Accuracy and agreement of PIRADSv2 for prostate cancer mpMRI: A multireader study

The primacy of multiparametric MRI in men with suspected prostate cancer

Background

Multiparametric MRI (mpMRI) became recognised in investigating those with suspected prostate cancer between 2010 and 2012; in the USA, the preventative task force moratorium on PSA screening was a strong catalyst. In a few short years, it has been adopted into daily urological and oncological practice. The pace of clinical uptake, born along by countless papers proclaiming high accuracy in detecting clinically significant prostate cancer, has sparked much debate about the timing of mpMRI within the traditional biopsy-driven clinical pathways. There are strongly held opposing views on using mpMRI as a triage test regarding the need for biopsy and/or guiding the biopsy pattern.

Objective

To review the evidence base and present a position paper on the role of mpMRI in the diagnosis and management of prostate cancer.

Methods

A subgroup of experts from the ESUR Prostate MRI Working Group conducted literature review and face to face and electronic exchanges to draw up a position statement.

Results

This paper considers diagnostic strategies for clinically significant prostate cancer; current national and international guidance; the impact of pre-biopsy mpMRI in detection of clinically significant and clinically insignificant neoplasms; the impact of pre-biopsy mpMRI on biopsy strategies and targeting; the notion of mpMRI within a wider risk evaluation on a patient by patient basis; the problems that beset mpMRI including inter-observer variability.

Conclusions

The paper concludes with a set of suggestions for using mpMRI to influence who to biopsy and who not to biopsy at diagnosis.

Key Points

• Adopt mpMRI as the first, and primary, investigation in the workup of men with suspected prostate cancer.

• PI-RADS assessment categories 1 and 2 have a high negative predictive value in excluding significant disease, and systematic biopsy may be postponed, especially in men with low-risk of disease following additional risk stratification.

• PI-RADS assessment category lesions 4 and 5 should be targeted; PI-RADS assessment category lesion 3 may be biopsied as a target, as part of systematic biopsies or may be observed depending on risk stratification.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06166-z) contains supplementary material, which is available to authorized users.

Detection and Localization of Prostate Cancer at 3-T Multiparametric MRI Using PI-RADS Segmentation

OBJECTIVE. The purpose of this study is to determine the overall and sector-based performance of 3-T multiparametric MRI for prostate cancer (PCa) detection and localization by using Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) scoring and segmentation compared with whole-mount histopathologic analysis. MATERIALS AND METHODS. Multiparametric 3-T MRI examinations of 415 consecutive men were compared with thin-section whole-mount histopathologic analysis. A genitourinary radiologist and pathologist collectively determined concordance. Two radiologists assigned PI-RADSv2 categories and sectoral location to all detected foci by consensus. Tumor detection rates were calculated for clinical and pathologic (Gleason score) variables. Both rigid and adjusted sector-matching models were used to account for fixation-related issues. RESULTS. The 415 patients had 863 PCa foci (52.7% had a Gleason score ≥ 7, 61.9% were ≥ 1 cm, and 90.4% (375/415) of index lesions were ≥ 1 cm) and 16,185 prostate sectors. Multiparametric MRI enabled greater detection of PCa lesions 1 cm or larger (all lesions vs index lesions, 61.6% vs 81.6%), lesions with Gleason score greater than or equal to 7 (all lesions vs index lesions, 71.4% vs 80.9%), and index lesions with both Gleason score greater than or equal to 7 and size 1 cm or larger (83.3%). Higher sensitivity was obtained for adjusted versus rigid tumor localization for all lesions (56.0% vs 28.5%), index lesions (55.4% vs 34.3%), lesions with Gleason score greater than or equal to 7 (55.7% vs 36.0%), and index lesions 1 cm or larger (56.1% vs 35.0%). Multiparametric 3-T MRI had similarly high specificity (96.0-97.9%) for overall and index tumor localization with adjusted and rigid sector-matching approaches. CONCLUSION. Using 3-T multiparametric MRI and PI-RADSv2, we achieved the highest sensitivity (83.3%) for the detection of lesions 1 cm or larger with Gleason score greater than or equal to 7. Sectoral localization of PCa within the prostate was moderate and was better with an adjusted model than with a rigid model.

Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI

OBJECTIVE. The purpose of this study was to evaluate agreement among radiologists in detecting and assessing prostate cancer at multiparametric MRI using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2). MATERIALS AND METHODS. Treatment-naïve patients underwent 3-T multipara-metric MRI between April 2012 and June 2015. Among the 163 patients evaluated, 110 underwent prostatectomy after MRI and 53 had normal MRI findings and transrectal ultrasound-guided biopsy results. Nine radiologists participated (three each with high, intermediate, and low levels of experience). Readers interpreted images of 58 patients on average (range, 56-60) using PI-RADSv2. Prostatectomy specimens registered to MRI were ground truth. Interob-server agreement was evaluated with the index of specific agreement for lesion detection and kappa and proportion of agreement for PI-RADS category assignment. RESULTS. The radiologists detected 336 lesions. Sensitivity for index lesions was 80.9% (95% CI, 75.1-85.9%), comparable across reader experience (p = 0.392). Patient-level specificity was experience dependent; highly experienced readers had 84.0% specificity versus 55.2% for all others (p < 0.001). Interobserver agreement was excellent for detecting index lesions (index of specific agreement, 0.871; 95% CI, 0.798-0.923). Agreement on PI-RADSv2 category assignment of index lesions was moderate (κ = 0.419; 95% CI, 0.238-0.595). For individual category assignments, proportion of agreement was slight for PI-RADS category 3 (0.208; 95% CI, 0.086-0.284) but substantial for PI-RADS category 4 (0.674; 95% CI, 0.540-0.776). However, proportion of agreement for T2-weighted PI-RADS 4 in the transition zone was 0.250 (95% CI, 0.108-0.372). Proportion of agreement for category assignment of index lesions on dynamic contrast-enhanced MR images was 0.822 (95% CI, 0.728-0.903), on T2-weighted MR images was 0.515 (95% CI, 0.430-0623), and on DW images was 0.586 (95% CI, 0.495-0.682). Proportion of agreement for dominant lesion was excellent (0.828; 95% CI, 0.742-0.913). CONCLUSION. Radiologists across experience levels had excellent agreement for detecting index lesions and moderate agreement for category assignment of lesions using PI-RADS. Future iterations of PI-RADS should clarify PI-RADS 3 and PI-RADS 4 in the transition zone.

Radiomics and radiogenomics of prostate cancer

Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer.

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.

How Often is the Dynamic Contrast Enhanced Score Needed in PI-RADS Version 2?

BACKGROUND

Prostate imaging reporting and data system version 2 (PI-RADS v2) relegates dynamic contrast enhanced (DCE) imaging to a minor role. We sought to determine how often DCE is used in PI-RADS v2 scoring.

