Prostate cancer localization using multiparametric MR imaging: comparison of Prostate Imaging Reporting and Data System (PI-RADS) and Likert scales

Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study

OBJECTIVES

To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives.

METHODS

One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions.

RESULTS

csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9-77.6/54.5-70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5-84.5/59.4-77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only.

CONCLUSION

Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

Interaction of patient age and high-grade prostate cancer on targeted biopsies of MRI suspicious lesions

OBJECTIVES

To evaluate the interaction of patient age and Prostate Imaging-Reporting and Data System (PI-RADS) score in determining the grade of prostate cancer (PCa) identified on magnetic resonance imaging (MRI)-targeted biopsy in older men.

PATIENTS AND METHODS

From a prospectively accrued Institutional Review Board-approved comparative study of MRI-targeted and systematic biopsy between June 2012 and December 2022, men with at least one PI-RADS ≥3 lesion on pre-biopsy MRI and no prior history of PCa were selected. Ordinal and binomial logistic regression analyses were performed.

RESULTS

A total of 2677 men met study criteria. The highest PI-RADS score was 3 in 1220 men (46%), 4 in 950 men (36%), and 5 in 507 men (19%). The median (interquartile range [IQR]) patient age was 66.7 (60.8-71.8) years, median (IQR) prostate-specific antigen (PSA) level was 6.1 (4.6-9.0) ng/mL, median (IQR) prostate volume was 48 (34-68) mL, and median (IQR) PSA density was 0.13 (0.08-0.20) ng/mL/mL. Clinically significant (cs)PCa and high-risk PCa were identified on targeted biopsy in 1264 (47%) and 321 (12%) men, respectively. Prevalence of csPCa and high-risk PCa were significantly higher in the older age groups. On multivariable analyses, patient age was significantly associated with csPCa but not high-risk PCa; PI-RADS score and the interaction of age and PI-RADS score were significantly associated with high-risk PCa but not csPCa.

CONCLUSION

In our cohort, the substantial rate of high-risk PCa on MRI-ultrasound fusion targeted biopsies in older men, and its significant association with MRI findings, supports the value of pre-biopsy MRI to localise disease that could cause cancer mortality even in older men.

Combining variational mode decomposition with regularisation techniques to denoise MRI data

In this paper, we propose a novel method for removing noise from MRI data by exploiting regularisation techniques combined with variational mode decomposition. Variational mode decomposition is a new decomposition technique for sparse decomposition of a 1D or 2D signal into a set of modes. In turn, regularisation is a method that can translate the ill-posed problem (e.g., image denoising) into a well-posed problem. The proposed method aims to remove the noise from the image in two steps. In the first step, the MR imaging data are decomposed by the 2D variational mode decomposition algorithm. In the second step, for effective suppression of Rician noise from MRI data, we used the fused lasso signal approximator with all modes acquired from the medical scan. The performance of the proposed approach was compared with state-of-the-art reference methods based on different metrics, that is, the peak signal-to-noise ratio, the structural similarity index metrics, the high-frequency error norm, the quality index based on local variance, and the sharpness index. The experiments were performed on the basis of both simulated and real images. The presented results prove the high denoising performance of the proposed algorithm; particularly under heavy noise conditions.

Combiner and HyperCombiner networks: Rules to combine multimodality MR images for prostate cancer localisation

One of the distinct characteristics of radiologists reading multiparametric prostate MR scans, using reporting systems like PI-RADS v2.1, is to score individual types of MR modalities, including T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels. First, we demonstrate that either a linear mixture model or a nonlinear stacking model is sufficient to model PI-RADS decision rules for localising prostate cancer. Second, parameters of these combining models are proposed as hyperparameters, weighing independent representations of individual image modalities in the Combiner network training, as opposed to end-to-end modality ensemble. A HyperCombiner network is developed to train a single image segmentation network that can be conditioned on these hyperparameters during inference for much-improved efficiency. Experimental results based on 751 cases from 651 patients compare the proposed rule-modelling approaches with other commonly-adopted end-to-end networks, in this downstream application of automating radiologist labelling on multiparametric MR. By acquiring and interpreting the modality combining rules, specifically the linear-weights or odds ratios associated with individual image modalities, three clinical applications are quantitatively presented and contextualised in the prostate cancer segmentation application, including modality availability assessment, importance quantification and rule discovery.

Evaluation of an Objective MRI-Based Tumor Regression Grade (mrTRG) Score and a Subjective Likert Score for Assessing Treatment Response in Locally Advanced Rectal Cancers-A Retrospective Study

Purpose:  Magnetic resonance imaging (MRI) with the help of MRI-based tumor regression grade (mrTRG) score has been used as a tool to predict pathological tumor regression grade (pTRG) in patients of rectal cancer post-neoadjuvant chemoradiation. Our study aims to evaluate the ability of MRI in assessing treatment response comparing an objective mrTRG score and a subjective Likert score, with a focus on the ability to predict pathologic complete response (pCR). Methods:  Post-treatment MRI studies were retrospectively reviewed for 170 consecutive cases of histopathologically proven rectal cancer after receiving neoadjuvant chemoradiation and prior to surgery by two oncoradiologists blinded to the eventual postoperative histopathology findings. An objective (mrTRG) and a subjective Likert score were assigned to all the cases. Receiver operating characteristic curves were constructed to determine the ability of Likert scale and mrTRG to predict pCR, with postoperative histopathology being the gold standard. The optimal cutoff points on the scale of 1 to 5 were obtained for mrTRG and Likert scale with the greatest sum of sensitivity and specificity using the Youden Index. Results:  The most accurate cutoff point for the mrTRG to predict complete response was 2.5 (using Youden index), with a sensitivity of 69.2%, specificity of 69.6%, positive predictive value (PPV) of 85.6%, negative predictive value (NPV) of 46.4%, and accuracy of 69.3%. The most accurate cutoff for the Likert scale to predict complete response was 3.5, with a sensitivity of 47.5%, specificity of 89.1%, PPV of 91.9%, NPV of 39.4%, and accuracy of 59%. mrTRG had a lower cutoff and was more accurate in predicting pCR compared to Likert score. Conclusion:  An objective mrTRG was more accurate than a subjective Likert scale to predict complete response in our study.

Texture analysis of multiparametric magnetic resonance imaging for differentiating clinically significant prostate cancer in the peripheral zone

BACKGROUND

Texture analysis (TA) provides additional tissue heterogeneity data that may assist in differentiating peripheral zone(PZ) lesions in multiparametric magnetic resonance imaging (mpMRI). This study investigates the role of magnetic resonance imaging texture analysis (MRTA) in detecting clinically significant prostate cancer (csPCa) in the PZ.

METHODS

This retrospective study included 80 consecutive patients who had an mpMRI and a prostate biopsy for suspected prostate cancer. Two radiologists in consensus interpreted mpMRI and performed texture analysis based on their histopathology. The first-, second-, and higher-order texture parameters were extracted from mpMRI and were compared between groups. Univariate and multivariate logistic regression analyses were performed using the texture parameters to determine the independent predictors of csPCa. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance of the texture parameters.

RESULTS

: In the periferal zone, 39 men had csPCa, while 41 had benign lesions or clinically insignificant prostate cancer (cisPCa). Themajority of texture parameters showed statistically significant differences between the groups. Univariate ROC analysis showed that the ADC mean and ADC median were the best variables in differentiating csPCa (p < 0.001). The first-order logistic regression model (mean + entropy) based on the ADC maps had a higher AUC value (0.996; 95% CI: 0.989-1) than other texture-based logistic regression models (p < 0.001).

DISCUSSION

MRTA is useful in differentiating csPCa from other lesions in the PZ. Consequently, the first-order multivariate regressionmodel based on ADC maps had the highest diagnostic performance in differentiating csPCa.

INTEGRATING PROSTATE SPECIFIC ANTIGEN DENSITY BIOMARKER INTO DEEP LEARNING PROSTATE MRI LESION SEGMENTATION MODELS

Prostate cancer lesion segmentation in multi-parametric magnetic resonance imaging (mpMRI) is crucial for pre-biopsy diagnosis and targeted biopsy guidance. Deep convolution neural networks have been widely utilized for lesion segmentation. However, these methods fail to achieve a high Dice coefficient because of the large variations in lesion size and location within the gland. To address this problem, we integrate the clinically-meaningful prostate specific antigen density (PSAD) biomarker into the deep learning model using feature-wise transformations to condition the features in latent space, and thus control the size of lesion prediction. We tested our models on a public dataset with 214 annotated mpMRI scans and compared the segmentation performance to a baseline 3D U-Net model. Results demonstrate that integrating the PSAD biomarker significantly improves segmentation performance in both Dice coefficient and centroid distance metric.

Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer

OBJECTIVES

To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes.

MATERIALS AND METHODS

Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson's coefficients and survival analysis using Kaplan-Meier estimators were performed.

RESULTS

The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively.

CONCLUSION

This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients.

Cross-Modality Image Registration Using a Training-Time Privileged Third Modality

In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusion-weighted scans with high b-value (DWI [Formula: see text]). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI [Formula: see text]) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWI [Formula: see text], to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI [Formula: see text] and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI [Formula: see text] and T2w in this challenging application.

Prospective testing of clinical Cerenkov luminescence imaging against standard-of-care nuclear imaging for tumour location

In oncology, the feasibility of Cerenkov luminescence imaging (CLI) has been assessed by imaging superficial lymph nodes in a few patients undergoing diagnostic ¹⁸F-fluoro-2-deoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT). However, the weak luminescence signal requires the removal of ambient light. Here we report the development of a clinical CLI fiberscope with a lightproof enclosure, and the clinical testing of the setup using five different radiotracers. In an observational prospective trial (ClinicalTrials.gov identifier NCT03484884 ) involving 96 patients with existing or suspected tumours, scheduled for routine clinical FDG PET or ¹³¹I therapy, the level of agreement of CLI with standard-of-care imaging (PET or planar single-photon emission CT) for tumour location was 'acceptable' or higher (≥3 in the 1-5 Likert scale) for 90% of the patients. CLI correlated with the concentration of radioactive activity, and captured therapeutically relevant information from patients undergoing targeted radiotherapy or receiving the alpha emitter ²²³Ra, which cannot be feasibly imaged clinically. CLI could supplement radiological scans, especially when scanner capacity is limited.

Performance of multi-parametric magnetic resonance imaging through PIRADS scoring system in biopsy naïve patients with suspicious prostate cancer

Background

Use of multi-parametric magnetic resonance imaging (mp-MRI) and Prostate Imaging Reporting and Data System (PI-RADS) scoring system allowed more precise detection of prostate cancer (PCa). Our study aimed at evaluating the diagnostic performance of mp-MRI in detection of PCa.

Methods

Eighty-six patients suspected to have prostate cancer were enrolled. All patients underwent mp-MRI followed by systematic and targeted trans-rectal ultrasound (TRUS) guided prostate biopsies. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of mp-MRI were evaluated.

Results

Forty-six patients (53.5%) had prostate cancer on targeted and systematic TRUS biopsies. On mp-MRI, 96.6% of lesions with PI-RADS < 3 revealed to be benign by TRUS biopsy, 73.3% of lesions with PI-RADS 4 showed ISUP grades ≥1, whereas all PI-RADS 5 lesions showed high ISUP grades ≥ 3. For PI-RADS 3 lesions, 62.5% of them revealed to be benign and 37.5% showed ISUP grades ≥1 by TRUS biopsy. PI-RADS scores ˃3 had 69.57% sensitivity and 85% specificity for detection of PCa. On adding the equivocal PI-RADS 3 lesions, PI-RADS scores ≥3 had higher sensitivity (97.83%), but at the cost of lower specificity (32.5%).

Conclusion

Mp-MRI using PI-RADS V2 scoring system categories ≤3 and >3 could help in detection of PCa. PI-RADS 3 lesions are equivocal. Including PI-RADS lesions ≥3 demonstrated higher sensitivity, but at the cost of lower specificity for mp-MRI in diagnosis for Pca.

Abbreviations

CDR: cancer detection rates; DRE: digital rectal examination; ISUP: international society of urological pathology; mp-MRI: multi-parametric magnetic resonance imaging; NPV: negative predictive value; PCa: prosatate cancer; PI-RADS: Prostate Imaging Reporting and Data System; PPV: Positive predictive value; PSA: prostate specific antigen; TRUS: transrectal ultrasound.

PI-RADS and Likert scales for structured reporting in multiparametric MR imaging of the prostate

Multiparametric MRI (mpMRI) plays a critical role in the detection, staging and risk stratification of prostate cancer (PCa). There are two widely accepted structured reporting systems used for interpretation of mpMRI of the prostate - PI-RADS v2.1 and Likert. Both these systems demonstrate good diagnostic performance with high cancer detection rates however have key conceptual differences. In this commentary, the authors highlight the individual strengths and areas of potential improvement as well as emphasize the need for continued clinical validation for these interpreting and reporting systems.

Comparison of prostate imaging reporting and data system v2.1 and 2 in transition and peripheral zones: evaluation of interreader agreement and diagnostic performance in detecting clinically significant prostate cancer

OBJECTIVE

To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v. 2.1, in comparison with v. 2.

METHODS

Institutional review board approval was obtained for this retrospective study. 77 consecutive patients who underwent a prostate multiparametric magnetic resonance imaging at 3.0 T before radical prostatectomy were included. Four radiologists (two experienced uroradiologists and two inexperienced radiologists) independently scored eight regions [six peripheral zones (PZ) and two transition zones (TZ)] using v. 2.1 and v. 2. Interreader agreement was assessed using κ statistics. To evaluate diagnostic performance for clinically significant prostate cancer (csPC), area under the curve (AUC) was estimated.

RESULTS

228 regions were pathologically diagnosed as positive for csPC. With a cut-off ≥3, the agreement among all readers was better with v. 2.1 than v. 2 in TZ, PZ, or both zones combined (κ-value: TZ, 0.509 vs 0.414; PZ, 0.686 vs 0.568; both zones combined, 0.644 vs 0.531). With a cut-off ≥4, the agreement among all readers was also better with v. 2.1 than v. 2 in the PZ or both zones combined (κ-value: PZ, 0.761 vs 0.701; both zones combined, 0.756 vs 0.709). For all readers, AUC with v. 2.1 was higher than with v. 2 (TZ, 0.826-0.907 vs 0.788-0.856; PZ, 0.857-0.919 vs 0.853-0.902).

CONCLUSION

Our study suggests that the PI-RADS v. 2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v. 2.

ADVANCES IN KNOWLEDGE

PI-RADS v. 2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.

Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use

Purpose: An accurate zonal segmentation of the prostate is required for prostate cancer (PCa) management with MRI. Approach: The aim of this work is to present UFNet, a deep learning-based method for automatic zonal segmentation of the prostate from T2-weighted (T2w) MRI. It takes into account the image anisotropy, includes both spatial and channelwise attention mechanisms and uses loss functions to enforce prostate partition. The method was applied on a private multicentric three-dimensional T2w MRI dataset and on the public two-dimensional T2w MRI dataset ProstateX. To assess the model performance, the structures segmented by the algorithm on the private dataset were compared with those obtained by seven radiologists of various experience levels. Results: On the private dataset, we obtained a Dice score (DSC) of 93.90 ± 2.85 for the whole gland (WG), 91.00 ± 4.34 for the transition zone (TZ), and 79.08 ± 7.08 for the peripheral zone (PZ). Results were significantly better than other compared networks' ( p - value < 0.05 ). On ProstateX, we obtained a DSC of 90.90 ± 2.94 for WG, 86.84 ± 4.33 for TZ, and 78.40 ± 7.31 for PZ. These results are similar to state-of-the art results and, on the private dataset, are coherent with those obtained by radiologists. Zonal locations and sectorial positions of lesions annotated by radiologists were also preserved. Conclusions: Deep learning-based methods can provide an accurate zonal segmentation of the prostate leading to a consistent zonal location and sectorial position of lesions, and therefore can be used as a helping tool for PCa diagnosis.

Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data

Multi-parametric magnetic resonance imaging (mpMRI) has been playing an increasingly important role in the detection of prostate cancer (PCa). Various computer-aided detection algorithms were proposed for automated PCa detection by combining information in multiple mpMRI parameters. However, there are specific features of mpMRI, including between-voxel correlation within each prostate and heterogeneity across patients, that have not been fully explored but could potentially improve PCa detection if leveraged appropriately. This article proposes novel Bayesian approaches for voxel-wise PCa classification that accounts for spatial correlation and between-patient heterogeneity in the mpMRI data. Modeling the spatial correlation is challenging due to the extreme high dimensionality of the data, and we propose three scalable approaches based on Nearest Neighbor Gaussian Process (NNGP), reduced-rank approximation, and a conditional autoregressive (CAR) model that approximates a Gaussian Process with the Matérn covariance, respectively. Our simulation study shows that properly modeling the spatial correlation and between-patient heterogeneity can substantially improve PCa classification. Application to in vivo data illustrates that classification is improved by all three spatial modeling approaches considered, while modeling the between-patient heterogeneity does not further improve our classifiers. Among the proposed models, the NNGP-based model is recommended given its high classification accuracy and computational efficiency.

Evaluating the performance of clinical and radiological data in predicting prostate cancer in prostate imaging reporting and data system version 2.1 category 3 lesions of the peripheral and the transition zones

PURPOSE

To define the value of clinical and radiological data, using multiparametric magnetic resonance imaging (mpMRI), to predict prostate cancer (PCa) in prostate imaging reporting and data system version 2.1 (PIRADSv2.1) 3 lesions of the peripheral and the transition zones (PZ and TZ).

METHODS

The mpMRI of patients with PIRADSv2.1 3 lesions who had undergone fusion targeted biopsy was reviewed. Morphological pattern, diffusion parameters and vascularisation were evaluated. The radiological/histopathological data of benign and malignant lesions, between the PZ and TZ were compared. Univariate and multivariate analyses were carried out to identify the clinical and radiological data capable of predicting PCa.

RESULTS

One hundred and twenty-three lesions were assessed, 93 (76%) in the PZ and 30 (24%) in the TZ. Of these, 56 (46%) were PCa and 67 (54%) were benign. The majority of the PCas were Grade Group System (GGS) 1 (38%) and GGS 2 (39%); tumours having a GGS ≥ 3 were more frequently in the TZ (p = 0.02). Univariate analysis showed a significant correlation between PCa and prostate volume, prostate-specific antigen (PSA) density, lesion zone and the apparent diffusion coefficient. At multivariate logistic regression PSA density > 0.15 ng/ml/ml {Odds ratio [OR] 2.38; p = 0.001} and lesion zone (i.e. TZ OR 7.55) were independent predictors of PCa (all p ≤ 0.04).

CONCLUSION

In solitary PIRADSv2.1 3 lesions, the most important predictive factor was the location zone, with a much greater risk for TZ lesions.

Diagnostic accuracy of bi-parametric magnetic resonance imaging in suspected prostate cancer: Correlation of scan results with biopsy findings in a series of 266 patients

Objectives:

This study aimed to determine the diagnostic accuracy of bi-parametric magnetic resonance imaging (bpMRI) for clinically significant (CS) prostate cancer (PCa), and to assess the suitability of a new diagnostic pathway using bpMRI and prostate-specific antigen density (PSAd) to determine the need for biopsy.

Methods:

A total of 386 patients referred to one UK cancer centre with suspected PCa across 12 months from 2017 to 2018 underwent bpMRI, with a Prostate Imaging Reporting and Data System (PIRADS) score assigned. Of these, 266 (69%) were biopsied, with 150 CS-PCa (a Gleason score of 7 or a Gleason score of 3 with core length ⩾5 mm) detected: a 57% diagnostic yield. Imaging, PSAd and biopsy results were collated, and a confusion matrix was calculated.

Results:

Twenty-three men with PIRADS 1 were biopsied, with two CS-PCa detected: PSAd M=0.19 ( SD=0.07). Twenty-one men with PIRADS 2 lesions were biopsied, with one CS-PCa detected: PSAd was 0.28. Seventy-five men with PIRADS 3 were biopsied, with 25 CS-PCa detected: PSAd M=0.26 ( SD=0.16). Fifty-seven men with PIRADS 4 were biopsied, with 46 CS-PCa detected: PSAd M=0.26 ( SD=0.16). Ninety men with PIRADS 5 were biopsied, with 83 CS-PCa detected: PSAd M=0.55 ( SD=0.63). Among the 266 biopsied patients, a pathway offering biopsy if PIRADS is ⩾3 or PSAd ⩾0.1 spares 11 (4.2%) biopsies compared to baseline practice, with a sensitivity of 100% and a specificity of 10.1%, for biopsy-detected CS-PCa. The diagnostic yield is 61.8%.

Conclusion:

BpMRI is comparable to multi-parametric MRI for assessing need for biopsy in suspected PCa, albeit with lower specificity. A diagnostic pathway using bpMRI and PSAd can be safely used to avoid biopsy in men at low risk, increasing diagnostic yield of biopsy while reducing overdiagnosis and avoiding the risks and costs associated with gadolinium contrast.

Level of evidence:

Level 4.

A novel read methodology to evaluate the optimal dose of 68Ga-satoreotide trizoxetan as a PET imaging agent in patients with gastroenteropancreatic neuroendocrine tumours: a phase II clinical trial

BACKGROUND

⁶⁸Ga-satoreotide trizoxetan is a novel somatostatin receptor antagonist exhibiting higher tumour-to-background ratios and sensitivity compared to ⁶⁸Ga-DOTATOC. This randomised, 2 × 3 factorial, phase II study aimed to confirm the optimal peptide mass and radioactivity ranges for ⁶⁸Ga-satoreotide trizoxetan, using binary visual reading. To that end, 24 patients with metastatic gastroenteropancreatic neuroendocrine tumours received 5-20 µg of ⁶⁸Ga-satoreotide trizoxetan on day 1 of the study and 30-45 µg on day 16-22, with one of three gallium-68  radioactivity ranges (40-80, 100-140, or 160-200 MBq) per visit. Two ⁶⁸Ga-satoreotide trizoxetan PET/CT scans were acquired from each patient post-injection, and were scored by experienced independent blinded readers using a binary system (0 for non-optimal image quality and 1 for optimal image quality). For each patient pair of ⁶⁸Ga-satoreotide trizoxetan scans, one or both images could score 1.

RESULTS

Total image quality score for ⁶⁸Ga-satoreotide trizoxetan PET scans was lower in the 40-80 MBq radioactivity range (56.3%) compared to 100-140 MBq (90.6%) and 160-200 MBq (81.3%). Both qualitative and semi-quantitative analysis showed that peptide mass (5-20 or 30-45 µg) did not influence ⁶⁸Ga-satoreotide trizoxetan imaging. There was only one reading where readers diverged on scoring; one reader preferred one image because of higher lesion conspicuity, and the other reader preferred the alternative image because of the ability to identify more lesions.

CONCLUSIONS

Binary visual reading, which was associated with a low inter-reader variability, has further supported that the optimal administered radioactivity of ⁶⁸Ga-satoreotide trizoxetan was 100-200 MBq with a peptide mass up to 50 µg. Trial registration ClinicalTrials.gov, NCT03220217. Registered 18 July 2017, https://clinicaltrials.gov/ct2/show/NCT03220217.

Inter-reader agreement of the PI-QUAL score for prostate MRI quality in the NeuroSAFE PROOF trial

Objectives

The Prostate Imaging Quality (PI-QUAL) score assesses the quality of multiparametric MRI (mpMRI). A score of 1 means all sequences are below the minimum standard of diagnostic quality, 3 implies that the scan is of sufficient diagnostic quality, and 5 means that all three sequences are of optimal diagnostic quality. We investigated the inter-reader reproducibility of the PI-QUAL score in patients enrolled in the NeuroSAFE PROOF trial.

Methods

We analysed the scans of 103 patients on different MR systems and vendors from 12 different hospitals. Two dedicated radiologists highly experienced in prostate mpMRI independently assessed the PI-QUAL score for each scan. Interobserver agreement was assessed using Cohen’s kappa with standard quadratic weighting (κw) and percent agreement.

Results

The agreement for each single PI-QUAL score was strong (κw = 0.85 and percent agreement = 84%). A similar agreement (κw = 0.82 and percent agreement = 84%) was observed when the scans were clustered into three groups (PI-QUAL 1–2 vs PI-QUAL 3 vs PI-QUAL 4–5). The agreement in terms of diagnostic quality for each single sequence was highest for T2-weighted imaging (92/103 scans; 89%), followed by dynamic contrast-enhanced sequences (91/103; 88%) and diffusion-weighted imaging (80/103; 78%).

