Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2

Diagnostic accuracy of multiparametric magnetic resonance imaging combined with clinical parameters in the detection of clinically significant prostate cancer: A novel diagnostic model

OBJECTIVES

To evaluate the diagnostic accuracy of multiparametric magnetic resonance imaging in the detection of prostate cancer, according to Prostate Imaging Reporting and Data System, and the usefulness of combining clinical parameters to improve patients' risk assessment.

METHODS

Overall, 201 patients underwent multiparametric magnetic resonance imaging investigation with a 3-T magnet and a 32-channel body coil based on triplanar high-resolution T2-weighted, diffusion-weighted and T1-weighted dynamic contrast-enhanced imaging before, during and after intravenous administration of paramagnetic contrast agent. Random transrectal ultrasound-guided biopsy was carried out for all eligible patients. If a Prostate Imaging Reporting and Data System ≥3 lesion was present, a targeted biopsy with magnetic resonance imaging-transrectal ultrasound fusion-guided system was carried out.

RESULTS

Sensitivity, specificity, positive predictive value and negative predictive value of Prostate Imaging Reporting and Data System ≥3 lesions for the detection of prostate cancer were 65.1%, 54.9%, 43.1% and 75.0% respectively, with an accuracy of 64.2% (55.1-72.7%). At uni- and multivariate analysis, age ≥70 years and prostate-specific antigen density ≥0.15 ng/mL/mL were significantly associated with prostate cancer. A new risk model named "modified Prostate Imaging Reporting and Data System" was created considering age and prostate-specific antigen density in addition to the Prostate Imaging Reporting and Data System score showing an improved correlation with prostate cancer compared with the Prostate Imaging Reporting and Data System alone (area under curve 71.4%, 95% confidence interval 62.2-80.5 vs area under curve 62.6%, 95% confidence interval 52.1-73; P ≤ 0.0001).

CONCLUSIONS

The accuracy of Prostate Imaging Reporting and Data System alone in the diagnosis of prostate cancer might be suboptimal, whereas a novel risk model based on the combination of multiparametric magnetic resonance imaging data with clinical parameters could offer higher discrimination and improve the ability of diagnosing clinically significant disease.

Slice profile effects on quantitative analysis of hyperpolarized pyruvate

Magnetic resonance imaging of hyperpolarized pyruvate provides a new imaging biomarker for cancer metabolism, based on the dynamic in vivo conversion of hyperpolarized pyruvate to lactate. Methods for quantification of signal evolution need to be robust and reproducible across a range of experimental conditions. Pharmacokinetic analysis of dynamic spectroscopic imaging data from hyperpolarized pyruvate and its metabolites generally assumes that signal arises from ideal rectangular slice excitation profiles. In this study, we examined whether this assumption could lead to bias in kinetic analysis of hyperpolarized pyruvate and, if so, whether such a bias can be corrected. A Bloch-McConnell simulator was used to generate synthetic data using a known set of "ground truth" pharmacokinetic parameter values. Signal evolution was then analyzed using analysis software that either assumed a uniform slice profile, or incorporated information about the slice profile into the analysis. To correct for slice profile effects, the expected slice profile was subdivided into multiple sub-slices to account for variable excitation angles along the slice dimension. An ensemble of sub-slices was then used to fit the measured signal evolution. A mismatch between slice profiles used for data acquisition and those assumed during kinetic analysis was identified as a source of quantification bias. Results indicate that imperfect slice profiles preferentially increase detected lactate signal, leading to an overestimation of the apparent metabolic exchange rate. The slice profile-correction algorithm was tested in simulation, in phantom measurements, and applied to data acquired from a patient with prostate cancer. The results demonstrated that slice profile-induced biases can be minimized by accounting for the slice profile during pharmacokinetic analysis. This algorithm can be used to correct data from either single or multislice acquisitions.

Additional Value of Dynamic Contrast-enhanced Sequences in Multiparametric Prostate Magnetic Resonance Imaging: Data from the PROMIS Study

BACKGROUND

Multiparametric magnetic resonance imaging (MP-MRI) is established in the diagnosis of prostate cancer, but the need for enhanced sequences has recently been questioned.

OBJECTIVE

To assess whether dynamic contrast-enhanced imaging (DCE) improves accuracy over T2 and diffusion sequences.

DESIGN, SETTING, AND PARTICIPANTS

PROMIS was a multicentre, multireader trial, with, in this part, 497 biopsy-naïve men undergoing standardised 1.5T MP-MRI using T2, diffusion, and DCE, followed by a detailed transperineal prostate mapping (TPM) biopsy at 5 mm intervals. Likert scores of 1-5 for the presence of a significant tumour were assigned in strict sequence, for (1) T2 + diffusion and then (2) T2 + diffusion + dynamic contrast-enhanced images.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

For the primary analysis, the primary PROMIS outcome measure (Gleason score ≥4 + 3 or ≥6 mm maximum cancer length) on TPM was used, and an MRI score of ≥3 was considered positive.

RESULTS AND LIMITATIONS

Sensitivity without and with DCE was 94% and 95%, specificity 37% and 38%, positive predictive value 51% and 51%, and negative predictive value 90% and 91%, respectively (p >  0.05 in each case). The number of patients avoiding biopsy (scoring 1-2) was similar (123/497 vs 121/497, p =  0.8). The number of equivocal scores (3/5) was slightly higher without DCE (32% vs 28% p =  0.031). The proportion of MRI equivocal (3/5) and positive (4-5) cases showing significant tumours were similar (23% and 71% vs 20% and 69%). No cases of dominant Gleason 4 or higher were missed with DCE, compared with a single case with T2 + diffusion-weighted imaging. No attempt was made to correlate lesion location on MRI and histology, which may be considered a limitation. Radiologists were aware of the patient's prostate-specific antigen.

CONCLUSIONS

Contrast adds little when MP-MRI is used to exclude significant prostate cancer.

PATIENT SUMMARY

An intravenous injection of contrast may not be necessary when magnetic resonance imaging is used as a test to rule out significant tumours in the prostate.

The role of radiomics in prostate cancer radiotherapy

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.

Use of Prostate Systematic and Targeted Biopsy on the Basis of Bi-Parametric Magnetic Resonance Imaging in Biopsy-Naïve Patients

OBJECTIVES

To explore the performance of targeted biopsy (TB) in combination with systematic biopsy (SB) in the detection of prostate cancer (PCa) in biopsy naïve patients.

METHODS

From May 2018 to January 2020, 230 biopsy-naïve men with suspicious bi-parametric MRI [bpMRI; Prostate Imaging Reporting and Data System (PI-RADS) score ≥3] were enrolled. All patients had prostate-specific antigen (PSA) levels of 20 ng/ml or less. For each patient, transrectal ultrasound-guided prostate biopsy was performed. The primary endpoint was the detection rate of CSPC [clinically-significant PCa, International Society of Urological Pathology grade group (ISUP GG) 2 or higher tumors]. The secondary endpoints were the detection rates of CIPC (clinically insignificant PCa, ISUP GG 1 tumors).

RESULTS

CSPC was detected in 90 patients. Twelve (13.33%) of them were detected by TB only and 18 (20.00%) by SB only. Detection of CSPC by SB and TB did not differ significantly (p = .36). In 4.35% of 230 patients, CSPC would have been missed if we performed SB only, and in 6.09% of patients if we performed TB only. Moreover, combination of TB and SB did not increase the detection of CIPC.

