinforcing and clarifying others. The interpretation of T2-weighted imaging (T2WI) in the transition zone (TZ), and the overall assessment of TZ nodules, now allows for a clearer distinction between those which are clearly benign and those which might warrant tissue sampling. Additional changes also resolve discrepancies in T2WI and diffusion-weighted imaging (DWI) of the peripheral zone (PZ). PI-RADS v2.1 is a simpler, more straightforward, and more reproducible method to better communicate between physicians regarding findings on prostate MRI.

PI-RADS version 2.1 scoring system is superior in detecting transition zone prostate cancer: a diagnostic study

PURPOSE

The studies comparing the versions 2 vs. 2.1 of the Prostate Imaging Reporting and Data System (PI-RADS) are rare. This study aimed to evaluate whether PI-RADS version 2.1 is superior in detecting transition zone prostate cancer in comparison with PI-RADS version 2.

METHODS

This was a diagnostic study of patients with prostate diseases who visited the Urology Department of The Second Affiliated Hospital of Soochow University and underwent a magnetic resonance imaging (MRI) examination between 03-01-2016 and 10-31-2018. The images originally analyzed using PI-RADS version 2 were retrospectively re-analyzed and scored in 2019 according to the updated PI-RADS version 2.1. The kappa and receiver operating characteristic (ROC) curves were used.

RESULTS

For Reader 1, compared with PI-RADS version 2, version 2.1 had higher sensitivity (85% vs. 79%, P = 0.03), lower specificity (65% vs. 83%, P < 0.001), and lower area under the curve (AUC) (0.749 vs. 0.809, P < 0.001). For Reader 2 (first attempt), compared with PI-RADS version 2, version 2.1 had lower specificity (67% vs. 91%, P < 0.001) and lower AUC (0.702 vs. 0.844, P < 0.001). For Reader 2 (second attempt), compared with PI-RADS version 2, version 2.1 had higher sensitivity (88% vs. 78%, P < 0.001) and lower specificity (77% vs. 91%, P < 0.001). The kappa between the two attempts for Reader 2 was 0.321.

CONCLUSION

These results suggest that PI-RADS version 2.1 might improve the detection of prostate cancers in the transition zone compared with PI-RADS version 2 but that it might results in higher numbers of biopsies because of lower specificity.

Role of Multiparametric Prostate Magnetic Resonance Imaging before Confirmatory Biopsy in Assessing the Risk of Prostate Cancer Progression during Active Surveillance

Objective

To evaluate the impact of multiparametric magnetic resonance imaging (mpMRI) before confirmatory prostate biopsy in patients under active surveillance (AS).

Materials and Methods

This retrospective study included 170 patients with Gleason grade 6 prostate cancer initially enrolled in an AS program between 2011 and 2019. Prostate mpMRI was performed using a 1.5 tesla (T) magnetic resonance imaging system with a 16-channel phased-array body coil. The protocol included T1-weighted, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced imaging sequences. Uroradiology reports generated by a specialist were based on prostate imaging-reporting and data system (PI-RADS) version 2. Univariate and multivariate analyses were performed based on regression models.

Results

The reclassification rate at confirmatory biopsy was higher in patients with suspicious lesions on mpMRI (PI-RADS score ≥ 3) (n = 47) than in patients with non-suspicious mpMRIs (n = 61) and who did not undergo mpMRIs (n = 62) (66%, 26.2%, and 24.2%, respectively; p < 0.001). On multivariate analysis, presence of a suspicious mpMRI finding (PI-RADS score ≥ 3) was associated (adjusted odds ratio: 4.72) with the risk of reclassification at confirmatory biopsy after adjusting for the main variables (age, prostate-specific antigen density, number of positive cores, number of previous biopsies, and clinical stage). Presence of a suspicious mpMRI finding (adjusted hazard ratio: 2.62) was also associated with the risk of progression to active treatment during the follow-up.

Conclusion

Inclusion of mpMRI before the confirmatory biopsy is useful to stratify the risk of reclassification during the biopsy as well as to evaluate the risk of progression to active treatment during follow-up.