Conclusion

We observed strong reproducibility in the assessment of PI-QUAL between two radiologists with high expertise in prostate mpMRI. At present, PI-QUAL offers clinicians the only available tool for evaluating and reporting the quality of prostate mpMRI in a systematic manner but further refinements of this scoring system are warranted.

Key Points

• Inter-reader agreement for each single Prostate Imaging Quality (PI-QUAL) score (i.e., PI-QUAL 1 to PI-QUAL 5) was strong, with weighted kappa = 0.85 (95% confidence intervals: 0.51 – 1) and percent agreement = 84%.

• Interobserver agreement was strong when the scans were clustered into three groups according to the ability (or not) to rule in and to rule out clinically significant prostate cancer (i.e., PI-QUAL 1-2 vs PI-QUAL 3 vs PI-QUAL 4–5), with weighted kappa = 0.82 (95% confidence intervals: 0.68 – 0.96) and percent agreement = 84%.

• T2-weighted acquisitions were the most compliant with the Prostate Imaging Reporting and Data System (PI-RADS) v. 2.0 technical recommendations and were the sequences of highest diagnostic quality for both readers in 95/103 (92%) scans, followed by dynamic contrast enhanced acquisition with 81/103 (79%) scans and lastly by diffusion-weighted imaging with 79/103 (77%) scans.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08169-1.

Impact of Chronic Prostatitis on the PI-RADS Score 3: Proposal for the Addition of a Novel Binary Suffix

We examined the impact of chronic prostatitis on the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI). In this retrospective study, 63 men underwent 3T mpMRI followed by MRI/ultrasound fusion biopsy to exclude/confirm clinically significant prostate cancer (csPCa). A total of 93 lesions were included for evaluation. Images were assessed by two radiologists. Prostatitis was graded visually on T2-weighted and contrast-enhanced sequences. The correlation of prostatitis features with the assigned Prostate Imaging Reporting and Data System (PI-RADS) and the presence of csPCa were assessed, and the clinical and functional imaging parameters for differentiating between prostatitis and significant tumors were examined. Histopathological analysis was used as the reference standard. The rate of PI-RADS 3 scores tended to be higher in the presence of radiologically severe prostatitis compared with no/discrete prostatitis (n = 52 vs. n = 9; p = 0.225). In severe prostatitis, csPCa was determined in only 7.7% (4/52) of PI-RADS 3 lesions. In severe chronic prostatitis, a binary prostatitis suffix (e.g., PI-RADS 3 i+ versus i−) within the radiological report may help assess the limitations of mpMRI interpretability because of severe prostatitis and avoid unnecessary biopsies. Mean apparent diffusion coefficient (ADC_mean) was the best marker (cutoff 0.93 × 10⁻³ mm²/s) to differentiate between csPCa/non csPCa in severe prostatitis.

Solitary bone tumor imaging reporting and data system (BTI-RADS): initial assessment of a systematic imaging evaluation and comprehensive reporting method

OBJECTIVES

Identify the most pertinent imaging features for solitary bone tumor characterization using a multimodality approach and propose a systematic evaluation system.

METHODS

Data from a prospective trial, including 230 participants with histologically confirmed bone tumors, typical "do not touch" lesions, and stable chondral lesions, were retrospectively evaluated. Clinical data, CT, and MR imaging features were analyzed by a musculoskeletal radiologist blinded to the diagnosis using a structured report. The benign-malignant distribution of lesions bearing each image feature evaluated was compared to the benign-malignant distribution in the study sample. Benign and malignant indicators were identified. Two additional readers with different expertise levels independently evaluated the study sample.

RESULTS

The sample included 140 men and 90 women (mean age 40.7 ± 18.3 years). The global benign-malignant distribution was 67-33%. Seven imaging features reached the criteria for benign indicators with a mean frequency of benignancy of 94%. Six minor malignant indicators were identified with a mean frequency of malignancy of 60.5%. Finally, three major malignant indicators were identified (Lodwick-Madewell grade III, aggressive periosteal reaction, and suspected metastatic disease) with a mean frequency of malignancy of 82.4%. A bone tumor imaging reporting and data system (BTI-RADS) was proposed. The reproducibility of the BTI-RADS was considered fair (kappa = 0.67) with a mean frequency of malignancy in classes I, II, III, and IV of 0%, 2.2%, 20.1%, and 71%, respectively.

CONCLUSION

BTI-RADS is an evidence-based systematic approach to solitary bone tumor characterization with a fair reproducibility, allowing lesion stratification in classes of increasing malignancy frequency.

TRIAL REGISTRATION

Clinical trial number NCT02895633 .

KEY POINTS

• The most pertinent CT and MRI criteria allowing bone tumor characterization were defined and presented. • Lodwick-Madewell grade III, aggressive periosteal reaction, and suspected metastatic disease should be considered major malignant indicators associated with a frequency of malignancy over 75%. • The proposed evidence-based multimodality reporting system stratifies solitary bone tumors in classes with increasing frequencies of malignancy.

Value of an online PI-RADS v2.1 score calculator for assessment of prostate MRI

Purpose

To evaluate the value of a browser-based PI-RADS Score Calculator (PCalc) compared to MRI reporting using the official PI-RADS v2.1 document (PDoc) for non-specialized radiologists in terms of reporting efficiency, interrater agreement and diagnostic accuracy for detection of clinically significant prostate cancer (PCa).

Methods

Between 09/2013 and 04/2015, 100 patients (median age, 64.8; range 47.5-78.2) who underwent prostate-MRI at a 3 T scanner and who received transperineal prostate mapping biopsy within <6 months were included in this retrospective study. Two non-specialized radiology residents (R1, R2) attributed a PI-RADS version 2.1 score for the most suspect (i. e. index) lesion (i) using the original PI-RADS v2.1 document only and after a 6-week interval (ii) using a browser-based PCalc. Reading time was measured. Reading time differences were assessed using Wilcoxon signed rank test. Intraclass-correlation Coefficient (ICC) was used to assess interrater agreement (IRA). Parameters of diagnostic accuracy and ROC curves were used for assessment of lesion-based diagnostic accuracy.

Results

Cumulative reading time was 32:55 (mm:ss) faster when using the PCalc, the difference being statistically significant for both readers (p < 0.05). The difference in IRA between the image sets (ICC 0.55 [0.40, 0.68]) and 0.75 [0.65, 0.82] for the image set with PDoc and PCalc, respectively) was not statistically significant. There was no statistically significant difference in lesion-based diagnostic accuracy (AUC 0.83 [0.74, 0.92] and 0.82 [95 %CI: 0.74, 0.91]) for images assessed with PDoc as compared to PCalc (AUC 0.82 [0.74, 0.91] and 0.74 [95 %CI: 0.64, 0.83]) for R1 and R2, respectively.

Conclusion

Non-specialized radiologists may increase reading speed in prostate MRI with the help of a browser-based PI-RADS Score Calculator compared to reporting using the official PI-RADS v2.1 document without impairing interreader agreement or lesion-based diagnostic accuracy for detection of clinically significant PCa.

Whole-body MRI for preventive health screening: Management strategies and clinical implications

PURPOSE

To document the diagnostic yields of whole-body MRI (WB-MRI) screening for asymptomatic individuals by using a classification system that categorizes the findings by clinical relevance and provides a flowchart for further investigations, and to determine the influence of WB-MRI findings on clinical decision-making.

METHODS

In this institutional review board-approved study, a retrospective review of individuals who underwent WB-MRI between 2009 and 2020 was conducted, and asymptomatic participants who underwent non-contrast-enhanced comprehensive WB-MRI for screening were enrolled. Participants were classified into four categories based on WB-MRI findings, and those with relevant findings (i.e., categories 3 and 4) were referred for further diagnostic workup. The participants' medical records were investigated, and interviews were conducted to reveal false-negative findings and identify the number of WB-MRI-triggered treatments.

RESULTS

We included 576 participants (377 [65.4 %] men, 199 [34.6 %] women; mean age, 48.40 ± 10.82 years), of which 266 (46.2 %) and 310 (53.8 %) underwent WB-MRI with 1.5 T and 3.0 T magnets, respectively. Approximately one-third of the participants showed clinically relevant findings, and 65 (11.2 %) received a treatment triggered by WB-MRI. Notably, 15 (2.6 %) and 28 (4.8 %) participants had cancers and intracranial aneurysms, respectively. Of the 576 participants, 16 (2.8 %) had false-negative findings, among which five had cancers.