CONCLUSIONS

No significant difference was found in the detection of CSPC between TB and SB; however, both techniques revealed substantial added value and combination of TB and SB could further improve this detection rate without increasing the detection of CIPC.

Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer

The purpose of this study is to compare diagnostic performance of Prostate Imaging Reporting and Data System (PI-RADS) version (v) 2.1 and 2.0 for detection of Gleason Score (GS) ≥ 7 prostate cancer on MRI. Three experienced radiologists provided PI-RADS v2.0 scores and at least 12 months later v2.1 scores on lesions in 333 prostate MRI examinations acquired between 2012 and 2015. Diagnostic performance was assessed retrospectively by using MRI/transrectal ultrasound fusion biopsy and 10-core systematic biopsy as the reference. From a total of 359 lesions, GS ≥ 7 tumor was present in 135 lesions (37.60%). Area under the ROC curve (AUC) revealed slightly lower values for peripheral zone (PZ) and transition zone (TZ) scoring in v2.1, but these differences did not reach statistical significance. A significant number of score 2 lesions in the TZ were downgraded to score 1 in v2.1 showing 0% GS ≥ 7 tumor (0/11). The newly introduced diffusion-weighted imaging (DWI) upgrading rule in v2.1 was applied in 6 lesions from a total of 143 TZ lesions (4.2%). In summary, PI-RADS v2.1 showed no statistically significant differences in overall diagnostic performance of TZ and PZ scoring compared to v2.0. Downgraded BPH nodules showed favorable cancer frequencies. The new DWI upgrading rule for TZ lesions was applied in only few cases.

Distortion-free 3D diffusion imaging of the prostate using a multishot diffusion-prepared phase-cycled acquisition and dictionary matching

PURPOSE

To achieve three-dimensional (3D) distortion-free apparent diffusion coefficient (ADC) maps for prostate imaging using a multishot diffusion prepared-gradient echo (msDP-GRE) sequence and ADC dictionary matching.

METHODS

The msDP-GRE sequence is combined with a 3D Cartesian, centric k-space trajectory with center oversampling. Oversampled k-space center averaging and phase cycling are used to address motion- and eddy current-induced magnitude corruption. Extended-phase-graph (EPG) simulations and ADC dictionary matching are used to compensate for T₁ effects. To shorten the acquisition time, each volume is undersampled by a factor of two and reconstructed using iterative sensitivity encoding. The proposed approach is characterized using simulations and validated in a kiwifruit phantom, comparing the msDP-GRE ADC maps obtained using both standard monoexponential fitting and dictionary matching with the clinical standard single-shot diffusion weighted-echo planar imaging (ssDW-EPI) ADC. Initial in vivo feasibility is tested in three healthy subjects, and geometric distortion is compared with anatomical T₂ -weighted-turbo spin echo.

RESULTS

In the kiwifruit phantom experiment, the signal magnitude could be recovered using k-space center averaging and phase cycling. No statistically significant difference was observed in the ADC values estimated using msDP-GRE with dictionary matching and clinical standard DW-EPI (P < .05). The in vivo prostate msDP-GRE scans were free of geometric distortion caused by off-resonance susceptibility, and the ADC values in the prostate were in agreement with values found in the published literature.

CONCLUSION

Nondistorted 3D ADC maps of the prostate can be achieved using a msDP sequence and dictionary matching.

Prostate cancer measurements on serial MRI during active surveillance: it's time to be PRECISE

OBJECTIVE

The PRECISE criteria for reporting multiparametric MRI in patients on active surveillance (AS) for prostate cancer (PCa) score the likelihood of clinically significant change over time using a 1-5 scale, where 4 or 5 indicates radiological progression. According to the PRECISE recommendations, the index lesion size can be reported using different definitions of volume (planimetry or ellipsoid formula) or by measuring one or two diameters. We compared different measurements using planimetry as the reference standard and stratified changes according to the PRECISE scores.

METHODS

We retrospectively analysed 196 patients on AS with PCa confirmed by targeted biopsy who had two MR scans (baseline and follow-up). Lesions were measured on T₂ weighted imaging (T₂WI) according to all definitions. A PRECISE score was assessed for each patient.

RESULTS

The ellipsoid formula exhibited the highest correlation with planimetry at baseline (ρ = 0.97) and follow-up (ρ = 0.98) imaging, compared to the biaxial measurement and single maximum diameter. There was a significant difference (p < 0.001) in the yearly percentage volume change between radiological regression/stability (PRECISE 2-3) and progression (PRECISE 4-5) for planimetry (39.64%) and for the ellipsoid formula (46.78%).

CONCLUSION

The ellipsoid formula could be used to monitor tumour growth during AS. Evidence of a significant yearly percentage volume change between radiological regression/stability (PRECISE 2-3) and progression (PRECISE 4-5) has been also observed.

ADVANCES IN KNOWLEDGE

The ellipsoid formula is a reasonable surrogate for planimetry in capturing tumour volume changes on T₂WI in patients on imaging-led AS. This is also associated with radiological changes using the PRECISE recommendations.

VI-RADS for Bladder Cancer: Current Applications and Future Developments

Bladder cancer (BCa) is among the ten most frequent cancers globally. It is the tumor with the highest lifetime treatment-associated costs, and among the tumors with the heaviest impacts on postoperative quality of life. The purpose of this article is to review the current applications and future perspectives of the Vesical Imaging Reporting and Data System (VI-RADS). VI-RADS is a newly developed scoring system aimed at standardization of MRI acquisition, interpretation, and reporting for BCa. An insight will be given on the BCa natural history, current MRI applications for local BCa staging with assessment of muscle invasiveness, and clinical implications of the score for disease management. Future applications include risk stratification of nonmuscle invasive BCa, surveillance, and prediction and monitoring of therapy response. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

The Role of Imaging in Bladder Cancer Diagnosis and Staging

Bladder cancer (BC) is the most common cancer of the urinary tract in the United States. Imaging plays a significant role in the management of patients with BC, including the locoregional staging and evaluation for distant metastatic disease, which cannot be assessed at the time of cystoscopy and biopsy/resection. We aim to review the current role of cross-sectional and molecular imaging modalities for the staging and restaging of BC and the potential advantages and limitations of each imaging modality. CT is the most widely available and frequently utilized imaging modality for BC and demonstrates good performance for the detection of nodal and visceral metastatic disease. MRI offers potential value for the locoregional staging and evaluation of muscular invasion of BC, which is critically important for prognostication and treatment decision-making. FDG-PET/MRI is a novel hybrid imaging modality combining the advantages of both MRI and FDG-PET/CT in a single-setting comprehensive staging examination and may represent the future of BC imaging evaluation.

Yield of concurrent systemic biopsy during MRI-targeted biopsy according to Prostate Imaging Reporting and Data System version 2 in patients with suspected prostate cancer

OBJECTIVES

To investigate the yield of concurrent systemic biopsy (SB) during MRI-targeted biopsy (MRTB) as Prostate Imaging Reporting and Data System (PI-RADS) version 2 (v2) interpretations in patients with suspected prostate cancer (PCa).