MATERIALS AND METHODS

We retrospectively reviewed data from 388 patients who underwent prostate magnetic resonance imaging and subsequent biopsy from January 2016 through December 2017. In accordance with PI-RADS v2, DCE was deemed necessary if a peripheral-zone lesion had a diffusion-weighted imaging score of 3, or if a transition-zone lesion had a T2 score of 3 and diffusion-weighted imaging experienced technical failure. Receiver operating characteristic curve analysis assessed the accuracy of prostate-specific antigen density (PSAD) at different threshold values for differentiating lesions that would be equivocal with noncontrast technique. Accuracy of PSAD was compared to DCE using McNemar's test.

RESULTS

Sixty-nine lesions in 62 patients (16%) required DCE for PI-RADS scoring. Biopsy of 10 (14%) of these lesions showed clinically significant cancer (Gleason score ≥7). In the subgroup of patients with equivocal lesions, those with clinically significant cancer had significantly higher PSADs than those with clinically insignificant lesions (means of 0.18 and 0.13 ng/mL/mL, respectively; P= 0.038). In this subgroup, there was no statistical difference in accuracy in determining clinically significant cancer between a PSAD threshold value of 0.13 and DCE (P= 0.25).

CONCLUSIONS

Only 16% of our patients needed DCE to generate the PI-RADS version 2 score, raising the possibility of limiting the initial screening prostate MRI to a noncontrast exam. PSAD may also be used to further decrease the need for or to replace DCE altogether.

Prostate cancer detection with biparametric magnetic resonance imaging (bpMRI) by readers with different experience: performance and comparison with multiparametric (mpMRI)

PURPOSE

To study the detection of clinically significant prostate cancer (PCa) by readers with different experience, comparing performance with biparametric magnetic resonance imaging (bmMRI) and with the reference multiparametric (mpMRI).

METHODS

Retrospective analysis of 68 patients with mpMRI of the prostate at 1.5 Tesla using a 32 phased-array coil. Forty-five patients (cases) underwent radical prostatectomy, whereas 23 (controls) had a negative prostate biopsy and ≥ 2.5 years of negative follow-up. Six observers (two with 1000 cases interpreted, two with 300, two with 100) performed the analysis first with bpMRI including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) imaging in three planes and, after 1 month, with mpMRI, adding dynamic contrast enhancement (DCE). The performance was quantified by sensitivity (SNS), specificity (SPC) and area under the curve (AUC) of the ROC (Receiver Operating Characteristics) procedure.

RESULTS

Concordance within observers of equivalent experience was good (weighted Cohen's k ≈ 0.7). The two expert readers performed as well in bpMRI as in mpMRI (SNS = 0.91-0.96, AUC = 0.86-0.93; p ≥ 0.10); readers with 300 cases performed well in mpMRI, but significantly worse in bpMR: SNS = 0.58 versus 0.91 (p < 0.0001) and AUC = 0.73 versus 0.86 (p = 0.01); the limited experience of readers with 100 cases showed in mpMRI (SNS = 0.71; AUC = 0.77) and even more in bpMRI (SNS = 0.50; AUC = 0.68).

CONCLUSION

The study revealed the impact of the readers' experience when using bpMRI. The bpMRI without contrast media was a valid alternative for expert readers, whereas less experienced ones needed DCE to significantly boost SNS and AUC. Results indicate 700-800 cases as threshold for reliable interpretation with bpMRI.

Prostate Imaging Reporting and Data System Version 2 for MRI of Prostate Cancer: Can We Do Better?

OBJECTIVE. Although the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) has been widely adopted and is generally considered a success, it has clear limitations. The purpose of this article is to highlight the strengths and weaknesses of PI-RADSv2 and discuss ways that it can be improved. CONCLUSION. PI-RADSv2 has improved standardization of acquisition and interpretation of prostate MR images. Although it improves the detection of clinically significant cancers, its subjectivity and intrareader variability limit its accuracy and reproducibility, causing concerns regarding its reliability.

Prostate Imaging-Reporting and Data System Steering Committee: PI-RADS v2 Status Update and Future Directions

CONTEXT

The Prostate Imaging-Reporting and Data System (PI-RADS) v2 analysis system for multiparametric magnetic resonance imaging (mpMRI) detection of prostate cancer (PCa) is based on PI-RADS v1, accumulated scientific evidence, and expert consensus opinion.

OBJECTIVE

To summarize the accuracy, strengths and weaknesses of PI-RADS v2, discuss pathway implications of its use and outline opportunities for improvements and future developments.

EVIDENCE ACQUISITION

For this consensus expert opinion from the PI-RADS steering committee, clinical studies, systematic reviews, and professional guidelines for mpMRI PCa detection were evaluated. We focused on the performance characteristics of PI-RADS v2, comparing data to systems based on clinicoradiologic Likert scales and non-PI-RADS v2 imaging only. Evidence selections were based on high-quality, prospective, histologically verified data, with minimal patient selection and verifications biases.

EVIDENCE SYNTHESIS

It has been shown that the test performance of PI-RADS v2 in research and clinical practice retains higher accuracy over systematic transrectal ultrasound (TRUS) biopsies for PCa diagnosis. PI-RADS v2 fails to detect all cancers but does detect the majority of tumors capable of causing patient harm, which should not be missed. Test performance depends on the definition and prevalence of clinically significant disease. Good performance can be attained in practice when the quality of the diagnostic process can be assured, together with joint working of robustly trained radiologists and urologists, conducting biopsy procedures within multidisciplinary teams.

CONCLUSIONS

It has been shown that the test performance of PI-RADS v2 in research and clinical practice is improved, retaining higher accuracy over systematic TRUS biopsies for PCa diagnosis.

PATIENT SUMMARY

Multiparametric magnetic resonance imaging (MRI) and MRI-directed biopsies using the Prostate Imaging-Reporting and Data System improves the detection of prostate cancers likely to cause harm, and at the same time decreases the detection of disease that does not lead to harms if left untreated. The keys to success are high-quality imaging, reporting, and biopsies by radiologists and urologists working together in multidisciplinary teams.

Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images

Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods. This paper presents a systematic and rigorous framework comprised of classification, cross-validation and statistical analyses that was developed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort. This classifier performed well in an independent validation set, including performing better than PI-RADS v2 in some aspects, indicating the value of objectively interpreting mpMRI images using radiomics and classification methods for PCa risk assessment.

Prostate Magnetic Resonance Imaging: Lesion Detection and Local Staging

Dramatic changes in the use of prostate magnetic resonance imaging (MRI) have occurred in the last decade. The recognition that MRI detects and localizes cancers with reasonable accuracy led to the development of directed biopsies. These image-guided biopsies have a higher sensitivity for clinically significant cancers and a lower sensitivity for indolent disease. Prospective trials provide level 1 evidence supporting the use of prostate MRI. For local staging, while the specificity of prostate MRI is high, its sensitivity is lacking for microscopic extraprostatic extension. Computer-aided diagnosis of prostate MRI promises to bring the diagnostic power of MRI to nonexpert readers and thus further integrate MRI into the diagnostic workup.

Multi-institutional Clinical Tool for Predicting High-risk Lesions on 3Tesla Multiparametric Prostate Magnetic Resonance Imaging

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) for prostate cancer detection without careful patient selection may lead to excessive resource utilization and costs.