CONCLUSION

WB-MRI yields numerous important findings that trigger therapeutic interventions in a large sample of asymptomatic adults. However, considering its inherent limitations, WB-MRI might be inadequate for detecting malignancies such as colon, thyroid, and breast cancers; thus, it may serve as a complementary screening method for health-conscious individuals.

Role of MRI for the detection of prostate cancer

The use of multiparametric MRI has been hastened under expanding, novel indications for its use in the diagnostic and management pathway of men with prostate cancer. This has helped drive a large body of the literature describing its evolving role over the last decade. Despite this, prostate cancer remains the only solid organ malignancy routinely diagnosed with random sampling. Herein, we summarize the components of multiparametric MRI and interpretation, and present a critical review of the current literature supporting is use in prostate cancer detection, risk stratification, and management.

False Positive Multiparametric Magnetic Resonance Imaging Phenotypes in the Biopsy-naïve Prostate: Are They Distinct from Significant Cancer-associated Lesions? Lessons from PROMIS

BACKGROUND

False positive multiparametric magnetic resonance imaging (mpMRI) phenotypes prompt unnecessary biopsies. The Prostate MRI Imaging Study (PROMIS) provides a unique opportunity to explore such phenotypes in biopsy-naïve men with raised prostate-specific antigen (PSA) and suspected cancer.

OBJECTIVE

To compare mpMRI lesions in men with/without significant cancer on transperineal mapping biopsy (TPM).

DESIGN, SETTING, AND PARTICIPANTS

PROMIS participants (n=235) underwent mpMRI followed by a combined biopsy procedure at University College London Hospital, including 5-mm TPM as the reference standard. Patients were divided into four mutually exclusive groups according to TPM findings: (1) no cancer, (2) insignificant cancer, (3) definition 2 significant cancer (Gleason ≥3+4 of any length and/or maximum cancer core length ≥4mm of any grade), and (4) definition 1 significant cancer (Gleason ≥4+3 of any length and/or maximum cancer core length ≥6mm of any grade).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Index and/or additional lesions present in 178 participants were compared between TPM groups in terms of number, conspicuity, volume, location, and radiological characteristics.

RESULTS AND LIMITATIONS

Most lesions were located in the peripheral zone. More men with significant cancer had two or more lesions than those without significant disease (67% vs 37%; p< 0.001). In the former group, index lesions were larger (mean volume 0.68 vs 0.50 ml; p< 0.001, Wilcoxon test), more conspicuous (Likert 4-5: 79% vs 22%; p< 0.001), and diffusion restricted (mean apparent diffusion coefficient [ADC]: 0.73 vs 0.86; p< 0.001, Wilcoxon test). In men with Likert 3 index lesions, log2PSA density and index lesion ADC were significant predictors of definition 1/2 disease in a logistic regression model (mean cross-validated area under the receiver-operator characteristic curve: 0.77 [95% confidence interval: 0.67-0.87]).

CONCLUSIONS

Significant cancer-associated MRI lesions in biopsy-naïve men have clinical-radiological differences, with lesions seen in prostates without significant disease. MRI-calculated PSA density and ADC could predict significant cancer in those with indeterminate MRI phenotypes.

PATIENT SUMMARY

Magnetic resonance imaging (MRI) lesions that mimic prostate cancer but are, in fact, benign prompt unnecessary biopsies in thousands of men with raised prostate-specific antigen. In this study we found that, on closer look, such false positive lesions have different features from cancerous ones. This means that doctors could potentially develop better tools to identify cancer on MRI and spare some patients from unnecessary biopsies.

Can multiparametric magnetic resonance of the prostate avoid biopsies in patients with elevated PSA and surgical indication for benign prostatic enlargement?

INTRODUCTION

To evaluate the clinical dilemma of men with surgical indication due to benign prostatic enlargement (BPE) and concomitant elevated PSA, we analysed if multiparametric magnetic resonance imaging (mpMRI) could safely prescind the prostate biopsy.

METHODS

Forty men with surgical indication due to BPE and concomitantly elevated PSA levels were prospectively enrolled and retrospectively analysed. All patients underwent 1.5 Tesla mpMRI prior to TRUS-guided biopsies. In cases where mpMRI was PIRADS 3 with focal lesions, PIRADS 4 or PIRADS 5, additional fragments were obtained with the fusion guided technique. Biopsy histopathological results were used as the standard of reference. Two scenarios were evaluated: scenario 1, considering mpMRI PIRADS 1 and 2 as negative; and scenario 2, considering PIRADS 1, 2 and 3 as negative. Clinically significant prostate cancer (CsPCa) was defined as ISUP ≥ 2.

RESULTS

Median age was 68 years, median PSA was 9.41 (6.40-19.54) and median prostatic volume was 116.5 cc (IQR 86.5-155). Scenario 1 mpMRI sensitivity, specificity, PPV, NPV and accuracy for any prostate cancer on prostate biopsy was 76.9%, 63%, 50%, 85% and 67.5%. For csPCa, they were 87.5%, 59.4%, 35%, 95% and 65%, respectively, for the same measures. Scenario 2 the sensitivity, specificity, PPV, NPV and accuracy of mpMRI for any prostate cancer on prostate biopsy was 53.8%, 96.3%, 87.5%, 81.3% and 82.5%. For csPCa, they were 75%, 93.8%, 75%, 93.8% and 90%, respectively, for the same measures.

CONCLUSION

Prostate mpMRI may prevent unnecessary biopsies in patients with elevated PSA and surgical indications due to BPE, given its high negative predictive value.

Comparison of Likert and PI-RADS version 2 MRI scoring systems for the detection of clinically significant prostate cancer

OBJECTIVE

To compare the performance of Likert and Prostate Imaging-Reporting and Data System (PI-RADS) multiparametric (mp) MRI scoring systems for detecting clinically significant prostate cancer (csPCa).

METHODS

199 biopsy-naïve males undergoing prostate mpMRI were prospectively scored with Likert and PI-RADS systems by four experienced radiologists. A binary cut-off (threshold score ≥3) was used to analyze histological results by three groups: negative, insignificant disease (Gleason 3 + 3; iPCa), and csPCa (Gleason ≥3 +4). Lesion-level results and prostate zonal location were also compared.

RESULTS

129/199 (64.8%) males underwent biopsy, 96 with Likert or PI-RADS score ≥3, and 21 with negative MRI. A further 12 patients were biopsied during follow-up (mean 507 days). Prostate cancer was diagnosed in 87/199 (43.7%) patients, 65 with (33.6%) csPCa. 30/92 (32.6%) patients with negative MRI were biopsied, with an NPV of 83.3% for cancer and 86.7% for csPCa. Likert and PI-RADS score differences were observed in 92 patients (46.2%), but only for 16 patients (8%) at threshold score ≥3. Likert scoring had higher specificity than PI-RADS (0.77 vs 0.66), higher area under the curve (0.92 vs 0.87, p = 0.002) and higher PPV (0.66 vs 0.58); NPV and sensitivity were the same. Likert had more five score results (58%) compared to PI-RADS (36%), but with similar csCPa detection (81.0 and 80.6% respectively). Likert demonstrated lower proportion of false positive in the predominately AFMS-involving lesions.

CONCLUSION

Likert and PI-RADS systems both demonstrate high cancer detection rates. Likert scoring had a higher AUC with moderately higher specificity and lower positive call rate and could potentially help to reduce the number of unnecessary biopsies performed.

ADVANCES IN KNOWLEDGE

This paper illustrates that the Likert scoring system has potential to help urologists reduce the number of prostate biopsies performed.

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.