METHODS

A total of 285 patients with suspected PCa underwent prebiopsy 3-T MRI, followed by MRI-transrectal ultrasound fusion targeted biopsy and concurrent standard SB for lesions with PI-RADS v2 scores 3-5. Detection rates and positive core rates of PCa and clinically significant cancer (CSC) were evaluated.

RESULTS

In concurrent MRTB and SB, PCa and CSC detection rates were 18.9% and 9.4% for PI-RADS score 3, 45.9% and 32.4% for PI-RADS score 4, and 82.1% and 72.6% for PI-RADS score 5, respectively. Overall detection rate of CSCs (40.0%) for concurrent MRTB and SB was significantly higher than that of MRTB (34.4%, p = 0.004) or SB alone (27.7%, p < 0.001): an increase of 5.6% (16 patients) compared with MRTB alone. For patients with PI-RADS score 4 or 5, the CSC detection rate of concurrent MRTB and SB was 47.0%, an increase of 6.1% when compared with MRTB (40.9%) only (p < 0.001). Of the 110 patients with both MRTB- and SB-positive findings, 22 (20.0%) had the highest Gleason score in SB compared with that in MRTB. In 9.5% (27/285) patients including 12 patients with CSCs, only SB was positive, with negative MRTB.

CONCLUSION

Concurrent SB with MRTB based on PI-RADS v2 can yield a higher CSC detection rate compared with MRTB alone in patients with suspected PCa.

KEY POINTS

• Concurrent SB with MRTB yields an increase of 5.6% CSC detection compared with MRTB alone. • Of both MRTB- and SB-positive findings, 20.0% patients have upgraded Gleason score in SB. • In 18.4% patients, only SB was positive, with negative MRTB. Adding MRTB to SB is helpful for adequate risk stratification, reducing diagnostic uncertainty of PCa.

PI-RADS Versions 2 and 2.1: Interobserver Agreement and Diagnostic Performance in Peripheral and Transition Zone Lesions Among Six Radiologists

BACKGROUND. PI-RADS version 2.1 (v2.1) modifications primarily address transition zone (TZ) interpretation. The revisions also impact peripheral zone (PZ) interpretation, which has received less attention. OBJECTIVE. The purpose of this study was to compare interobserver agreement of PI-RADS version 2 (v2) and v2.1 in the prostate PZ and TZ and perform a pilot comparison of their diagnostic performance in the two zones. METHODS. Six radiologists with varying experience retrospectively assessed 80 prostate lesions (40 PZ, 40 TZ) on MRI in separate sessions for PI-RADS v2 and v2.1. Interobserver agreement was assessed using Conger kappa (κ). For 50 lesions with pathology data, average AUC for detecting clinically significant cancer was compared between versions using multireader multicase statistical methods. Error variance and covariance results informed post hoc power analysis. RESULTS. Interobserver agreement for PI-RADS category 4 or greater was higher for version 2.1 (κ = 0.64) than version 2 (κ = 0.51) in the PZ, but similar for version 2 (κ = 0.64) and version 2.1 (κ = 0.60) in the TZ. The PI-RADS v2.1 DWI descriptor "linear/wedge-shaped" had higher agreement than its predecessor version 2 descriptor "indistinct hypointense" (κ = 0.52 vs κ = 0.18) and yielded 14 more true-negative versus five more false-negative interpretations. The ADC signal descriptor "markedly hypointense," for which only version 2.1 provides a specific definition, had lower agreement in version 2.1 (κ = 0.26) than version 2 (κ = 0.52). Modified TZ T2-weighted category 2 descriptors in version 2.1 had fair agreement (κ = 0.21), and agreement for PI-RADS category 2 in the TZ was lower in version 2.1 (κ = 0.31) than version 2 (κ = 0.57). DWI upgraded a TZ lesion category from 2 to 3 in four patients, detecting two additional cancers. Average AUC was not different between versions 2 and 2.1 for the PZ (AUC, 0.81 vs 0.85; p = .24) or the TZ (AUC, 0.69 vs 0.69; p = .94), though among experienced readers AUC was higher for version 2.1 than version 2 for the PZ (0.91 vs 0.82; p = .001). Overall performance comparison had sufficient power (0.8) to detect a 0.085 difference in AUC. CONCLUSION. Interobserver agreement improved using PI-RADS v2.1 in the PZ but not the TZ. Diagnostic performance improved using version 2.1 only in the PZ for experienced readers. Specific version 2.1 modifications yielded mixed results. CLINICAL IMPACT. The impact of PI-RADS v2.1 in the PZ is notable given the emphasis on version 2.1 TZ modifications. The findings suggest areas in which additional modification could further improve interobserver agreement and performance.

Prospective PI-RADS v2.1 Atypical Benign Prostatic Hyperplasia Nodules With Marked Restricted Diffusion: Detection of Clinically Significant Prostate Cancer on Multiparametric MRI

BACKGROUND. On the basis of expert consensus, PI-RADS version 2.1 (v2.1) introduced the transition zone (TZ) atypical benign prostatic hyperplasia (BPH) nodule, defined as a TZ lesion with an incomplete or absent capsule (T2 score, 2). PI-RADS v2.1 also included a revised scoring pathway whereby such nodules, if exhibiting marked restricted diffusion (DWI score, 4-5), are upgraded from overall PI-RADS category 2 to category 3 (2 + 1 TZ lesions). OBJECTIVE. The purpose of this study was to compare the rates of detection of clinically significant prostate cancer (csPCa) in prospectively reported 2 + 1 TZ lesions, as defined by PI-RADS v2.1, and conventional 3 + 0 TZ lesions with targeted biopsy as the reference standard. METHODS. This retrospective study included men with no known PCa or with treatment-naïve grade group (GG) 1 PCa who underwent 3-T multiparametric MRI of the prostate with prospective reporting by means of PI-RADS v2.1. Patients with at least one PI-RADS category 3 TZ lesion who underwent targeted biopsy formed the final sample. Biopsy results were summarized descriptively for 2 + 1 and 3 + 0 lesions. Generalized estimating equations were used to compare csPCa detection rates between groups. Associations between csPCa in 2 + 1 lesions and patient age, PSA level, prostate volume, PSA density, biopsy history, lesion size, and lesion ADC were tested with Kruskal-Wallis and Fisher exact tests. RESULTS. Among 1238 eligible patients who underwent MRI reported with PI-RADS v2.1, 2 + 1 lesions were reported in 6% (n = 69) and 3 + 0 TZ lesions in 7% (n = 87) of patients. No PCa, GG1 PCa, or csPCa was found in 84% (n = 41), 10% (n = 5), and 6% (n = 3) of 49 patients with 2 + 1 lesions who underwent targeted biopsy. Nor were they found in 74% (n = 45), 15% (n = 9), and 11% (n = 7) of 61 patients with 3 + 0 lesions who underwent targeted biopsy. The csPCa detection rate was not significantly different between 2 + 1 and 3 + 0 lesions (p = .31). All cases of csPCa were GG2, except for one 3 + 0 lesion with a GG3 tumor. No clinical or imaging variable was associated with csPCa in 2 + 1 lesions. CONCLUSION. The rate of csPCa in atypical BPH nodules with marked restricted diffusion was low (6%) and not significantly different from that of conventional 3 + 0 TZ lesions (11%). CLINICAL IMPACT. The results provide prospective clinical data about the revised TZ scoring criterion and pathway in PI-RADS v2.1 for atypical BPH nodules with marked restricted diffusion.