OBJECTIVE

To develop and validate a clinical tool for predicting the presence of high-risk lesions on mpMRI.

DESIGN, SETTING, AND PARTICIPANTS

Four tertiary care centers were included in this retrospective and prospective study (BiRCH Study Collaborative). Statistical models were generated using 1269 biopsy-naive, prior negative biopsy, and active surveillance patients who underwent mpMRI. Using age, prostate-specific antigen, and prostate volume, a support vector machine model was developed for predicting the probability of harboring Prostate Imaging Reporting and Data System 4 or 5 lesions. The accuracy of future predictions was then prospectively assessed in 214 consecutive patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Receiver operating characteristic, calibration, and decision curves were generated to assess model performance.

RESULTS AND LIMITATIONS

For biopsy-naïve and prior negative biopsy patients (n=811), the area under the curve (AUC) was 0.730 on internal validation. Excellent calibration and high net clinical benefit were observed. On prospective external validation at two separate institutions (n=88 and n=126), the machine learning model discriminated with AUCs of 0.740 and 0.744, respectively. The final model was developed on the Microsoft Azure Machine Learning platform (birch.azurewebsites.net). This model requires a prostate volume measurement as input.

CONCLUSIONS

In patients who are naïve to biopsy or those with a prior negative biopsy, BiRCH models can be used to select patients for mpMRI.

PATIENT SUMMARY

In this multicenter study, we developed and prospectively validated a calculator that can be used to predict prostate magnetic resonance imaging (MRI) results using patient age, prostate-specific antigen, and prostate volume as input. This tool can aid health care professionals and patients to make an informed decision regarding whether to get an MRI.

A Multireader Exploratory Evaluation of Individual Pulse Sequence Cancer Detection on Prostate Multiparametric Magnetic Resonance Imaging (MRI)

RATIONALE AND OBJECTIVES

To determine independent contribution of each prostate multiparametric magnetic resonance imaging (mpMRI) sequence to cancer detection when read in isolation.

MATERIALS AND METHODS

Prostate mpMRI at 3-Tesla with endorectal coil from 45 patients (n = 30 prostatectomy cases, n = 15 controls with negative magnetic resonance imaging [MRI] or biopsy) were retrospectively interpreted. Sequences (T2-weighted [T2W] MRI, diffusion-weighted imaging [DWI], and dynamic contrast-enhanced [DCE] MRI; N = 135) were separately distributed to three radiologists at different institutions. Readers evaluated each sequence blinded to other mpMRI sequences. Findings were correlated to whole-mount pathology. Cancer detection sensitivity, positive predictive value for whole prostate (WP), transition zone, and peripheral zone were evaluated per sequence by reader, with reader concordance measured by index of specific agreement. Cancer detection rates (CDRs) were calculated for combinations of independently read sequences.

RESULTS

44 patients were evaluable (cases median prostate-specific antigen 6.83 [ range 1.95-51.13] ng/mL, age 62 [45-71] years; controls prostate-specific antigen 6.85 [2.4-10.87] ng/mL, age 65.5 [47-71] years). Readers had highest sensitivity on DWI (59%) vs T2W MRI (48%) and DCE (23%) in WP. DWI-only positivity (DWI+/T2W-/DCE-) achieved highest CDR in WP (38%), compared to T2W-only (CDR 24%) and DCE-only (CDR 8%). DWI+/T2W+/DCE- achieved CDR 80%, an added benefit of 56.4% from T2W-only and of 42% from DWI-only (P < .0001). All three sequences interpreted independently positive gave highest CDR of 90%. Reader agreement was moderate (index of specific agreement: T2W = 54%, DWI = 58%, DCE = 33%).

CONCLUSIONS

When prostate mpMRI sequences are interpreted independently by multiple observers, DWI achieves highest sensitivity and CDR in transition zone and peripheral zone. T2W and DCE MRI both add value to detection; mpMRI achieves highest detection sensitivity when all three mpMRI sequences are positive.

Performance of T2 Maps in the Detection of Prostate Cancer

RATIONALE AND OBJECTIVES

This study compares the performance of T2 maps in the detection of prostate cancer (PCa) in comparison to T2-weighted (T2W) magnetic resonance images.

MATERIALS AND METHODS

The prospective study was institutional review board approved. Consenting patients (n = 45) with histologic confirmed PCa underwent preoperative 3-T magnetic resonance imaging with or without endorectal coil. Two radiologists, working independently, marked regions of interests (ROIs) on PCa lesions separately on T2W images and T2 maps. Each ROI was assigned a score of 1-5 based on the confidence in accurately detecting cancer, with 5 being the highest confidence. Subsequently, the histologically confirmed PCa lesions (n = 112) on whole-mount sections were matched with ROIs to calculate sensitivity, positive predictive value (PPV), and radiologist confidence score. Quantitative T2 values of PCa and benign tissue ROIs were measured.

RESULTS

Sensitivity and confidence score for PCa detection were similar for T2W images (51%, 4.5 ± 0.8) and T2 maps (52%, 4.5 ± 0.6). However, PPV was significantly higher (P = .001) for T2 maps (88%) compared to T2W (72%) images. The use of endorectal coils nominally improved sensitivity (T2W: 55 vs 47%, T2 map: 54% vs 48%) compared to the use of no endorectal coils, but not the PPV and the confidence score. Quantitative T2 values for PCa (105 ± 28 milliseconds) were significantly (P = 9.3 × 10^-14) lower than benign peripheral zone tissue (211 ± 71 milliseconds), with moderate significant correlation with Gleason score (ρ = -0.284).

CONCLUSIONS

Our study shows that review of T2 maps by radiologists has similar sensitivity but higher PPV compared to T2W images. Additional quantitative information obtained from T2 maps is helpful in differentiating cancer from normal prostate tissue and determining its aggressiveness.

The current role of prostate multiparametric magnetic resonance imaging

Prostate multi-parametric magnetic resonance imaging (mpMRI) has shown excellent sensitivity for Gleason ≥7 cancers, especially when their volume is ≥0.5 mL. As a result, performing an mpMRI before prostate biopsy could improve the detection of clinically significant prostate cancer (csPCa) by adding targeted biopsies to systematic biopsies. Currently, there is a consensus that targeted biopsies improve the detection of csPCa in the repeat biopsy setting and at confirmatory biopsy in patients considering active surveillance. Several prospective multicentric controlled trials recently showed that targeted biopsy also improved csPCa detection in biopsy-naïve patients. The role of mpMRI and targeted biopsy during the follow-up of active surveillance remains unclear. Whether systematic biopsy could be omitted in case of negative mpMRI is also a matter of controversy. mpMRI did show excellent negative predictive values (NPV) in the literature, however, since NPV depends on the prevalence of the disease, negative mpMRI findings should be interpreted in the light of a priori risk for csPCa of the patient. Nomograms combining mpMRI findings and classical risk predictors (age, prostate-specific antigen density, digital rectal examination, etc.) will probably be developed in the future to decide whether a prostate biopsy should be obtained. mpMRI has a good specificity for detecting T3 stage cancers, but its sensitivity is low. It should therefore not be used routinely for staging purposes in low-risk patients. Nomograms combining mpMRI findings and other clinical and biochemical data will also probably be used in the future to better assess the risk of T3 stage disease.