Influence of the Location and Zone of Tumor in Prostate Cancer Detection and Localization on 3-T Multiparametric MRI Based on PI-RADS Version 2

OBJECTIVE. The objective of our study was to determine the performance of 3-T multiparametric MRI (mpMRI) for prostate cancer (PCa) detection and localization, stratified by anatomic zone and level, using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and whole-mount histopathology (WMHP) as reference. MATERIALS AND METHODS. Multiparametric MRI examinations of 415 consecutive men were compared with thin-section WMHP results. A genitourinary radiologist and pathologist collectively determined concordance. Two radiologists assigned PI-RADSv2 scores and sector location to all detected foci by consensus. Tumor detection rates were calculated for clinical and pathologic (tumor location and zone) variables. Both rigid and adjusted sector-matching models were used to account for fixation-related issues. RESULTS. Of 863 PCa foci in 16,185 prostate sectors, the detection of overall and index PCa lesions in the midgland, base, and apex was 54.9% and 83.1%, 42.1% and 64.0% (p = 0.04, p = 0.02), and 41.9% and 71.4% (p = 0.001, p = 0.006), respectively. Tumor localization sensitivity was highest in the midgland compared with the base and apex using an adjusted match compared with a rigid match (index lesions, 71.3% vs 43.7%; all lesions, 70.8% vs 36.0%) and was greater in the peripheral zone (PZ) than in the transition zone. Three-Tesla mpMRI had similarly high specificity (range, 93.8-98.3%) for overall and index tumor localization when using both rigid and adjusted sector-matching approaches. CONCLUSION. For 3-T mpMRI, the highest sensitivity (83.1%) for detection of index PCa lesions was in the midgland, with 98.3% specificity. Multiparametric MRI performance for sectoral localization of PCa within the prostate was moderate and was best for index lesions in the PZ using an adjusted model.

Differentiation of peripheral nerve sheath tumors in patients with neurofibromatosis type 1 using diffusion-weighted magnetic resonance imaging

BACKGROUND

We sought to determine the value of diffusion-weighted (DW) magnetic resonance imaging (MRI) for characterization of benign and malignant peripheral nerve sheath tumors (PNSTs) in patients with neurofibromatosis type 1 (NF1).

METHODS

Twenty-six patients with NF1 and suspicion of malignant transformation of PNSTs were prospectively enrolled and underwent DW MRI at 3T. For a set of benign (n = 55) and malignant (n = 12) PNSTs, functional MRI parameters were derived from both biexponential intravoxel incoherent motion (diffusion coefficient D and perfusion fraction f) and monoexponential data analysis (apparent diffusion coefficients [ADCs]). A panel of morphological MRI features was evaluated using T1- and T2-weighted imaging. Mann-Whitney U-test, Fisher's exact test, and receiver operating characteristic (ROC) analyses were applied to assess the diagnostic accuracy of quantitative and qualitative MRI. Cohen's kappa was used to determine interrater reliability.

RESULTS

Malignant PNSTs demonstrated significantly lower diffusivity (P < 0.0001) compared with benign PNSTs. The perfusion fraction f was significantly higher in malignant PNSTs (P < 0.001). In ROC analysis, functional MRI parameters showed high diagnostic accuracy for differentiation of PNSTs (eg, ADCmean, 92% sensitivity with 98% specificity, AUC 0.98; Dmean, 92% sensitivity with 98% specificity, AUC 0.98). By contrast, morphological imaging features had only limited sensitivity (18-94%) and specificity (18-82%) for identification of malignancy. Interrater reliability was higher for monoexponential data analysis.

CONCLUSION

DW imaging shows better diagnostic performance than morphological features and allows accurate differentiation of benign and malignant peripheral nerve sheath tumors in NF1.

Interobserver reproducibility of the PRECISE scoring system for prostate MRI on active surveillance: results from a two-centre pilot study

OBJECTIVES

We aimed to determine the interobserver reproducibility of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) criteria for magnetic resonance imaging in patients on active surveillance (AS) for prostate cancer (PCa) at two different academic centres.

METHODS

The PRECISE criteria score the likelihood of clinically significant change over time. The system is a 1-to-5 scale, where 1 or 2 implies regression of a previously visible lesion, 3 denotes stability and 4 or 5 indicates radiological progression. A retrospective analysis of 80 patients (40 from each centre) on AS with a biopsy-confirmed low- or intermediate-risk PCa (i.e. ≤ Gleason 3 + 4 and prostate-specific antigen ≤ 20 ng/ml) and ≥ 2 prostate MR scans was performed. Two blinded radiologists reported all scans independently and scored the likelihood of radiological change (PRECISE score) from the second scan onwards. Cohen's κ coefficients and percent agreement were computed.

RESULTS

Agreement was substantial both at a per-patient and a per-scan level (κ = 0.71 and 0.61; percent agreement = 79% and 81%, respectively) for each PRECISE score. The agreement was superior (κ = 0.83 and 0.67; percent agreement = 90% and 91%, respectively) when the PRECISE scores were grouped according to the absence/presence of radiological progression (PRECISE 1-3 vs 4-5). Higher inter-reader agreement was observed for the scans performed at University College London (UCL) (κ = 0.81 vs 0.55 on a per-patient level and κ = 0.70 vs 0.48 on a per-scan level, respectively). The discrepancies between institutions were less evident for percent agreement (80% vs 78% and 86% vs 75%, respectively).

CONCLUSIONS

Expert radiologists achieved substantial reproducibility for the PRECISE scoring system, especially when data were pooled together according to the absence/presence of radiological progression (PRECISE 1-3 vs 4-5).

KEY POINTS

• Inter-reader agreement between two experienced prostate radiologists using the PRECISE criteria was substantial. • The agreement was higher when the PRECISE scores were grouped according to the absence/presence of radiological progression (i.e. PRECISE 1-3 vs PRECISE 4 and 5). • Higher inter-reader agreement was observed for the scans performed at UCL, but the discrepancies between institutions were less evident for percent agreement.

Multiparametric MRI for prostate cancer diagnosis: current status and future directions

The current diagnostic pathway for prostate cancer has resulted in overdiagnosis and consequent overtreatment as well as underdiagnosis and missed diagnoses in many men. Multiparametric MRI (mpMRI) of the prostate has been identified as a test that could mitigate these diagnostic errors. The performance of mpMRI can vary depending on the population being studied, the execution of the MRI itself, the experience of the radiologist, whether additional biomarkers are considered and whether mpMRI-targeted biopsy is carried out alone or in addition to systematic biopsy. A number of challenges to implementation remain, such as ensuring high-quality execution and reporting of mpMRI and ensuring that this diagnostic pathway is cost-effective. Nevertheless, emerging clinical trial data support the adoption of this technology as part of the standard of care for the diagnosis of prostate cancer.

Role of Changes in Magnetic Resonance Imaging or Clinical Stage in Evaluation of Disease Progression for Men with Prostate Cancer on Active Surveillance

BACKGROUND

Active surveillance (AS) protocols rely on rectal examination, prostate-specific antigen, imaging, and biopsy to identify disease progression.

OBJECTIVE

To evaluate whether an AS regimen based on magnetic resonance imaging (MRI) or clinical stage changes can detect reclassification to grade group (GG) ≥2 disease compared with scheduled systematic biopsies.

DESIGN, SETTING, AND PARTICIPANTS

We identified a cohort of men initiated on AS between January 2013 and April 2016 at a single tertiary-care center. Patients completed confirmatory testing and prostate MRI prior to enrollment, then underwent laboratory and physical evaluation every 6 mo, MRI every 18 mo, and biopsy every 3yr.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

MRI results were evaluated using composite Likert/Prostate Imaging Reporting and Data System v2 scoring. MRI and clinical changes were assessed for association with disease progression. Univariable and multivariable regression models were used to predict upgrading on 3-yr biopsy.

RESULTS AND LIMITATIONS

At 3yr, of 207 men, 66 (32%) had≥GG2 at biopsy: 55 (83%) with GG2, 10 (15%) with GG3, and one (1.5%) with GG4. Among patients with a 3-yr MRI score of ≥3, 41% had≥GG2 disease, compared with 15% with an MRI score of <3 (p=0.0002). The MRI score increased in 48 men (23%), decreased in 27 (13%), and was unchanged in 132 (64%) men. Increases in MRI score were not associated with reclassification after adjusting for the 3-yr MRI score (p=0.9). Biopsying only for an increased MRI score or clinical stage would avoid 681 biopsies per 1000 men, at the cost of missing ≥GG2 disease in 169 patients.

CONCLUSIONS

An AS strategy that uses MRI or clinical changes to trigger prostate biopsy avoids many biopsies but misses an unacceptable amount of clinically significant disease. Prostate biopsy for men on AS should be performed at scheduled intervals, regardless of stable imaging or examination findings.