RadioGraphics Update: PI-RADS Version 2.1-A Pictorial Update

Editor's Note.-Articles in the RadioGraphics Update section provide current knowledge to supplement or update information found in full-length articles previously published in RadioGraphics. Authors of the previously published article provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes. Articles in this section are published solely online and are linked to the original article.

Prostate MRI: practical guidelines for interpreting and reporting according to PI-RADS version 2.1

The increasing precision of multiparametric magnetic resonance imaging of the prostate, together with greater experience and standardization in its interpretation, has given this technique an important role in the management of prostate cancer, the most prevalent non-cutaneous cancer in men. This article reviews the concepts in PI-RADS version 2.1 for estimating the probability and zonal location of significant tumors of the prostate, using a practical approach that includes current considerations about the prerequisites for carrying out the test and recommendations for interpreting the findings. It emphasizes benign findings that can lead to confusion and the criteria for evaluating the probability of local spread, which must be included in the structured report.

Radiotherapy for prostate cancer: Effect of gold fiducial markers on diffusion-weighted magnetic resonance imaging

PURPOSE

There has been an increase in the use of gold fiducial markers to ensure precise radiotherapy delivery in prostate cancer patients. However, metal artifacts may affect the quality of subsequent imaging used to assess disease status following treatment. In this study, we evaluated the effect of gold fiducial markers on magnetic resonance imaging (MRI), particularly on diffusion-weighted imaging (DWI).

MATERIAL AND METHODS

Among 57 patients with prostate cancer, 21 patients in whom two gold markers were placed in the prostate tumor with abnormal signal intensity on DWI were evaluated. The effect of the markers on DWI was evaluated on a scale of 1-5, with a high score indicating clinical usefulness. Change inapparent diffusion coefficient (ADC; 10^-3 mm² /s) from before to after marker placement was also evaluated.

RESULTS

The mean effect of the markers on DWI was 4.3 (standard deviation [SD] 1.3, range 2-5) points. The mean change in ADC was 0.045 (SD 0.041, range 0.025-0.089) × 10^-3 mm² /s.

CONCLUSIONS

The gold fiducial markers demonstrated negligible effect on DWI quality. Therefore, gold markers do not affect MRI quality, particularly DWI, and may be used during follow-up in prostate cancer patients.

Prostatitis, the Great Mimicker of Prostate Cancer: Can We Differentiate Them Quantitatively With Multiparametric MRI?

OBJECTIVE. The purpose of this study was to investigate the diagnostic performance of semiquantitative and quantitative pharmacokinetic parameters and quantitative apparent diffusion coefficient (ADC) values obtained from prostate multiparametric MRI (mpMRI) to differentiate prostate cancer (PCa) and prostatitis objectively. MATERIALS AND METHODS. We conducted a retrospective review of patients with biopsy-proven PCa or prostatitis who underwent mpMRI study between January 2015 and February 2018. Mean ADC, forward volume transfer constant (K^trans), reverse volume transfer constant (k_ep), plasma volume fraction (V_p), extravascular extracellular space volume fraction (V_e), and time to peak (TTP) values were calculated for both lesions and contralateral normal prostate tissue. Signal intensity-time curves were analyzed. Lesion-to-normal prostate tissue ratios of pharmacokinetic parameters were also calculated. The diagnostic accuracy and cutoff points of all parameters were analyzed to differentiate PCa from prostatitis. RESULTS. A total of 138 patients (94 with PCa and 44 with prostatitis) were included in the study. Statistically, ADC, quantitative pharmacokinetic parameters (K^trans, k_ep, V_e, and V_p), their lesion-to-normal prostate tissue ratios, and TTP values successfully differentiated PCa and prostatitis. Surprisingly, we found that V_e values were significantly higher in prostatitis lesions. The combination of these parameters had 92.7% overall diagnostic accuracy. ADC, k_ep, and TTP made up the most successful combination for differential diagnosis. Analysis of the signal intensity-time curves showed mostly type 2 and type 3 enhancement curve patterns for patients with PCa. Type 3 curves were not seen in any prostatitis cases. CONCLUSION. Quantitative analysis of mpMRI differentiates PCa from prostatitis with high sensitivity and specificity, appears to have significant potential, and may improve diagnostic accuracy. In addition, evaluating these parameters does not cause any extra burden to the patients.