Imaging as a Personalized Biomarker for Prostate Cancer Risk Stratification

Biomarkers provide objective data to guide clinicians in disease management. Prostate-specific antigen serves as a biomarker for screening of prostate cancer but has come under scrutiny for detection of clinically indolent disease. Multiple imaging techniques demonstrate promising results for diagnosing, staging, and determining definitive management of prostate cancer. One such modality, multiparametric magnetic resonance imaging (mpMRI), detects more clinically significant disease while missing lower volume and clinically insignificant disease. It also provides valuable information regarding tumor characteristics such as location and extraprostatic extension to guide surgical planning. Information from mpMRI may also help patients avoid unnecessary biopsies in the future. It can also be incorporated into targeted biopsies as well as following patients on active surveillance. Other novel techniques have also been developed to detect metastatic disease with advantages over traditional computer tomography and magnetic resonance imaging, which primarily rely on defined size criteria. These new techniques take advantage of underlying biological changes in prostate cancer tissue to identify metastatic disease. The purpose of this review is to present literature on imaging as a personalized biomarker for prostate cancer risk stratification.

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

BACKGROUND

The diagnostic pathway for prostate cancer (PCa) is advancing towards an imaging-driven approach. Multiparametric magnetic resonance imaging, although increasingly used, has not shown sufficient accuracy to replace biopsy for now. The introduction of new ultrasound (US) modalities, such as quantitative contrast-enhanced US (CEUS) and shear wave elastography (SWE), shows promise but is not evidenced by sufficient high quality studies, especially for the combination of different US modalities. The primary objective of this study is to determine the individual and complementary diagnostic performance of greyscale US (GS), SWE, CEUS and their combination, multiparametric ultrasound (mpUS), for the detection and localization of PCa by comparison with corresponding histopathology.

METHODS/DESIGN

In this prospective clinical trial, US imaging consisting of GS, SWE and CEUS with quantitative mapping on 3 prostate imaging planes (base, mid and apex) will be performed in 50 patients with biopsy-proven PCa before planned radical prostatectomy using a clinical ultrasound scanner. All US imaging will be evaluated by US readers, scoring the four quadrants of each imaging plane for the likelihood of significant PCa based on a 1 to 5 Likert Scale. Following resection, PCa tumour foci will be identified, graded and attributed to the imaging-derived quadrants in each prostate plane for all prostatectomy specimens. Primary outcome measure will be the sensitivity, specificity, negative predictive value and positive predictive value of each US modality and mpUS to detect and localize significant PCa evaluated for different Likert Scale thresholds using receiver operating characteristics curve analyses.

DISCUSSION

In the evaluation of new PCa imaging modalities, a structured comparison with gold standard radical prostatectomy specimens is essential as first step. This trial is the first to combine the most promising ultrasound modalities into mpUS. It complies with the IDEAL stage 2b recommendations and will be an important step towards the evaluation of mpUS as a possible option for accurate detection and localization of PCa.

TRIAL REGISTRATION

The study protocol for multiparametric ultrasound was prospectively registered on Clinicaltrials.gov on 14 March 2017 with the registry name 'Multiparametric Ultrasound-Study for the Detection of Prostate Cancer' and trial registration number NCT03091231.

Prospective comparison of PI-RADS version 2 and qualitative in-house categorization system in detection of prostate cancer

BACKGROUND

Prostate Imaging-Reporting and Data System v. 2 (PI-RADSv2) provides standardized nomenclature for interpretation of prostate multiparametric MRI (mpMRI). Inclusion of additional features for categorization may provide benefit to stratification of disease.

PURPOSE

To prospectively compare PI-RADSv2 to a qualitative in-house system for detecting prostate cancer on mpMRI.

STUDY TYPE

Prospective.

POPULATION

In all, 338 patients who underwent mpMRI May 2015-May 2016, with subsequent MRI/transrectal ultrasound fusion-guided biopsy.

FIELD STRENGTH

3T mpMRI (T2 W, diffusion-weighted [DW], apparent diffusion coefficient [ADC] map, b-2000 DWI acquisition, and dynamic contrast-enhanced [DCE] MRI).

ASSESSMENT

One genitourinary radiologist prospectively read mpMRIs using both in-house and PI-RADSv2 5-category systems.

STATISTICAL TEST

In lesion-based analysis, overall and clinically significant (CS) tumor detection rates (TDR) were calculated for all PI-RADSv2 and in-house categories. The ability of each scoring system to detect cancer was assessed by area under receiver operator characteristic curve (AUC). Within each PI-RADSv2 category, lesions were further stratified by their in-house categories to determine if TDRs can be increased by combining features of both systems.

RESULTS

In 338 patients (median prostate-specific antigen [PSA] 6.5 [0.6-113.6] ng/mL; age 64 [44-84] years), 733 lesions were identified (47% tumor-positive). Predictive abilities of both systems were comparable for all (AUC 76-78%) and CS cancers (AUCs 79%). The in-house system had higher overall and CS TDRs than PI-RADSv2 for categories 3 and 4 (P < 0.01 for both), with the greatest difference between the scoring systems seen in lesions scored category 4 (CS TDRs: in-house 65%, PI-RADSv2 22.1%). For lesions categorized as PI-RADSv2 = 4, characterization of suspicious/indeterminate extraprostatic extension (EPE) and equivocal findings across all mpMRI sequences contributed to significantly different TDRs for both systems (TDR range 19-75%, P < 0.05).

DATA CONCLUSION

PI-RADSv2 behaves similarly to an existing validated system that relies on the number of sequences on which a lesion is seen. This prospective evaluation suggests that sequence positivity and suspicion of EPE can enhance PI-RADSv2 category 4 cancer detection.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1326-1335.

Accuracy and Variability of Prostate Multiparametric Magnetic Resonance Imaging Interpretation Using the Prostate Imaging Reporting and Data System: A Blinded Comparison of Radiologists

BACKGROUND

Multiparametric (mp) magnetic resonance imaging (MRI) has become an important tool for the detection of clinically significant prostate cancer. However, diagnostic accuracy is affected by variability between radiologists.

OBJECTIVE

To determine the accuracy and variability in prostate mpMRI interpretation among radiologists, both individually and in teams, in a blinded fashion.

DESIGN, SETTING, AND PARTICIPANTS

A study cohort (n=32) was created from our prospective registry of patients who received prostate mpMRI with subsequent biopsy. The cohort was then independently reviewed by four radiologists of varying levels of experience, who assigned a Prostate Imaging Reporting and Data System (PI-RADS) classification, blinded to all clinical information. Consensus interpretation by teams of two radiologists was evaluated after a 12-wk wash-out period. Interpretive accuracy was calculated with various cutoffs for PI-RADS classification and Gleason score. Variability among individual radiologists and teams was calculated using the Fleiss kappa and intraclass correlation coefficient (ICC).