Interpreting body MRI cases: classic findings in pelvic MRI

The high contrast resolution provided by magnetic resonance imaging (MRI) compared to all other modalities allows the interpreting radiologist to make a specific diagnosis for many common and uncommon findings. In some cases, the diagnosis can be so certain that there is no differential diagnosis. In this article, we review the most classically recognized findings when interpreting MRI of the pelvis including the following: Ovaries: Simple cyst, hemorrhagic cyst, corpus luteum, dermoid, fibroma/fibrothecoma. Uterus: C-section scar, adenomyosis, endometriosis, fibroid, congenital uterine anomalies. Cervix: nabothian cyst, cervical cancer. Vagina/Vulva: Gardener's duct cyst, Bartholin's gland cyst. Urethra: skene gland cyst, urethral diverticulum. Prostate: utricle cyst, Mullerian duct cyst, benign prostatic hyperplasia, prostate cancer.

Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation

The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.

Magnetic Resonance Imaging of Prostate Adenocarcinoma: Detection and Staging

Prostate cancer (PCa) is common among men worldwide and is a major cause of morbidity and mortality. The detection of PCa has historically followed a stepwise process of prostate-specific antigen screening followed with systematic transrectal ultrasound-guided biopsy. Magnetic resonance imaging (MRI), utilizing a set of sequences to assess morphology and function, has gained clinical acceptance to detect, characterize, and stage PCa. The Prostate Imaging - Reporting and Data System has helped facilitate the standardization of reporting across institutions and increased adoption of this method. In this review, we will (1) discuss the strengths and weaknesses of conventional diagnostic methods; (2) describe the clinical utility of prostate MRI, specifically addressing its uses in the detection and staging of PCa; and (3) list important technical parameters required for state-of-the-art prostate MRI.

Prostate Cancer: Prostate-specific Membrane Antigen Positron-emission Tomography/Computed Tomography or Positron-emission Tomography/Magnetic Resonance Imaging for Staging

Positron-emission tomography (PET) with prostate-specific membrane antigen (PSMA) has been increasingly used to image prostate cancer in the last decade. In the staging setting several studies have already been published suggesting PSMA PET can be a valuable tool. They, however, did not translate into recommendations by guidelines. Both PSMA PET/computed tomography (CT) and PET/magnetic resonance imaging have been investigated in the staging setting, showing higher detection rate of prostate cancer lesions over the conventional imaging work-up and some studies already showed an impact on disease management. The aim of this review is to provide an overview of the existing published data regarding PSMA PET for staging prostate cancer, with emphasis on PET/magnetic resonance imaging. Despite the fact that PSMA is a relatively new tool and not officially recommended for staging yet, there are >50 original studies in the literature assessing PSMA PET performance in the staging setting of prostate cancer, and some meta-analyses.

Uncommon Prostate Malignant Neoplasms

Prostate adenocarcinoma is the most common prostate cancer; however, there are several other malignant neoplasms that radiologists should be familiar with. The histological classification of malignant prostate neoplasms includes epithelial tumors, mesenchymal tumors, neuroendocrine tumors, hematolymphoid tumors, and stromal tumors. Knowledge of the main clinical and prostate magnetic resonance imaging features of uncommon tumors beyond adenocarcinoma is important for radiologists to enlarge their diagnostic ability and guide referring physician regarding the appropriate patient workup.The aim of this review article is to (1) review the main anatomical and histological concepts of the prostate gland and (2) provide an overview of uncommon prostate malignant neoplasms, emphasizing the key clinical, pathological, and imaging findings that may help radiologists in their daily interpretation of prostate magnetic resonance imaging.

Computer-Aided Diagnosis in Multiparametric MRI of the Prostate: An Open-Access Online Tool for Lesion Classification with High Accuracy

Computer-aided diagnosis (CADx) approaches could help to objectify reporting on prostate mpMRI, but their use in many cases is hampered due to common-built algorithms that are not publicly available. The aim of this study was to develop an open-access CADx algorithm with high accuracy for classification of suspicious lesions in mpMRI of the prostate. This retrospective study was approved by the local ethics commission, with waiver of informed consent. A total of 124 patients with 195 reported lesions were included. All patients received mpMRI of the prostate between 2014 and 2017, and transrectal ultrasound (TRUS)-guided and targeted biopsy within a time period of 30 days. Histopathology of the biopsy cores served as a standard of reference. Acquired imaging parameters included the size of the lesion, signal intensity (T2w images), diffusion restriction, prostate volume, and several dynamic parameters along with the clinical parameters patient age and serum PSA level. Inter-reader agreement of the imaging parameters was assessed by calculating intraclass correlation coefficients. The dataset was stratified into a train set and test set (156 and 39 lesions in 100 and 24 patients, respectively). Using the above parameters, a CADx based on an Extreme Gradient Boosting algorithm was developed on the train set, and tested on the test set. Performance optimization was focused on maximizing the area under the Receiver Operating Characteristic curve (ROC_AUC). The algorithm was made publicly available on the internet. The CADx reached an ROC_AUC of 0.908 during training, and 0.913 during testing (p = 0.93). Additionally, established rule-in and rule-out criteria allowed classifying 35.8% of the malignant and 49.4% of the benign lesions with error rates of <2%. All imaging parameters featured excellent inter-reader agreement. This study presents an open-access CADx for classification of suspicious lesions in mpMRI of the prostate with high accuracy. Applying the provided rule-in and rule-out criteria might facilitate to further stratify the management of patients at risk.

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

Objectives

To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI.

Methods

In 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient.

Results

In the 259 eligible men (median 64 [IQR 61–72] years), PI-RADS had a sensitivity of 98% [106/108]/84% [91/108] with a specificity of 17% [25/151]/58% [88/151], for thresholds at ≥ 3/≥ 4 respectively. U-Net using dynamic threshold adjustment had a sensitivity of 99% [107/108]/83% [90/108] (p > 0.99/> 0.99) with a specificity of 24% [36/151]/55% [83/151] (p > 0.99/> 0.99) for probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and ≥ 4 decisions respectively, not statistically different from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4 examination and U-Net ≥ d3 assessment significantly improved the positive predictive value from 59 to 63% (p = 0.03), on a per-patient basis.

Conclusions

U-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance.

Key Points

• U-Net maintained similar diagnostic performance compared to radiological assessment of PI-RADS ≥ 4 when applied in a simulated clinical deployment.

• Application of our proposed prospective dynamic calibration method successfully adjusted U-Net performance within acceptable limits of the PI-RADS reference over time, while not being limited to PI-RADS as a reference.

• Simultaneous detection by U-Net and radiological assessment significantly improved the positive predictive value on a per-patient and per-lesion basis, while the negative predictive value remained unchanged.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07086-z) contains supplementary material, which is available to authorized users.

Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps

OBJECTIVES

To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential function, ADC_m.

METHODS

One hundred twelve patients with prostate cancer (PCa) underwent 2 prostate MRI examinations on the same day. PCa areas were annotated using whole mount prostatectomy sections. Two U-Net-based convolutional neural networks were trained on three different ADC_m b value settings for (a) slice- and (b) lesion-level detection and (c) segmentation of csPCa. Short-term test-retest repeatability was estimated using intra-class correlation coefficient (ICC(3,1)), proportionate agreement, and dice similarity coefficient (DSC). A 3-fold cross-validation was performed on training set (N = 78 patients) and evaluated for performance and repeatability on testing data (N = 34 patients).