PATIENT SUMMARY

An active surveillance strategy for biopsy based only on increases in magnetic resonance imaging score or clinical stage will avoid many biopsies; however, it will miss many patients with clinically significant prostate cancer.

Endorectal power Doppler/grayscale ultrasound-guided biopsies vs. multiparametric MRI/ultrasound fusion-guided biopsies in males with high risk of prostate cancer: A prospective cohort study

Multiparametric MRI fusion with transrectal ultrasound (mpMRI/TRUS)-guided biopsy has the sensitivity of mpMRI with the practicality of TRUS, but males with no cancerous lesion(s) detected on mpMRI have a considerable remaining risk of cancer. Endorectal power Doppler ultrasound improves the sensitivity of grayscale ultrasound-guided biopsies. The objective of the present study was to evaluate the beneficial effect of endorectal power Doppler/grayscale ultrasound-guided biopsy over that of mpMRI/TRUS-guided biopsy for decision-making regarding prostatectomy in males with a high risk of prostate cancer. Data regarding endorectal power Doppler/grayscale ultrasound-guided biopsies and mpMRI/TRUS-guided biopsies of 1,094 males with elevated specific prostate antigen, were included. Radical prostatectomy was performed in males aged <70 years with Gleason scores ≥3+4 in any one of the biopsy reports. The histopathological data of the surgical specimen of 776 males were included in the analysis. Compared to the histopathology of the surgical specimen, endorectal power Doppler/grayscale ultrasound-guided biopsies had a lower sensitivity (0.930 vs. 1.000; P<0.0001) but mpMRI/TRUS-guided biopsies had the same sensitivity (0.990 vs. 1.000; P=0.02). The accuracy of mpMRI/TRUS-guided biopsies was higher than that of endorectal power Doppler/grayscale ultrasound-guided biopsies (0.944 vs. 0.783). On mpMRI, lesions of 105 subjects (10%) with a Likert scale score of <3 were identified. Among them, 14 subjects (2%) had Gleason scores of ≥3+4 as determined by endorectal power Doppler/grayscale ultrasound-guided biopsies. In addition, 20 (2%) false-positive lesions compared to the histopathological analysis of the surgical specimen were identified from mpMRI/TRUS-guided biopsies. In conclusion, mpMRI/TRUS-guided biopsy was indicated to have a moderate performance and endorectal power Doppler/grayscale ultrasound-guided biopsy had a scant performance for decision-making regarding prostatectomy.

Likert vs PI-RADS v2: a comparison of two radiological scoring systems for detection of clinically significant prostate cancer

OBJECTIVE

To compare the clinical validity and utility of Likert assessment and the Prostate Imaging Reporting and Data System (PI-RADS) v2 in the detection of clinically significant and insignificant prostate cancer.

PATIENTS AND METHODS

A total of 489 pre-biopsy multiparametric magnetic resonance imaging (mpMRI) scans in consecutive patients were subject to prospective paired reporting using both Likert and PI-RADS v2 by expert uro-radiologists. Patients were offered biopsy for any Likert or PI-RADS score ≥4 or a score of 3 with PSA density ≥0.12 ng/mL/mL. Utility was evaluated in terms of proportion biopsied, and proportion of clinically significant and insignificant cancer detected (both overall and on a 'per score' basis). In those patients biopsied, the overall accuracy of each system was assessed by calculating total and partial area under the receiver-operating characteristic (ROC) curves. The primary threshold of significance was Gleason ≥3 + 4. Secondary thresholds of Gleason ≥4 + 3, Ahmed/UCL1 (Gleason ≥4 + 3 or maximum cancer core length [CCL] ≥6 or total CCL≥6) and Ahmed/UCL2 (Gleason ≥3 + 4 or maximum CCL ≥4 or total CCL ≥6) were also used.

RESULTS

The median (interquartile range [IQR]) age was 66 (60-72) years and the median (IQR) prostate-specific antigen level was 7 (5-10) ng/mL. A similar proportion of men met the biopsy threshold and underwent biopsy in both groups (83.8% [Likert] vs 84.8% [PI-RADS v2]; P = 0.704). The Likert system predicted more clinically significant cancers than PI-RADS across all disease thresholds. Rates of insignificant cancers were comparable in each group. ROC analysis of biopsied patients showed that, although both scoring systems performed well as predictors of significant cancer, Likert scoring was superior to PI-RADS v2, exhibiting higher total and partial areas under the ROC curve.

CONCLUSIONS

Both scoring systems demonstrated good diagnostic performance, with similar rates of decision to biopsy. Overall, Likert was superior by all definitions of clinically significant prostate cancer. It has the advantages of being flexible, intuitive and allowing inclusion of clinical data. However, its use should only be considered once radiologists have developed sufficient experience in reporting prostate mpMRI.

Diagnostic evaluation of diffusion kurtosis imaging for prostate cancer: Detection in a biopsy population

PURPOSE

To prospectively assess the feasibility of diffusional kurtosis (DK) imaging for distinguishing prostate cancer(PCa) from benign prostate hyperplasia (BPH) in comparison with standard diffusion-weighted (DW) imaging, as well as low-from high-grade malignant regions.

MATERIALS AND METHODS

147 consecutive patients with suspected PCa underwent multi-parametric 1.5-TMR. Diffusion kurtosis imaging was acquired with with 5 b values (0,600,800,1600,and 2400sec/mm2).Region of interest (ROI)-based measurements were performed on ADC, D, and K map by two radiologists. Data were analyzed by using mixed-model analysis of variance and receiver operating characteristic curves. Correlations among the three parameters (ADC,D and K) in all patients, and correlations between three parameters with the tumor Gleason score (GS) in PCa group were analyzed using Pearson's correlation coefficient in peripheral zone(PZ) and transiton zone(TZ).

RESULTS

58 patients were proved with PCa (9 GS 3 + 3[PZ/TZ = 4/5], 49 GS ≥ 7 [PZ/TZ = 26/23]), and 89 patients were with BPH. ADC,D and K were able to distinguish benignance from tumor tissue both in PZ and TZ(P＜0.01), but performed poorly in neither differentiating low-(GS 3 + 3) from high-grade (GS≥3 + 4) disease, nor GS(3 + 4) from GS(4 + 3).There was a weak correlation between the GS and ADC, D (PZ:ADC r=-0.113, D r=-0.139; TZ:ADC r=-0.104,D r=-0.103), while a moderate correlation between the GS and K(PZ:K r = 0.492; TZ:K r = 0.433, P＜0.01).K had significantly greater area under the curve for differentiating PCa from BHP than ADC both in PZ and TZ.

CONCLUSION

DK model may add value in PCa detection and diagnosis, but none can differentiate low-from high-grade PCas (including GS=3+4 from GS=4+3).

Improved Assessment of Middle Ear Recurrent Cholesteatomas Using a Fusion of Conventional CT and Non-EPI-DWI MRI

BACKGROUND AND PURPOSE

Recurrent middle ear cholesteatomas are commonly preoperatively assessed using MR imaging (non-EPI-DWI) and CT. Both modalities are used with the aim of distinguishing scar tissue from cholesteatoma and determining the extent of bone erosions. Inflammation and scar tissue associated with the lesions might hamper a proper delineation of the corresponding extensions on CT images. Using surgical findings as the criterion standard, we assessed the recurrent middle ear cholesteatoma extent using either uncoregistered or fused CT-MR imaging datasets and determined the corresponding accuracy and repeatability.

MATERIALS AND METHODS

Twenty consecutive patients with suspected recurrent middle ear cholesteatoma and preoperative CT-MR imaging datasets were prospectively included. A double-blind assessment and coregistration of the recurrent middle ear cholesteatoma extent and manual delineation of 18 presumed recurrent middle ear cholesteatomas were performed by 2 radiologists and compared with the criterion standard. "Reliability score" was defined to qualify radiologists' confidence. For each volume, segmentation repeatability was assessed on the basis of intraclass correlation coefficient and overlap indices.

RESULTS

For the whole set of patients, recurrent middle ear cholesteatoma was further supported by surgical results. Two lesions were excluded from the analysis, given that MR imaging did not show a restricted diffusion. Lesions were accurately localized using the fused datasets, whereas significantly fewer lesions (85%) were correctly localized using uncoregistered images. Reliability scores were larger for fused datasets. Segmentation repeatability showed an almost perfect intraclass correlation coefficient regarding volumes, while overlaps were significantly lower in uncoregistered (52%) compared with fused (60%, P < .001) datasets.