RESULTS AND LIMITATIONS

Using PI-RADS 3+/Gleason 7+ (p<0.01) and PI-RADS 4+/Gleason 6+ (p=0.02) as cutoffs, significant differences in accuracy among the four radiologists were noted. At no cutoff for PI-RADS classification or Gleason score did a team read achieve higher accuracy than the most accurate radiologist. The kappa and ICC ranged from 0.22 to 0.29 for the individuals and from 0.16 to 0.21 for the teams (poor agreement). A larger sample size may be needed to adequately power differences in accuracy among individual radiologists.

CONCLUSIONS

At various cutoffs for PI-RADS classification and Gleason score, we find significant differences in individual radiologist accuracy, as well as a poor agreement among individual radiologists. Consensus interpretations-as teams of two radiologists-did not improve accuracy or reduce variability.

PATIENT SUMMARY

This study investigated radiologist variability and differences in accuracy using multiparametric magnetic resonance imaging for the diagnosis of prostate cancer. Despite attempts to standardize interpretation within the field, we found substantial variability and significant differences in accuracy among individual radiologists.

The Problems with the Kappa Statistic as a Metric of Interobserver Agreement on Lesion Detection Using a Third-reader Approach When Locations Are Not Prespecified

RATIONALE AND OBJECTIVES

To point out the problems with Cohen kappa statistic and to explore alternative metrics to determine interobserver agreement on lesion detection when locations are not prespecified.

MATERIALS AND METHODS

Use of kappa and two alternative methods, namely index of specific agreement (ISA) and modified kappa, for measuring interobserver agreement on the location of detected lesions are presented. These indices of agreement are illustrated by application to a retrospective multireader study in which nine readers detected and scored prostate cancer lesions in 163 consecutive patients (n = 110 cases, n = 53 controls) using the guideline of Prostate Imaging Reporting and Data System version 2 on multiparametric magnetic resonance imaging.

RESULTS

The proposed modified kappa, which properly corrects for the amount of agreement by chance, is shown to be approximately equivalent to the ISA. In the prostate cancer data, average kappa, modified kappa, and ISA equaled 30%, 55%, and 57%, respectively, for all lesions and 20%, 87%, and 87%, respectively, for index lesions.

CONCLUSIONS

The application of kappa could result in a substantial downward bias in reader agreement on lesion detection when locations are not prespecified. ISA is recommended for assessment of reader agreement on lesion detection.

Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation

For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development.

Interreader agreement of PI-RADS v. 2 in assessing prostate cancer with multiparametric MRI: A study using whole-mount histology as the standard of reference

BACKGROUND

Most studies assessing interreader agreement of Prostate Imaging Reporting and Data System v. 2 (PI-RADS v2) have used biopsy as the standard of reference, thus carrying the risk of not definitively noting all existent cancers.

PURPOSE

To evaluate the interreader agreement in assessing prostate cancer (PCa) of PI-RADS v2, using whole-mount histology as the standard of reference.

STUDY TYPE

Monocentric prospective cohort study.

POPULATION

In all, 48 patients with biopsy-proven PCa referred for radical prostatectomy, undergoing staging multiparametric magnetic resonance imaging (mpMRI) between May 2016 to February 2017.

FIELD STRENGTH/SEQUENCE

3.0T system using high-resolution T₂ -weighted imaging, diffusion-weighted imaging (echo-planar imaging with maximum b-value 2000 sec/mm² ), and dynamic contrast-enhanced imaging (T₁ -weighted high resolution isotropic volume examination; THRIVE) ASSESSMENT: Three radiologists blinded to final histology (2-8 years of experience) analyzed mpMRI images independently, scoring imaging findings in accordance with PI-RADS v2. On a per-lesion basis, we calculated overall and pairwise interreader agreement in assigning PI-RADS categories, as well as assessing malignancy with categories ≥3 or ≥4, and stage ≥pT3.

STATISTICAL TESTS

Cohen's kappa analysis of agreement.

RESULTS

On 71 lesions found on histology, there was moderate agreement in assigning PI-RADS categories to all cancers (k = 0.53) and clinically significant cancers (csPCa) (k = 0.47). Assessing csPCa with PI-RADS ≥4 cutoff provided higher agreement than PI-RADS ≥3 cutoff (k = 0.63 vs. 0.57). Interreader agreement was higher between more experienced readers, with the most experienced one achieving the highest cancer detection rate (0.73 for csPCa using category ≥4). There was substantial agreement in assessing stage ≥pT3 (k = 0.72).

DATA CONCLUSION

We found moderate to substantial agreement in assigning the PI-RADS v2 categories and assessing the spectrum of cancers found on whole-mount histology, with category 4 as the most reproducible cutoff for csPCa. Readers' experience influenced interreader agreement and cancer detection rate.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:546-555.

Validation of PI-RADS Version 2 in Transition Zone Lesions for the Detection of Prostate Cancer

Purpose To determine the association between Prostate Imaging Reporting and Data System (PI-RADS) version 2 scores and prostate cancer (PCa) in a cohort of patients undergoing biopsy of transition zone (TZ) lesions. Materials and Methods A total of 634 TZ lesions in 457 patients were identified from a prospectively maintained database of consecutive patients undergoing prostate magnetic resonance imaging. Prostate lesions were retrospectively categorized with the PI-RADS version 2 system by two readers in consensus who were blinded to histopathologic findings. The proportion of cancer detection for all PCa and for clinically important PCa (Gleason score ≥3+4) for each PI-RADS version 2 category was determined. The performance of PI-RADS version 2 in cancer detection was evaluated. Results For PI-RADS category 2 lesions, the overall proportion of cancers was 4% (one of 25), without any clinically important cancer. For PI-RADS category 3, 4, and 5 lesions, the overall proportion of cancers was 22.2% (78 of 352), 39.1% (43 of 110), and 87.8% (129 of 147), respectively, and the proportion of clinically important cancers was 11.1% (39 of 352), 29.1% (32 of 110), and 77.6% (114 of 147), respectively. Higher PI-RADS version 2 scores were associated with increasing likelihood of the presence of clinically important PCa (P < .001). Differences were found in the percentage of cancers in the PI-RADS category between PI-RADS 3 and those upgraded to PI-RADS 4 based on diffusion-weighted imaging for clinically important cancers (proportion for clinically important cancers for PI-RADS 3 and PI-RADS 3+1 were 11.1% [39 of 352] and 30.8% [28 of 91], respectively; P < .001). Conclusion Higher PI-RADS version 2 scores are associated with a higher proportion of clinically important cancers in the TZ. PI-RADS category 2 lesions rarely yield PCa, and their presence does not justify targeted biopsy.

All over the map: An interobserver agreement study of tumor location based on the PI-RADSv2 sector map

BACKGROUND

Prostate imaging reporting and data system version 2 (PI-RADSv2) recommends a sector map for reporting findings of prostate cancer mulitparametric MRI (mpMRI). Anecdotally, radiologists may demonstrate inconsistent reproducibility with this map.