RESULTS

For the three ADC_m b value settings, repeatability of mean ADC_m of csPCa lesions was ICC(3,1) = 0.86-0.98. Two CNNs with U-Net-based architecture demonstrated ICC(3,1) in the range of 0.80-0.83, agreement of 66-72%, and DSC of 0.68-0.72 for slice- and lesion-level detection and segmentation of csPCa. Bland-Altman plots suggest that there is no systematic bias in agreement between inter-scan ground truth segmentation repeatability and segmentation repeatability of the networks.

CONCLUSIONS

For the three ADC_m b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility.

KEY POINTS

• For the three ADC_m b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. • The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility.

Turbo Spin-Echo Diffusion-Weighted Imaging in Prostate Magnetic Resonance Imaging of Men With Pelvic Hardware

We evaluated an alternative diffusion-weighted imaging (DWI) acquisition for prostate magnetic resonance imaging of men with pelvic hardware, using radial k-space sampling (MultiVane [MV]), short-tau inversion-recovery (STIR) fat suppression, and split acquisition of turbo spin-echo signals. The optimized STIR-MV-DWI reduced metal-associated artifacts and image distortion, and aided in visualization of the prostate and lesions. The STIR-MV-DWI can be a valuable adjunct in prostate magnetic resonance imaging of men with pelvic hardware, among whom the conventional echo-planar DWI is compromised.

Rethinking prostate cancer screening: could MRI be an alternative screening test?

In the past decade rigorous debate has taken place about population-based screening for prostate cancer. Although screening by serum PSA levels can reduce prostate cancer-specific mortality, it is unclear whether the benefits outweigh the risks of false-positive results and overdiagnosis of insignificant prostate cancer, and it is not recommended for population-based screening. MRI screening for prostate cancer has the potential to be analogous to mammography for breast cancer or low-dose CT for lung cancer. A number of potential barriers and technical challenges need to be overcome in order to implement such a programme. We discuss different approaches to MRI screening that could address these challenges, including abbreviated MRI protocols, targeted MRI screening, longer rescreening intervals and a multi-modal screening pathway. These approaches need further investigation, and we propose a phased stepwise research framework to ensure proper evaluation of the use of a fast MRI examination as a screening test for prostate cancer.

PI-RADS score v.2 in predicting malignancy in patients undergoing 5α-reductase inhibitor therapy

BACKGROUND

To investigate if the PI-RADS score represents an effective tool in detecting prostate cancer in patients treated with a 5α-reductase inhibitor (dutasteride) and to identify dedicated total serum PSA and PSA density thresholds.

METHODS

Between April 2015 and March 2018, 75 patients under dutasteride treatment underwent multi-parametric magnetic resonance imaging (mpMRI) scans and US/MRI fusion prostate biopsy. Lesions were classified into two groups: Group 1 included PI-RADS 4 and 5 lesions, whilst Group 2 included PI-RADS 3-4-5 lesions. Lesions in groups 1 and 2 were further divided according to the patients' history of previous prostate biopsy.

RESULTS

Ninety-seven lesions were detected. In PI-RADS 4-5 group, mpMRI showed a sensitivity of 78.0% and a specificity of 78.7%. The positive predictive value (PPV) was 79.6% and the negative predictive value (NPV) 77.1%. In PI-RADS 3-4-5 group, sensitivity was 100%, specificity 21.3%, PPV 57.5%, NPV 100%. In PI-RADS 4-5 biopsy-naive group sensitivity was 71.4%, specificity 75.0%. PPV 78.9% and NPV 66.7%. In PI-RADS 4-5 non-biopsy-naive group sensitivity was 82.8%, specificity 80.6%, PPV 80%, and NPV 83.3%. PI-RADS 3-4-5 biopsy-naive group showed sensitivity 100%, specificity 31.3%, PPV 65.6%, NPV 100%. PI-RADS 3-4-5 non-biopsy-naive group showed sensitivity 100%, specificity 16.1%, PPV 52.7%, and NPV 100%. ROC curve analysis indicated a serum total PSA threshold of 6 ng/ml (AUC: 0.71-95% confidence interval: 0.60-0.81) and a PSA density >0.22 (AUC: 0.70-95% confidence interval: 0.6-0.81) as optimal cut-offs for recommending prostate biopsy.

CONCLUSIONS

In our experience the PI-RADS score proved to be sufficiently accurate in predicting prostate cancer in patients under dutasteride therapy.

Multiparametric magnetic resonance imaging and multiparametric magnetic resonance imaging-guided biopsy in the diagnostic pathway of prostate cancer

Multiparametric magnetic resonance imaging (mpMRI) of the prostate and mpMRI-guided biopsy have proved to be a valuable part of the diagnostic pathway for prostate cancer. This review reports on the current results in terms of clinical performance of these diagnostic tools and their role in clinical decision-making.

Detection of Significant Prostate Cancer Using Target Saturation in Transperineal Magnetic Resonance Imaging/Transrectal Ultrasonography-fusion Biopsy

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) and targeted biopsies (TBs) facilitate accurate detection of significant prostate cancer (sPC). However, it remains unclear how many cores should be applied per target.

OBJECTIVE

To assess sPC detection rates of two different target-dependent magnetic resonance imaging (MRI)/transrectal ultrasonography (TRUS)-fusion biopsy approaches (TB and target saturation [TS]) compared with extended systematic biopsies (SBs).

DESIGN, SETTING, AND PARTICIPANTS

Retrospective single-centre outcome of transperineal MRI/TRUS-fusion biopsies of 213 men was evaluated. All men underwent TB with a median of four cores per MRI lesion, followed by a median of 24 SBs, performed by experienced urologists. Cancer and sPC (International Society of Urological Pathology grade group ≥2) detection rates were analysed. TB was compared with SB and TS, with nine cores per target, calculated by the Ginsburg scheme and using individual cores of the lesion and its "penumbra".

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Cancer detection rates were calculated for TS, TB, and SB at both lesion and patient level. Combination of SB + TB served as a reference. Statistical differences in prostate cancer (PC) detection between groups were calculated using McNemar's tests with confidence intervals.

RESULTS AND LIMITATIONS

TS detected 99% of 134 sPC lesions, which was significantly higher than the detection by TB (87%, p = 0.001) and SB (82%, p < 0.001). SB detected significantly more of the 72 low-risk PC lesions than TB (99% vs 68%, p < 0.001) and 10% (p = 0.15) more than that detected by TS. At a per-patient level, 99% of men harbouring sPC were detected by TS. This was significantly higher than that by TB and SB (89%, p = 0.03 and 81%, p = 0.001, respectively). Limitations include limited generalisability, as a transperineal biopsy route was used.

CONCLUSIONS

TS detected significantly more cases of sPC than TB and extended SB. Given that both 99% of sPC lesions and men harbouring sPC were identified by TS, the results suggest that this approach allows to omit SB cores without compromising sPC detection.

PATIENT SUMMARY

Target saturation of magnetic resonance imaging-suspicious prostate lesions provides excellent cancer detection and finds fewer low-risk tumours than the current gold standard combination of targeted and systematic biopsies.