Clinical utility of combined T2-weighted imaging and T2-mapping in the detection of prostate cancer: a multi-observer study

Background

To evaluate the clinical utility of combined T2-weighted imaging and T2-mapping for the detection of prostate cancer.

Methods

Forty patients underwent multiparametric magnetic resonance imaging (mpMRI) and T2-mapping of the prostate. Three readers each reviewed two sets of images: T2-weighted fast spin-echo (FSE) sequence (standard T2), and standard T2 in combination with T2-mapping. Each reader assigned probability scores for malignancy to each zone [peripheral zone (PZ) or transition zone (TZ)]. Inter-observer variability for standard T2 and combined standard T2 with T2-mapping were assessed. Diagnostic accuracy was compared between standard T2 and combined standard T2 with T2-mapping.

Results

There was fair agreement between all three readers for standard T2 [intraclass correlation coefficient (ICC) =0.56] and combined standard T2 with T2-mapping (ICC =0.58). There was no significant difference in the area under the receiver operator characteristics curve for standard T2 compared to combined standard T2 with T2-mapping (0.89 vs. 0.82, P=0.31). Sensitivity (Sn) for combined standard T2 with T2-mapping was significantly higher compared to standard T2 alone (73.0% vs. 49.2%, P=0.006). Specificity (Sp) for combined standard T2 with T2-mapping was borderline significantly lower compared to standard T2 alone (89.3% vs. 94.9%, P=0.05). There was no significant differences between the negative predictive values (NPVs) and positive predictive values (PPVs) (P=0.07, P=0.45).

Conclusions

Combination of T2-weighted imaging and T2-mapping could potentially increase Sn for prostate malignancy compared to T2-weighted imaging alone.

Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence

Background:

Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer.

Aim:

The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening.

Methods:

This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results.

Results:

CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist.

Conclusion:

When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.

Diagnostic Performance of Mass Enhancement on Dynamic Contrast-Enhanced MRI for Predicting Clinically Significant Peripheral Zone Prostate Cancer

OBJECTIVE. Current criteria for positive findings on dynamic contrast-enhanced MRI (DCE-MRI) are unclear. We compared the diagnostic performance of mass enhancement on DCE-MRI versus conventional DCE-MRI criteria for identifying clinically significant prostate cancer (csPCa) in the peripheral zone (PZ). MATERIALS AND METHODS. A total of 173 consecutive patients with MRI- and surgically proven prostate cancer (PCa) were evaluated. Two readers independently interpreted DCE-MRI examinations of the PZ. Criteria denoting a positive DCE-MRI examination included conventional criteria from the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and mass enhancement. The diagnostic performance of and interreader agreement for the two types of enhancement criteria in identifying csPCa in the PZ that met Epstein criteria were investigated. RESULTS. The proportion of csPCa in the PZ was 69.3% (120/173). For both readers, the specificity and positive predictive value of mass enhancement were increased compared with conventional enhancement criteria (specificity, 75.5% vs 5.7% [for reader 1] and 84.9% vs 30.2% [for reader 2], respectively; positive predictive value, 87.1% vs 70.6% [for reader 1] and 91.5% vs 75.3% [for reader 2], respectively). The AUC value of mass enhancement was higher than that of conventional criteria (for reader 1, 0.744 [95% CI, 0.672-0.807] vs 0.528 [95% CI, 0.451-0.605] [p < 0.001], respectively; for reader 2, 0.783 [95% CI, 0.714-0.842] vs 0.602 [95% CI, 0.497-0.700] [p < 0.001], respectively). The weighted kappa value for agreement between the two readers was 0.206 for conventional criteria and 0.613 for mass enhancement. CONCLUSION. PZ lesions with mass enhancement on DCE-MRI are more likely to be csPCa. This enhancement pattern may need to be considered as one of the criteria in PI-RADS.