PURPOSE

To evaluate interobserver agreement in defining prostate tumor location on mpMRI using the PI-RADSv2 sector map.

STUDY TYPE

Retrospective.

POPULATION

Thirty consecutive patients who underwent mpMRI between October, 2013 and March, 2015 and who subsequently underwent prostatectomy with whole-mount processing.

FIELD STRENGTH

3T mpMRI with T2 W, diffusion-weighted imaging (DWI) (apparent diffusion coefficient [ADC] and b-2000), dynamic contrast-enhanced (DCE).

ASSESSMENT

Six radiologists (two high, two intermediate, and two low experience) from six institutions participated. Readers were blinded to lesion location and detected up to four lesions as per PI-RADSv2 guidelines. Readers marked the long-axis of lesions, saved screen-shots of each lesion, and then marked the lesion location on the PI-RADSv2 sector map. Whole-mount prostatectomy specimens registered to the MRI served as ground truth. Index lesions were defined as the highest grade lesion or largest lesion if grades were equivalent.

STATISTICAL TEST

Agreement was calculated for the exact, overlap, and proportion of agreement.

RESULTS

Readers detected an average of 1.9 lesions per patient (range 1.6-2.3). 96.3% (335/348) of all lesions for all readers were scored PI-RADS ≥3. Readers defined a median of 2 (range 1-18) sectors per lesion. Agreement for detecting index lesions by screen shots was 83.7% (76.1%-89.9%) vs. 71.0% (63.1-78.3%) overlap agreement on the PI-RADS sector map (P < 0.001). Exact agreement for defining sectors of detected index lesions was only 21.2% (95% confidence interval [CI]: 14.4-27.7%) and rose to 49.0% (42.4-55.3%) when overlap was considered. Agreement on defining the same level of disease (ie, apex, mid, base) was 61.4% (95% CI 50.2-71.8%).

DATA CONCLUSION

Readers are highly likely to detect the same index lesion on mpMRI, but exhibit poor reproducibility when attempting to define tumor location on the PI-RADSv2 sector map. The poor agreement of the PI-RADSv2 sector map raises concerns its utility in clinical practice.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:482-490.

Can Apparent Diffusion Coefficient Values Assist PI-RADS Version 2 DWI Scoring? A Correlation Study Using the PI-RADSv2 and International Society of Urological Pathology Systems

OBJECTIVE

The purposes of this study were to assess correlation of apparent diffusion coefficient (ADC) and normalized ADC (ratio of tumor to nontumor tissue) with the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and updated International Society of Urological Pathology (ISUP) categories and to determine how to optimally use ADC metrics for objective assistance in categorizing lesions within PI-RADSv2 guidelines.

MATERIALS AND METHODS

In this retrospective study, 100 patients (median age, 62 years; range, 44-75 years; prostate-specific antigen level, 7.18 ng/mL; range, 1.70-84.56 ng/mL) underwent 3-T multiparametric MRI of the prostate with an endorectal coil. Mean ADC was extracted from ROIs based on subsequent prostatectomy specimens. Histopathologic analysis revealed 172 lesions (113 peripheral, 59 transition zone). Two radiologists blinded to histopathologic outcome assigned PI-RADSv2 categories. Kendall tau was used to correlate ADC metrics with PI-RADSv2 and ISUP categories. ROC curves were used to assess the utility of ADC metrics in differentiating each reader's PI-RADSv2 DWI category 4 or 5 assessment in the whole prostate and by zone.

RESULTS

ADC metrics negatively correlated with ISUP category in the whole prostate (ADC, τ = -0.21, p = 0.0002; normalized ADC, τ = -0.21, p = 0.0001). Moderate negative correlation was found in expert PI-RADSv2 DWI categories (ADC, τ = -0.34; normalized ADC, τ = -0.31; each p < 0.0001) maintained across zones. In the whole prostate, AUCs of ADC and normalized ADC were 87% and 82% for predicting expert PI-RADSv2 DWI category 4 or 5. A derived optimal cutoff ADC less than 1061 and normalized ADC less than 0.65 achieved positive predictive values of 83% and 84% for correct classification of PI-RADSv2 DWI category 4 or 5 by an expert reader. Consistent relations and predictive values were found by an independent novice reader.

CONCLUSION

ADC and normalized ADC inversely correlate with PI-RADSv2 and ISUP categories and can serve as quantitative metrics to assist with assigning PI-RADSv2 DWI category 4 or 5.

Radiologists' preferences regarding content of prostate MRI reports: a survey of the Society of Abdominal Radiology

PURPOSE

To evaluate radiologist preferences regarding specific content that warrants inclusion in prostate MRI reports.

METHODS

Sixty-one members of the Society of Abdominal Radiology responded to a 74-item survey regarding specific content warranted in prostate MRI reports, conducted in August 2016.

RESULTS

General items deemed essential report content by ≥ 50% of respondents were prostate volume (80%), extent of prostate hemorrhage (74%), TURP defects (69%), coil type (64%), BPH (61%), contrast dose (61%), contrast agent (59%), medications administered (59%), and magnet strength (54%). Details regarding lesion description deemed essential by ≥ 50% were overall PI-RADS category (88%), DCE (±) (82%), subjective degree of diffusion restriction (72%), T2WI intensity (72%), T2WI margins (65%), T2WI shape (52%), DWI 1-5 score (50%), and T2WI 1-5 score (50%). Details deemed essential to include in the report Impression by ≥ 50% of respondents were lymphadenopathy and metastases (100%), EPE (98%), SVI (98%), neurovascular bundle involvement (93%), index lesion location (93%), PI-RADS category of index lesion (82%), number of suspicious lesions (78%), significance of index lesion PI-RADS category (53%), and PI-RADS category of non-index lesions (52%). Preferred methods for lesion localization were slice/image number (68%), 3-part craniocaudal level (68%), zonal location (65%), anterior vs. posterior location (57%), and medial vs. lateral position (56%). Least preferred methods for localization were numeric sector from the PI-RADS sector map (8%), annotated screen capture (10%), and graphical schematic of PI-RADS sector map (11%).

CONCLUSION

Radiologists generally deemed a high level of detail warranted in prostate MRI reports. The PI-RADS v2 sector map was disliked for lesion localization.

Follow-up of negative MRI-targeted prostate biopsies: when are we missing cancer?

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI) has improved clinicians' ability to detect clinically significant prostate cancer (csPCa). Combining or fusing these images with the real-time imaging of transrectal ultrasound (TRUS) allows urologists to better sample lesions with a targeted biopsy (Tbx) leading to the detection of greater rates of csPCa and decreased rates of low-risk PCa. In this review, we evaluate the technical aspects of the mpMRI-guided Tbx procedure to identify possible sources of error and provide clinical context to a negative Tbx.

METHODS

A literature search was conducted of possible reasons for false-negative TBx. This includes discussion on false-positive mpMRI findings, termed "PCa mimics," that may incorrectly suggest high likelihood of csPCa as well as errors during Tbx resulting in inexact image fusion or biopsy needle placement.