3-T Multiparametric MRI Followed by In-Bore MR-Guided Biopsy for Detecting Clinically Significant Prostate Cancer After Prior Negative Transrectal Ultrasound-Guided Biopsy

OBJECTIVE. The purpose of this study was to evaluate the rate of detection of clinically significant prostate cancer (csPCa), as assessed on the basis of Prostate Imaging Reporting and Data System version 2.1 (PI-RADSv2.1) guidelines, using 3-T in-bore MR-guided biopsy (MRGB) for a cohort of patients suspected of having csPCa despite having a history of recent negative transrectal ultrasound-guided biopsy results. MATERIALS AND METHODS. The cohort in this retrospective, single-center study was derived from a database of 330 patients who underwent multiparametric MRI (mpMRI) followed by in-bore transrectal 3-T MRGB. Seventy-nine patients (mean [± SD] age, 64.1 ± 8.6 years) with prior negative transrectal ultrasound-guided biopsy results and positive pre-MRGB mpMRI results (PI-RADS score ≥ 3) composed the final cohort. The rate of detection of PCa and csPCa (the latter of which was defined by a Gleason score of 3 + 4 or higher) was stratified according to updated PI-RADSv2.1 assessment. RESULTS. MRGB detected PCa in 36 patients (45.6%), 30 (83.3%) of whom had csPCa. The PI-RADSv2.1 score was a strong predictor (odds ratio, 3.97; 95% CI, 1.93-7.47) of csPCa detection. We found two benign transition zone target lesions that were downgraded from PI-RADSv2 category 3 to PI-RADSv2.1 category 2. PCa was detected in 18.4% (7/38), 65.2% (15/23), and 87.5% (14/16) of individuals with PI-RADSv2.1 category 3, 4, and 5 lesions, respectively, with 85.7% (6/7), 86.7% (13/15), and 78.6% (11/14) of these cases found to be csPCa, respectively. Of the seven PI-RADSv2.1 category 3 csPCa lesions, six had prostate-specific antigen density greater than 0.10 ng/mL/cc. CONCLUSION. With the use of 3-T in-bore MRGB, csPCa was detected in 38% of individuals with prior negative transrectal ultrasound-guided biopsy results. PI-RADSv2.1 was a strong predictor of csPCa detection. On the basis of our results, patients with PI-RADSv2.1 category 4 or 5 lesions and patients with PI-RADSv2.1 category 3 lesions and a prostate-specific antigen density greater than or equal to 0.10 ng/mL/cc may benefit from in-bore MRGB.

Current Role of Multiparametric MRI and MRI Targeted Biopsies for Prostate Cancer Diagnosis in Germany: A Nationwide Survey

INTRODUCTION

Multiparametric MRI (mpMRI) and MRI targeted biopsies (MRtb) are a new standard in prostate cancer (PCa) screening and diagnosis. Guidelines already include this approach for patients at risk. We aimed to gather information from German urologists about their knowledge, routine use, and attitude toward mpMRI and consecutive biopsy methods.

MATERIALS AND METHODS

An anonymous online questionnaire was sent via Survey Monkey to the members of the German Society of Urology (DGU). Statistical analyses were performed using SPSS version 25.0.

RESULTS

496 members with a median age of 48.6 years (±11.7) participated in the survey. The majority rated mpMRI of the prostate as a very useful diagnostic tool (72.7%). MRtb of the prostate was considered as very advantageous (71.5%). MpMRI was used by 95.9%, and 83.2% also recommended MRtb predominantly in clinical institutions. For targeted biopsy, MRI-ultrasound fusion biopsy was clearly favored (75.8%). MpMRI was mostly used in patients with previously negative biopsy (90.9%) and in patients under active surveillance (60.9%). Arguments against the use of prostate mpMRI are costs (84.9%) and/or lack of sufficient radiological infrastructure (17.4%).

CONCLUSION

Our data illustrate the meanwhile high acceptance and clinical use of the prostate mpMRI and MRtb in Germany.

Advances in Prostate Magnetic Resonance Imaging

Prostate magnetic resonance (MR) imaging is a widely used imaging technique to detect intraprostatic lesions and guide prostate biopsies, with continuous technical advances for better accuracy in prostate cancer diagnosis. Current evaluation of prostate multiparametric MR imaging mainly depends on qualitative evaluation, which is prone to inter-reader variation. Recent advances in prostate MR imaging, such as quantitative T2 mapping and abbreviated MR imaging protocols (eg, biparametric MR imaging), are designed to simplify prostate MR imaging acquisition and interpretation.

Comparison of Biparametric and Multiparametric MRI for Clinically Significant Prostate Cancer Detection With PI-RADS Version 2.1

BACKGROUND

Biparametric MRI (bpMRI) without dynamic contrast-enhanced MRI (DCE-MRI) results in an elimination of adverse events, shortened examination time, and reduced costs, compared to multiparametric MRI (mpMRI). The ability of bpMRI to detect clinically significant prostate cancer (csPC) with the Prostate Imaging and Reporting Data System version 2.1 (PI-RADS v2.1) compared to standard mpMRI has not been studied extensively.

PURPOSE

To compare the interobserver reliability and diagnostic performance for detecting csPC of bpMRI and mpMRI using PI-RADS v2.1.

STUDY TYPE

Retrospective.

POPULATION

In all, 103 patients with elevated prostate-specific antigen (PSA) levels who underwent mpMRI and subsequent MRI-ultrasonography fusion-guided prostate-targeted biopsy (MRGB) with or without prostatectomy.

FIELD STRENGTH/SEQUENCES

T₂ -weighted imaging (T₂ WI), diffusion-weighted imaging (DWI), and DCE-MRI at 3T.

ASSESSMENT

Three readers independently assessed each suspected PC lesion, assigning a score of 1-5 for T₂ WI, a score of 1-5 for DWI, and positive and negative for DCE-MRI according to PI-RADS v2.1 and determined the overall PI-RADS assessment category of bpMRI (T₂ WI and DWI) and mpMRI (T₂ WI, DWI, and DCE-MRI). The reference standard was MRGB or prostatectomy-derived histopathology.

STATISTICAL TESTING

Statistical analysis was performed using the kappa statistic and McNemar and Delong tests.

RESULTS

Of the 165 suspected PC lesions in 103 patients, 81 were diagnosed with csPC and 84 with benign conditions. Interobserver variability of PI-RADS assessment category showed good agreement for bpMRI (kappa value = 0.642) and mpMRI (kappa value = 0.644). For three readers, the diagnostic sensitivity was significantly higher for mpMRI than for bpMRI (P < 0.001 to P = 0.016, respectively), whereas diagnostic specificity was significantly higher for bpMRI than for mpMRI (P < 0.001 each). For three readers, the area under the receiver operating characteristic curve (AUC) was higher for bpMRI than for mpMRI; however, the difference was significant only for Reader 1 and Reader 3 (Reader 1: 0.823 vs. 0.785, P = 0.035; Reader 2: 0.852 vs. 0.829, P = 0.099; and Reader 3: 0.828 vs. 0.773, P = 0.002).

DATA CONCLUSION

For detecting csPC using PI-RADS v2.1, the interobserver reliability and diagnostic performance of bpMRI was comparable with those of mpMRI.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2.