Combining Prostate-Specific Antigen Parameters With Prostate Imaging Reporting and Data System Score Version 2.0 to Improve Its Diagnostic Accuracy

Background

Any non-invasive test that can predict the absence of prostate cancer (PCa) or absence of clinically significant PCa (CSPCa) is necessary, as it can reduce the number of unnecessary biopsies in patients with gray zone prostate-specific antigen (PSA, 4 - 10 ng/mL). This study evaluated the diagnostic performance of free PSA% and PSA density (PSAD), and Prostate Imaging Reporting and Data System (PIRADS) score (version 2.0) alone and combined in predicting CSPCa in patients with PSA between 4 and 10 ng/mL.

Methods

This prospective study included a total of 104 consecutive patients with lower urinary tract symptoms (LUTS) and serum PSA between 4 and 10 ng/mL, with or without abnormal digital rectal examination (DRE) findings or any hypoechoic lesion on ultrasound sonography of prostate and without prior transrectal ultrasound (TRUS) biopsy of prostate. PIRADS score was calculated using multi-parametric magnetic resonance imaging (mp-MRI) before TRUS biopsy of prostate. Relationships among PIRADS score, PSAD, free PSA% and presence of CSPCa in TRUS biopsy were statistically analyzed.

Results

In patients with CSPCa, significantly higher median age (P = 0.001), PSA level (P < 0.001), PSAD (P < 0.001) and significantly lower prostate volume (P < 0.001) and free PSA% were observed as compared to patients with non-CSPCa. Significantly higher proportion of patients with CSPCa showed PIRADS positive test compared to those with non-CSPCa (86.4% vs. 53.3%, P < 0.001). Cut-off values for PSAD and free PSA% were 0.12 ng/mL² and 25%, respectively. Age, PSAD and free PSA% were significant predictors of PCa, while age and PSAD were significant predictors of CSPCa. Criteria 2, 3 and 4 demonstrated higher specificity and positive predictive value (PPV) in predicting CSPCa as compared to criterion 1. The overall accuracies of criterion 1, 2, 3 and 4 were 64.42%, 85.58%, 80.77% and 79.81%, respectively. The area under the curve (AUC) values of criterion 2, 3 and 4 were higher (0.827, 0.732 and 0.792) than criterion 1 (0.665).

Conclusion

Using PIRADS score for predicting CSPCa as a screening test, criteria 2, 3 and 4 have much higher diagnostic performance and present accuracy of mp-MRI to predict CSPCa can be increased with addition of PSAD and free PSA%.

Comparison of prostate cancer detection rates of various prostate biopsy methods for patients with prostate-specific antigen levels of <10.0 ng/mL in real-world practice

Purpose

Several strategies of prostate biopsy (PBx) have been introduced to improve prostate cancer (PCa) detection rates. However, studies comparing cancer detection rates (CDRs) according to biopsy methods in real-world practice are scarce. This study aimed to investigate CDRs according to the biopsy methods for patients with prostate-specific antigen (PSA) <10.0 ng/mL.

Materials and Methods

From 2006 to 2015, patients who underwent PBx were initially selected. All patients were categorized according to the biopsy methods performed (magnetic resonance imaging targeted biopsy [MR-TBx], 12+2 hypoechoic lesion target biopsy, saturation biopsy [sPBx], extended biopsy, and 12-core PBx). The CDR of MR-TBx was compared to that of sPBx and other protocols. Volume per core (VPC) was defined as prostate volume divided by the number of biopsy cores. Patients previously diagnosed with PCa were excluded.

Results

Of the 1,598 patients (median PSA, 5.41 ng/mL), 401 (25.1%) were diagnosed with PCa. Among the biopsy methods, MR-TBx has the highest CDR and proportion of Gleason score ≥7 (3+4). Biopsy methods, VPC, age, prostate volume, and PSA were associated with PCa detection. In the sub-analysis for initial biopsy, MR-TBx had no significant difference with sPBx, but had higher CDR than the other biopsy protocols. For repeat biopsy, VPC, rather than the biopsy method, was associated with CDR.