RESULTS

Despite the strong negative predictive value associated with Tbx, concerns of missed disease often remain, especially with MR-visible lesions. This raises questions about what to do next after a negative Tbx result. Potential sources of error can arise from each step in the targeted biopsy process ranging from "PCa mimics" or technical errors during mpMRI acquisition to failure to properly register MRI and TRUS images on a fusion biopsy platform to technical or anatomic limits on needle placement accuracy.

CONCLUSIONS

A better understanding of these potential pitfalls in the mpMRI-guided Tbx procedure will aid interpretation of a negative Tbx, identify areas for improving technical proficiency, and improve both physician understanding of negative Tbx and patient-management options.

Multiparametric Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy-Are 2 Biopsy Cores per Magnetic Resonance Imaging Lesion Required?

PURPOSE

For multiparametric magnetic resonance imaging/ultrasound fusion prostate biopsy the number of biopsy cores obtained is arbitrarily established by urologists. Moreover, a general consensus is lacking on the number of biopsy cores to be obtained from a single magnetic resonance imaging lesion. Therefore, we evaluated the feasibility of obtaining only 1 biopsy core per magnetic resonance imaging lesion.

MATERIALS AND METHODS

We retrospectively evaluated a total of 2,128 biopsy cores of 1,064 prostatic lesions (2 cores per lesion) in 418 patients in regard to prostate cancer detection (histology) and the Gleason score of the first biopsy core compared to the second biopsy core. Two analyses were performed, including patient level analysis based on prostate cancer detection per patient and lesion level analysis based exclusively on the histology of each lesion regardless of the overall histological outcome of the case.

RESULTS

The overall prostate cancer detection rate was 45.7% (191 of 418 patients). The first biopsy core detected 170 of all 191 prostate cancers (89%). In 17 of these 170 prostate cancers (10%) the second biopsy core revealed Gleason score upgrading. Nine of the 21 prostate cancers (43%) missed by the first biopsy core had a Gleason score of 6. Altogether 537 of the 2,128 biopsy cores were positive, including 283 first (26.6%) and 254 second (24%) biopsy cores (p ≤0.001). The concordance between the first and second biopsy cores was 89% (κ = 0.71). There was a discrepancy with Gleason score upgrading in 28 of 212 lesions (13.2%) with positive first and second biopsy cores.

CONCLUSIONS

Our study shows that obtaining more than 1 biopsy core per magnetic resonance imaging lesion only slightly improves the prostate cancer detection rate and Gleason grading.

PI-RADS v2: Current standing and future outlook

The Prostate Imaging-Reporting and Data System (PI-RADS) was created in 2012 to establish standardization in prostate multiparametric magnetic resonance imaging (mpMRI) acquisition, interpretation, and reporting. In hopes of improving upon some of the PI-RADS v1 shortcomings, the PI-RADS Steering Committee released PI-RADS v2 in 2015. This paper reviews the accuracy, interobserver agreement, and clinical outcomes of PI-RADS v2 and comments on the limitations of the current literature. Overall, PI-RADS v2 shows improved sensitivity and similar specificity compared to PI-RADS v1. However, concerns exist regarding interobserver agreement and the heterogeneity of the study methodology.

Inter-observer agreement of the Coronary Artery Disease Reporting and Data System (CAD-RADSTM) in patients with stable chest pain

Purpose

To assess inter-observer variability of the Coronary Artery Disease - Reporting and Data System (CAD-RADS) for classifying the degree of coronary artery stenosis in patients with stable chest pain.

Material and methods

A prospective study was conducted upon 96 patients with coronary artery disease, who underwent coronary computed tomography angiography (CTA). The images were classified using the CAD-RAD system according to the degree of stenosis, the presence of a modifier: graft (G), stent (S), vulnerable plaque (V), or non-diagnostic (n) and the associated coronary anomalies, and non-coronary cardiac and extra-cardiac findings. Image analysis was performed by two reviewers. Inter-observer agreement was assessed.

Results

There was excellent inter-observer agreement for CAD-RADS (k = 0.862), at 88.5%. There was excellent agreement for CAD-RADS 0 (k = 1.0), CAD-RADS 1 (k = 0.92), CAD-RADS 3 (k = 0.808), CAD-RADS 4 (k = 0.826), and CAD-RADS 5 (k = 0.833) and good agreement for CAD-RADS 2 (k = 0.76). There was excellent agreement for modifier G (k = 1.0) and modifier S (k = 1.0), good agreement for modifier N (k = 0.79), and moderate agreement for modifier V (k = 0.59). There was excellent agreement for associated coronary artery anomalies (k = 0.845), non-coronary cardiac findings (k = 0.857), and extra-cardiac findings (k = 0.81).

Conclusions

There is inter-observer agreement of CAD-RADS in categorising the degree of coronary arteries stenosis, and the modifier of the system and associated cardiac and extra-cardiac findings.

Characterizing indeterminate (Likert-score 3/5) peripheral zone prostate lesions with PSA density, PI-RADS scoring and qualitative descriptors on multiparametric MRI

OBJECTIVE

To determine whether indeterminate (Likert-score 3/5) peripheral zone (PZ) multiparametric MRI (mpMRI) studies are classifiable by prostate-specific antigen (PSA), PSA density (PSAD), Prostate Imaging Reporting And Data System version 2 (PI-RADS_v2) rescoring and morphological MRI features.

METHODS

Men with maximum Likert-score 3/5 within their PZ were retrospectively selected from 330 patients who prospectively underwent prostate mpMRI (3 T) without an endorectal coil, followed by 20-zone transperineal template prostate mapping biopsies ＋/- focal lesion-targeted biopsy. PSAD was calculated using pre-biopsy PSA and MRI-derived volume. Two readers A and B independently assessed included men with both Likert-assessment and PI-RADS_v2. Both readers then classified mpMRI morphological features in consensus. Men were divided into two groups: significant cancer (≥ Gleason 3 ＋ 4) or insignificant cancer (≤ Gleason 3 ＋ 3)/no cancer. Comparisons between groups were made separately for PSA & PSAD using Mann-Whitney test and morphological descriptors with Fisher's exact test. PI-RADS_v2 and Likert-assessment were descriptively compared and percentage inter-reader agreement calculated.

RESULTS

76 males were eligible for PSA & PSAD analyses, 71 for PI-RADS scoring, and 67 for morphological assessment (excluding significant image artefacts). Unlike PSA (p = 0.915), PSAD was statistically different (p = 0.004) between the significant [median: 0.19 ng ml-2 (interquartile range: 0.13-0.29)] and non-significant/no cancer [median: 0.13 ng ml-2 (interquartile range: 0.10-0.17)] groups. Presence of mpMRI morphological features was not significantly different between groups. Subjective Likert-assessment discriminated patients with significant cancer better than PI-RADS_v2. Inter-reader percentage agreement was 83% for subjective Likert-assessment and 56% for PI-RADS_v2.