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

A Comparative Study of Prostate Cancer Detection Rate Between Transperineal COG-TB and Transperineal FUS-TB in Patients with PSA ≤20 ng/mL

Background: The combination of prebiopsy MRI and transperineal targeted biopsies is being increasingly used to obtain tissues from patients with suspected prostate cancer (PCa). Objective: To investigate the difference in PCa detection rate between transperineal cognitive fusion TB (COG-TB) and transperineal software fusion TB (FUS-TB). Participants: The present study included 163 male patients with suspected PCa who had not undergone prostate biopsy, had a prostate-specific antigen (PSA) level of ≤20 ng/mL, and had been examined by bi-parameter MRI and confirmed to have prostate nodules by prostate imaging reporting and data system version 2 (PI-RADS V2) scores ≥3 (from December 3, 2018 to October 7, 2019). Intervention: Seventy-one patients underwent transperineal COG-TB, and 92 patients underwent transperineal FUS-TB. The detection rate of the first four needles was compared. Results: No significant difference was found in the overall detection rate of PCa between COG-TB and FUS-TB (60.56% vs 51.08%, p = 0.228). This result was consistent even after stratifying by PI-RADS score. There was also no significant difference between COG-TB and FUS-TB in the detection rate of clinically significant PCa (p = 0.641). Moreover, COG-TB and FUS-TB showed no difference in the detection rate of PCa with different Gleason scores. Conclusions: In patients with suspected PCa with PSA ≤20 ng/mL and PI-RADS ≥3, FUS-TB was comparable to COG-TB in the detection rate of PCa.

The Negative Predictive Value of a PI-RADS Version 2 Score of 1 on Prostate MRI and the Factors Associated With a False-Negative MRI Study

OBJECTIVE. The purpose of this study was to calculate the negative predictive value of a prostate MRI study with a Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) score of 1 (hereafter referred to as a PI-RADS 1 MRI study) and to explore the patient characteristics and MRI-based factors associated with an MRI study with false-negative results. MATERIALS AND METHODS. A total of 542 consecutive patients with a PI-RADS 1 MRI study obtained between January 2016 and July 2019 were retrospectively identified. Patient charts were examined to identify those patients who subsequently underwent systematic prostate biopsy within 1 year of undergoing MRI or at any later date if the biopsy was negative. Patient characteristics and MRI-specific factors were recorded. Two blinded radiologists evaluated the quality of the axial T2-weighted, DWI, and apparent diffusion coefficient sequences; measured the volume of the bladder, the prostate gland, and rectal gas; and determined whether the peripheral zone was avidly enhancing and whether low signal intensity was seen in 50% or more of the peripheral zone on T2-weighted images. Interobserver agreement was tested. Univariable and multivariable logistic regression models were built. RESULTS. A total of 150 patients (median age, 63 years; interquartile range, 56-70 years) were included. Of these patients, 19 (13%) had prostate cancer with a Gleason score of 3 + 4 or greater, yielding a negative predictive value of 87%. Both low T2 signal intensity in the peripheral zone and the prostate-specific antigen level were associated with a false-negative PI-RADS 1 assessment (odds ratio, 4.9 [95% CI, 1.6-14.9; p = 0.006] and 1.1 [95% CI, 1.0-1.2; p = 0.03], respectively). A cutoff prostate-specific antigen level of 3.97 ng/mL resulted in sensitivity and specificity of 89% and 21%, respectively. There was moderate interobserver agreement for low T2 signal intensity in the peripheral zone (κ coefficient = 0.75). CONCLUSION. Even among select patients who undergo subsequent biopsy because of a high clinical suspicion of prostate cancer, a PI-RADS 1 prostate MRI study has a high negative predictive value. A T2-hypointense peripheral zone and an elevated prostate-specific antigen level are significantly associated with a false-negative MRI study.

Prostate MRI: staging and decision-making

Multi-parametric prostate MRI (mpMRI) plays a critical role in the diagnosis, staging, and evaluation of treatment response in patients with prostate cancer. Radiologists, through an accurate and standardized interpretation of mpMRI, can clinically stage prostate cancer and help to risk stratify patients who may benefit from more invasive treatment or exclude patients who may be harmed by overtreatment. The purpose of this article is to describe key findings to accurately stage prostate cancer with mpMRI and to describe the contexts in which mpMRI is best applied.

Prostate MRI with PI-RADS v2.1: initial detection and active surveillance

The role of prostate MRI in clinical practice has continued to broaden over time. Multiple iterations of PI-RADS reporting have aided in improving detection and reporting of prostate cancer. In addition, recent recommendations from the PI-RADS Steering Committee promote an MRI-first approach with an MRI-directed prostate cancer diagnostic pathway. It is imperative for radiologists to be knowledgeable and familiar with prostate MRI and PI-RADS recommendations, as there is an increasing demand for prostate imaging by clinicians and patients alike.

Prostate magnetic resonance imaging technique

Multiparametric magnetic resonance (MR) imaging of the prostate is an excellent tool to detect clinically significant prostate cancer, and it has widely been incorporated into clinical practice due to its excellent tissue contrast and image resolution. The aims of this article are to describe the prostate MR imaging technique for detection of clinically significant prostate cancer according to PI-RADS v2.1, as well as alternative sequences and basic aspects of patient preparation and MR imaging artifact avoidance.

Improving detection of prostate cancer foci via information fusion of MRI and temporal enhanced ultrasound

PURPOSE

The detection of clinically significant prostate cancer (PCa) is shown to greatly benefit from MRI-ultrasound fusion biopsy, which involves overlaying pre-biopsy MRI volumes (or targets) with real-time ultrasound images. In previous literature, machine learning models trained on either MRI or ultrasound data have been proposed to improve biopsy guidance and PCa detection. However, quantitative fusion of information from MRI and ultrasound has not been explored in depth in a large study. This paper investigates information fusion approaches between MRI and ultrasound to improve targeting of PCa foci in biopsies.

METHODS

We build models of fully convolutional networks (FCN) using data from a newly proposed ultrasound modality, temporal enhanced ultrasound (TeUS), and apparent diffusion coefficient (ADC) from 107 patients with 145 biopsy cores. The architecture of our models is based on U-Net and U-Net with attention gates. Models are built using joint training through intermediate and late fusion of the data. We also build models with data from each modality, separately, to use as baseline. The performance is evaluated based on the area under the curve (AUC) for predicting clinically significant PCa.

RESULTS

Using our proposed deep learning framework and intermediate fusion, integration of TeUS and ADC outperforms the individual modalities for cancer detection. We achieve an AUC of 0.76 for detection of all PCa foci, and 0.89 for PCa with larger foci. Results indicate a shared representation between multiple modalities outperforms the average unimodal predictions.

CONCLUSION

We demonstrate the significant potential of multimodality integration of information from MRI and TeUS to improve PCa detection, which is essential for accurate targeting of cancer foci during biopsy. By using FCNs as the architecture of choice, we are able to predict the presence of clinically significant PCa in entire imaging planes immediately, without the need for region-based analysis. This reduces the overall computational time and enables future intra-operative deployment of this technology.

Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis

OBJECTIVES

The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically significant prostate cancer (csPCa) identification on MRI.