Conclusions

This study reaffirmed the efficacy of MR-TBx on CDR in real-world practice. In cases with barriers to performing magnetic resonance imaging, VPC might be useful for adjusting the optimal number of biopsy cores in repeat biopsy.

Prostate cancer aggressive prediction: preponderant diagnostic performances of intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) beyond ADC at 3.0 T scanner with gleason score at final pathology

PURPOSE

To explore the preponderant diagnostic performances of IVIM and DKI in predicting the Gleason score (GS) of prostate cancer.

METHODS

Diffusion-weighted imaging data were postprocessed using monoexponential, lVIM and DK models to quantitate the apparent diffusion coefficient (ADC), molecular diffusion coefficient (D), perfusion-related diffusion coefficient (Dstar), perfusion fraction (F), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). Spearman's rank correlation coefficient was used to explore the relationship between those parameters and the GS, Kruskal-Wallis test, and Mann-Whitney U test were performed to compare the above parameters between the different groups, and a receiver-operating characteristic (ROC) curve was used to analyze the differential diagnosis ability. The interpretation of the results is in view of histopathologic tumor tissue composition.

RESULTS

The area under the ROC curves (AUCs) of ADC, F, D, Dapp, and Kapp in differentiating GS ≤ 3 + 4 and GS > 3 + 4 PCa were 0.744 (95% CI 0.581-0.868), 0.726 (95% CI 0.563-0.855), 0.732 (95% CI 0.569-0.860), and 0.752 (95% CI 0.590-0.875), 0.766 (95% CI 0.606-0.885), respectively, and those in differentiating GS ≤ 7 and GS > 7 PCa were 0.755 (95% CI 0.594-0.877), 0.734 (95% CI 0.571-0.861), 0.724 (95% CI0.560-0.853), and 0.716 (95% CI 0.552-0.847), 0.828 (95% CI 0.676-0.929), respectively. All the P values were less than 0.05. There was no significant difference in the AUC for the detection of different GS groups by using those parameters.

CONCLUSION

Both the IVIM and DKI models are beneficial to predict GS of PCa and indirectly predict its aggressiveness, and they have a comparable diagnostic performance with each other as well as ADC.

Necessity of differentiating small (< 10 mm) and large (≥ 10 mm) PI-RADS 4

PURPOSE

Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) provides reasonable performance in detecting significant cancers. Still, it is unclear about whether all PI-RADS 4 lesions show the same cancer detection rate (CDR) regardless of tumor size. The aim was to compare the CDRs of small (< 10 mm) and large (≥ 10 mm) PI-RADS 4.

METHODS

After magnetic resonance imaging (MRI) was performed in 684 men, a radiologist interpreted the MR images and detected 281 index lesions categorized as PI-RADS 4 in 281 men. PI-RADS 4 lesions were divided into small and large groups on size of 10 mm. Overall and significant CDRs were compared between the groups. A significant cancer was defined as one with Gleason score (GS) ≥ 7 or tumor volume ≥ 0.5 ml. Tumor volumes were roughly calculated as πr³4/3 (π = 3.14 and r = a half of tumor size) and were compared between the groups. Standard reference was a biopsy examination. Fisher's exact and Mann-Whitney tests were used for statistical analysis.

RESULTS

The overall CDRs of small and large groups were 39.0% (53/136) and 59.3% (86/145), respectively, (p = 0.0008). The median tumor volumes of cancer-proven small and large groups were 0.18 ml (0.01-0.38 ml) and 0.70 ml (0.52-1.44 ml), respectively (p < 0.0001). Using GS or tumor volume, the significant CDRs of these groups were 26.5% (36/136) and 59.3% (86/145), respectively (p < 0.0001), and using GS alone, 26.5% (36/136) and 39.3% (57/145), respectively (p = 0.0232).

CONCLUSIONS

PI-RADS 4 lesions should be sub-divided on size of 10 mm because of different significant CDRs.