CONCLUSION

PSAD may categorize presence of significant cancer in patients with Likert-scored 3/5 PZ mpMRI findings. Advances in knowledge: PSAD may be used in indeterminate PZ mpMRI to guide decisions between biopsy vs monitoring.

Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2

Purpose To validate the dominant pulse sequence paradigm and limited role of dynamic contrast material-enhanced magnetic resonance (MR) imaging in the Prostate Imaging Reporting and Data System (PI-RADS) version 2 for prostate multiparametric MR imaging by using data from a multireader study. Materials and Methods This HIPAA-compliant retrospective interpretation of prospectively acquired data was approved by the local ethics committee. Patients were treatment-naïve with endorectal coil 3-T multiparametric MR imaging. A total of 163 patients were evaluated, 110 with prostatectomy after multiparametric MR imaging and 53 with negative multiparametric MR imaging and systematic biopsy findings. Nine radiologists participated in this study and interpreted images in 58 patients, on average (range, 56-60 patients). Lesions were detected with PI-RADS version 2 and were compared with whole-mount prostatectomy findings. Probability of cancer detection for overall, T2-weighted, and diffusion-weighted (DW) imaging PI-RADS scores was calculated in the peripheral zone (PZ) and transition zone (TZ) by using generalized estimating equations. To determine dominant pulse sequence and benefit of dynamic contrast-enhanced (DCE) imaging, odds ratios (ORs) were calculated as the ratio of odds of cancer of two consecutive scores by logistic regression. Results A total of 654 lesions (420 in the PZ) were detected. The probability of cancer detection for PI-RADS category 2, 3, 4, and 5 lesions was 15.7%, 33.1%, 70.5%, and 90.7%, respectively. DW imaging outperformed T2-weighted imaging in the PZ (OR, 3.49 vs 2.45; P = .008). T2-weighted imaging performed better but did not clearly outperform DW imaging in the TZ (OR, 4.79 vs 3.77; P = .494). Lesions classified as PI-RADS category 3 at DW MR imaging and as positive at DCE imaging in the PZ showed a higher probability of cancer detection than did DCE-negative PI-RADS category 3 lesions (67.8% vs 40.0%, P = .02). The addition of DCE imaging to DW imaging in the PZ was beneficial (OR, 2.0; P = .027), with an increase in the probability of cancer detection of 15.7%, 16.0%, and 9.2% for PI-RADS category 2, 3, and 4 lesions, respectively. Conclusion DW imaging outperforms T2-weighted imaging in the PZ; T2-weighted imaging did not show a significant difference when compared with DW imaging in the TZ by PI-RADS version 2 criteria. The addition of DCE imaging to DW imaging scores in the PZ yields meaningful improvements in probability of cancer detection. © RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on July 27, 2017. Online supplemental material is available for this article.

Risk of Clinically Significant Prostate Cancer Associated With Prostate Imaging Reporting and Data System Category 3 (Equivocal) Lesions Identified on Multiparametric Prostate MRI

OBJECTIVE

The objective of this study is to determine the frequency of clinically significant cancer (CSC) in Prostate Imaging Reporting and Data System (PI-RADS) category 3 (equivocal) lesions prospectively identified on multiparametric prostate MRI and to identify risk factors (RFs) for CSC that may aid in decision making.

MATERIALS AND METHODS

Between January 2015 and July 2016, a total of 977 consecutively seen men underwent multiparametric prostate MRI, and 342 underwent MRI-ultrasound (US) fusion targeted biopsy. A total of 474 lesions were retrospectively reviewed, and 111 were scored as PI-RADS category 3 and were visualized using a 3-T MRI scanner. Multiparametric prostate MR images were prospectively interpreted by body subspecialty radiologists trained to use PI-RADS version 2. CSC was defined as a Gleason score of at least 7 on targeted biopsy. A multivariate logistic regression model was constructed to identify the RFs associated with CSC.

RESULTS

Of the 111 PI-RADS category 3 lesions, 81 (73.0%) were benign, 11 (9.9%) were clinically insignificant (Gleason score, 6), and 19 (17.1%) were clinically significant. On multivariate analysis, three RFs were identified as significant predictors of CSC: older patient age (odds ratio [OR], 1.13; p = 0.002), smaller prostate volume (OR, 0.94; p = 0.008), and abnormal digital rectal examination (DRE) findings (OR, 3.92; p = 0.03). For PI-RADS category 3 lesions associated with zero, one, two, or three RFs, the risk of CSC was 4%, 16%, 62%, and 100%, respectively. PI-RADS category 3 lesions for which two or more RFs were noted (e.g., age ≥ 70 years, gland size ≤ 36 mL, or abnormal DRE findings) had a CSC detection rate of 67% with a sensitivity of 53%, a specificity of 95%, a positive predictive value of 67%, and a negative predictive value of 91%.

CONCLUSION

Incorporating clinical parameters into risk stratification algorithms may improve the ability to detect clinically significant disease among PI-RADS category 3 lesions and may aid in the decision to perform biopsy.

Multimodality Image Fusion with PSMA PET/CT and High-Intensity Focused Ultrasound Focal Therapy for Primary Diagnosis and Management of Prostate Cancer: A Planned Research Initiative

Recent developments in diagnostic imaging herald a new approach to diagnosis and management of prostate cancer. Multimodality fusion that combines anatomic with functional imaging data has surpassed either of the two alone. This opens up the possibility to "find and fix" malignancy with greater accuracy than ever before. This is particularly important for prostate cancer because it is the most common male cancer in most developed countries. This article describes technical advances under investigation at our institution and others using multimodality image fusion of magnetic resonance imaging (MRI), transrectal ultrasound (TRUS), and PSMA PET/CT (defined as the combination of prostate-specific membrane antigen [PSMA], positron emission tomography [PET], and computed tomography [CT]) for personalized medicine in the diagnosis and focal therapy of prostate cancer with high-intensity focused ultrasound (HiFUS).

Preventing clinical progression and need for treatment in patients on active surveillance for prostate cancer

PURPOSE OF REVIEW

Active surveillance is an established treatment option for men with localized, low-risk prostate cancer (CaP). It entails the postponement of immediate therapy with the option of delayed intervention upon disease progression. The rate of clinical progression and need for treatment on active surveillance is approximately 50% over 15 years. The present review summarizes recent data on current methods, attempting to prevent clinical progression.

RECENT FINDINGS

Patient selection for active surveillance is the first mandatory step required to lower progression. Adherence to active surveillance protocols is critical in making sure patients are monitored well and treated early when progression occurs. Before active surveillance allocation and during active surveillance follow-up, methods involving multiparametric MRI, prostate specific antigen derivatives, biopsy factors, urinary, tissue and genetic markers can be used to prevent clinical progression and/or identify those at risk for progression. Medications such as 5α-reductase inhibitors and others might inhibit disease progression in patients on active surveillance.

SUMMARY

Active surveillance is required because of overdiagnosis, along with our inability to accurately predict individual CaP behavior. Several methods can potentially reduce the risk of CaP progression in patients with active surveillance. However, a measure of uncertainty and fear of progression will always accompany patients with active surveillance and the physicians treating them.