METHODS

Multiple medical databases were systematically searched for studies on ML applications in csPCa identification up to July 31, 2019. Two reviewers screened all papers independently for eligibility. The area under the receiver operating characteristic curves (AUC) was pooled to quantify predictive accuracy. A random-effects model estimated overall effect size while statistical heterogeneity was assessed with the I² value. A funnel plot was used to investigate publication bias. Subgroup analyses were performed based on reference standard (biopsy or radical prostatectomy) and ML type (deep and non-deep).

RESULTS

After the final revision, 12 studies were included in the analysis. Statistical heterogeneity was high both in overall and in subgroup analyses. The overall pooled AUC for ML in csPCa identification was 0.86, with 0.81-0.91 95% confidence intervals (95%CI). The biopsy subgroup (n = 9) had a pooled AUC of 0.85 (95%CI = 0.79-0.91) while the radical prostatectomy one (n = 3) of 0.88 (95%CI = 0.76-0.99). Deep learning ML (n = 4) had a 0.78 AUC (95%CI = 0.69-0.86) while the remaining 8 had AUC = 0.90 (95%CI = 0.85-0.94).

CONCLUSIONS

ML pipelines using prostate MRI to identify csPCa showed good accuracy and should be further investigated, possibly with better standardisation in design and reporting of results.

KEY POINTS

• Overall pooled AUC was 0.86 with 0.81-0.91 95% confidence intervals. • In the reference standard subgroup analysis, algorithm accuracy was similar with pooled AUCs of 0.85 (0.79-0.91 95% confidence intervals) and 0.88 (0.76-0.99 95% confidence intervals) for studies employing biopsies and radical prostatectomy, respectively. • Deep learning pipelines performed worse (AUC = 0.78, 0.69-0.86 95% confidence intervals) than other approaches (AUC = 0.90, 0.85-0.94 95% confidence intervals).

Multiparametric magnetic resonance imaging can exclude prostate cancer progression in patients on active surveillance: a retrospective cohort study

Objectives

To assess the ability of multiparametric MRI (mp-MRI) of the prostate to exclude prostate cancer (PCa) progression during monitoring patients on active surveillance (AS).

Methods

One hundred forty-seven consecutive patients on AS with mp-MRI (T2WI, DWI, DCE-MRI) at 3T were initially enrolled. Fifty-five received follow-up mp-MRI after a minimum interval of 12 months and subsequent targeted MR/US fusion-guided biopsy (FUS-GB) plus concurrent systematic transrectal ultrasound-guided (TRUS-GB) biopsy as reference standard. Primary endpoint was the negative predictive value (NPV) of the follow-up mp-MRI to exclude histopathologic tumor progression using PRECISE recommendations. Secondary endpoints were the positive predictive value (PPV), sensitivity, specificity, Gleason score (GS) upgrades, and comparison of biopsy method.

Results

Of 55 patients, 29 (53%) had a GS upgrade on re-biopsy. All 29 patients showed a tumor progression on follow-up mp-MRI. Fifteen of 55 patients (27%) displayed signs of tumor progression, but had stable GS on re-biopsy. None of the 11 patients (20%) without signs of progression on follow-up mp-MRI had a GS upgrade on re-biopsy. The NPV was 100%, PPV was 66%, sensitivity was 100%, and specificity 42%. FUS-GB resulted in GS upgrade significantly more often (n = 28; 51%) compared with TRUS-GB (n = 12; 22%; p < 0.001).

Conclusions

(Follow-up) Mp-MRI can reliably exclude PCa progression in patients on AS. Standard serial re-biopsies might be waived if follow-up mp-MRIs are stable. Over 60% of patients with signs of tumor progression on mp-MRI during AS had a GS upgrade on re-biopsy. Targeted re-biopsies should be performed if cancer progression or higher-grade PCa is suspected on mp-MRI.

Key Points

• None of the patients with unsuspicious mp-MRI had a GS upgrade in re-biopsy and mp-MRI might replace serial biopsies in these cases

• More than 60% of patients with mp-MRI signs of tumor progression had subsequent Gleason score (GS) upgrades

• Targeted re-biopsies should be performed in case of higher GS cancer suspicion on mp-MRI

Role of multiparametric prostate MRI in the management of prostate cancer

INTRODUCTION

Prostate cancer has traditionally been diagnosed by an elevation in PSA or abnormal exam leading to a systematic transrectal ultrasound (TRUS)-guided biopsy. This diagnostic pathway underdiagnoses clinically significant disease while over diagnosing clinically insignificant disease. In this review, we aim to provide an overview of the recent literature regarding the role of multiparametric MRI (mpMRI) in the management of prostate cancer.

MATERIALS AND METHODS

A thorough literature review was performed using PubMed to identify articles discussing use of mpMRI of the prostate in management of prostate cancer.

CONCLUSION

The incorporation of mpMRI of the prostate addresses the shortcomings of the prostate biopsy while providing several other advantages. mpMRI allows some men to avoid an immediate biopsy and permits visualization of areas likely to harbor clinically significant cancer prior to biopsy to facilitate use of MR-targeted prostate biopsies. This allows for reduction in diagnosis of clinically insignificant disease as well as improved detection and better characterization of higher risk cancers, as well as the improved selection of patients for active surveillance. In addition, mpMRI can be used for selection and monitoring of patients for active surveillance and treatment planning during surgery and focal therapy.

Role of Multiparametric Magnetic Resonance Imaging in Predicting Pathologic Outcomes in Prostate Cancer

Multiparametric magnetic resonance imaging (mpMRI) and the introduction of standardized protocols for its interpretation have had a significant impact on the field of prostate cancer (PC). Multiple randomized controlled trials have shown that the sensitivity for detection of clinically significant PC is increased when mpMRI results are the basis for indication of a prostate biopsy. The added value with regards to sensitivity has been strongest for patients with persistent suspicion for PC after a prior negative biopsy. Although enhanced sensitivity of mpMRI is convincing, studies that have compared mpMRI with prostatectomy specimens prepared by whole-mount section analysis have shown a significant number of lesions that were not detected by mpMRI. In this context, the importance of an additional systematic biopsy (SB) is still being debated. While SB in combination with targeted biopsies leads to an increased detection rate, most of the tumors detected by SB only are considered clinically insignificant. Currently, multiple risk calculation tools are being developed that include not only clinical parameters but mpMRI results in addition to clinical parameters in order to improve risk stratification for PC, such as the Partin tables. In summary, mpMRI of the prostate has become a standard procedure recommended by multiple important guidelines for the diagnostic work-up of patients with suspicion of PC.

Clinical Applications of Molecular Biomarkers in Prostate Cancer

There is clinically relevant molecular heterogeneity in prostate cancer (PCa), but this biological diversity has had only a minimal impact on clinical practice. Treatment outcomes in patients with localised PCa are often highly variable, even among patients stratified to the same risk group or disease state based on standard clinical and pathological parameters. In recent years, the development of gene panels has provided valuable data on the differential expression of genes in patients with PCa. Nevertheless, there is an urgent need to identify and validate prognostic and predictive biomarkers that can be applied across clinical scenarios, ranging from localised disease to metastatic castration-resistant PCa. The availability of such tools would allow for precision medicine to finally reach PCa patients. In this review, we evaluate current data on molecular biomarkers for PCa, with an emphasis on the biomarkers and gene panels with the most robust evidence to support their application in routine clinical practice.