CONCLUSIONS

The use of coregistered CT-MR images significantly improved the assessment of recurrent middle ear cholesteatoma with a greater accuracy and better reliability and repeatability.

18F-Choline PET/mpMRI for Detection of Clinically Significant Prostate Cancer: Part 1. Improved Risk Stratification for MRI-Guided Transrectal Prostate Biopsies

A prospective single-arm clinical trial was conducted to determine whether ¹⁸F-choline PET/mpMRI can improve the specificity of multiparametric MRI (mpMRI) of the prostate for Gleason ≥ 3+4 prostate cancer. Methods: Before targeted and systematic prostate biopsy, mpMRI and ¹⁸F-choline PET/CT were performed on 56 evaluable subjects with 90 Likert score 3-5 mpMRI target lesions, using a ¹⁸F-choline target-to-background ratio of greater than 1.58 to indicate a positive ¹⁸F-choline result. Prostate biopsies were performed after registration of real-time transrectal ultrasound with T2-weighted MRI. A mixed-effects logistic regression was applied to measure the performance of mpMRI (based on prospective Likert and retrospective Prostate Imaging Reporting and Data System, version 2 [PI-RADS], scores) compared with ¹⁸F-choline PET/mpMRI to detect Gleason ≥ 3+4 cancer. Results: The per-lesion accuracy of systematic plus targeted biopsy for mpMRI alone was 67.8% (area under receiver-operating-characteristic curve [AUC], 0.73) for Likert 4-5 and 70.0% (AUC, 0.76) for PI-RADS 3-5. Several PET/MRI models incorporating ¹⁸F-choline with mpMRI data were investigated. The most promising model selected all high-risk disease on mpMRI (Likert 5 or PI-RADS 5) plus low- and intermediate-risk disease (Likert 4 or PI-RADS 3-4), with an elevated ¹⁸F-choline target-to-background ratio greater than 1.58 as positive for significant cancer. Using this approach, the accuracy on a per-lesion basis significantly improved to 88.9% for Likert (AUC, 0.90; P < 0.001) and 91.1% for PI-RADS (AUC, 0.92; P < 0.001). On a per-patient basis, the accuracy improved to 92.9% for Likert (AUC, 0.93; P < 0.001) and to 91.1% for PI-RADS (AUC, 0.91; P = 0.009). Conclusion: ¹⁸F-choline PET/mpMRI improved the identification of Gleason ≥ 3+4 prostate cancer compared with mpMRI, with the principal effect being improved risk stratification of intermediate-risk mpMRI lesions.

The Evolution of MRI of the Prostate: The Past, the Present, and the Future

OBJECTIVE. The purpose of this article is to discuss the evolution of MRI in prostate cancer from the early 1980s to the current day, providing analysis of the key studies on this topic. CONCLUSION. The rapid diffusion of MRI technology has meant that residual variability remains between centers regarding the quality of acquisition and the quality and standardization of reporting.

Diagnostic Performance of Multiparametric Transrectal Ultrasound in Localized Prostate Cancer: A Comparative Study With Magnetic Resonance Imaging

OBJECTIVES

The aim of this study was to compare the diagnostic performance of multiparametric transrectal ultrasound (TRUS), including grayscale imaging, color Doppler imaging, shear wave elastography, and contrast-enhanced ultrasound, to that of multiparametric magnetic resonance imaging (MRI), including T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI, in the diagnosis of localized prostate cancer (PCa; lesions at stage T2 or lower).

METHODS

Seventy-eight patients were prospectively enrolled, including 40 in a benign prostate group and 38 in a localized PCa group (≤T2). The diagnostic performance of multiparametric TRUS and multiparametric MRI in detecting localized PCa was analyzed with surgical and biopsy pathologic results as the references.

RESULTS

Multiparametric TRUS had higher sensitivity, negative predictive value, and accuracy than multiparametric MRI (97.4% versus 94.7%, 96.9% versus 92.3%, and 87.2% versus 76.9%, respectively) for detecting localized PCa. The mean area under the receiver operating characteristic curve ± SD for multiparametric TRUS was 0.874 ± 0.043 (95% confidence interval, 0.790-0.959), and it was 0.774 ± 0.055 (95% confidence interval, 0.666-0.881) for multiparametric MRI.

CONCLUSIONS

Our results suggest that multiparametric TRUS has high diagnostic performance in the diagnosis of localized PCa. Multiparametric TRUS is compatible with multiparametric MRI in the detection of localized PCa (≤T2).

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.

Can We Improve the Preoperative Prediction of Prostate Cancer Recurrence With Multiparametric MRI?

INTRODUCTION

The use of multiparametric magnetic resonance imaging (mpMRI) to assess prostate cancer (PCa) has increased over the past decade. We aimed to assess if preoperative mpMRI lesion score, a variable routinely available for men undergoing pre-biopsy MRI, improves the performance of commonly used preoperative predictive models for PCa recurrence.

PATIENTS AND METHODS

We analyzed data from 372 patients with PCa treated with radical prostatectomy in 2012 to 2017 and assessed with pre-biopsy mpMRI within 6 months prior to surgery. Suspicious areas for cancer were scored on a standardized 5-point scale. Cox regression was used to assess the association between mpMRI score and the risk of postoperative biochemical recurrence. Two different models were tested accounting for factors included in the Kattan nomogram and in the D'Amico risk-classification.

RESULTS

Overall, 53% and 30% of patients were found with a lesion scored 4 or 5 at pre-biopsy mpMRI, respectively. Risk varied widely by mpMRI (29% 2-year risk of biochemical recurrence for a score of 5 vs. 5% for a score of 1-2), and mpMRI score was associated with large hazard ratios after adjusting for stage, grade, and prostate-specific antigen: 1.66, 1.96, and 2.71 for scores 3, 4, and 5, respectively. However, 95% confidence intervals were very wide (0.19-14.20, 0.26-14.65, and 0.36-20.55, respectively) and included 1.

CONCLUSIONS

Our data did not show that preoperative models, commonly used to assess PCa risk, were improved after including the pre-biopsy mpMRI score. However, the value of pre-biopsy mpMRI to improve preoperative risk models should be investigated in larger data sets.

The need for standardization of nuclear cardiology reporting and data system (NCAD-RADS): Learning from coronary artery disease (CAD), breast imaging (BI), liver imaging (LI), and prostate imaging (PI) RADS

Newer structured reporting manners, the reporting and data system (RADS), have made vast steps in improving standardized and structured reporting, allowing better communication between radiologists and referring providers. This has been implemented in several fields: breast (BI-RADS), lung (Lung-RADS), liver (LI-RADS), thyroid (TI-RADS), prostate (PI-RADS), and in cardiovascular radiology (CAD-RADS). The field of nuclear cardiology began its efforts of standardization years ago; however, a widespread standardized reporting structure has not yet been adopted. Such an approach in nuclear cardiology, the nuclear cardiology reporting and data system (NCAD-RADS), will assist radiologists and treating clinicians in conveying and understanding reports and determining the appropriate next steps in management. By linking explicit findings to defined recommendations, patients will receive more consistent and appropriate care.

Characteristics of missed prostate cancer lesions on 3T multiparametric-MRI in 518 patients: based on PI-RADSv2 and using whole-mount histopathology reference

PURPOSE

To determine the characteristics of missed prostate cancer (PCa) lesions on 3T multiparametric-MRI (mpMRI) based on PI-RADSv2 with whole-mount histopathology (WMHP) correlation.

MATERIALS AND METHODS

This IRB-approved, HIPAA-compliant study, included 614 consecutive men with 3T mpMRI prior to prostatectomy at a single tertiary center between 12/2009 and 4/2017. Clinical, mpMRI, and pathologic features were obtained. PI-RADSv2-based MRI detected lesions were matched with previously finalized WMHP by a genitourinary (GU) radiologist and a GU pathologist. Patients with no mpMRI detected PCa lesion, but with at least one lesion ≥ 1 cm on WMHP, were reviewed retrospectively and assigned a PI-RADSv2 score. Tumor characteristics were compared between missed and detected lesions.

RESULT

The final cohort included 518 patients with 1085 WMHP lesions. 51.9% (563/1085) of lesions were missed on 3T mpMRI. 71.4% (402/563), 21.7% (122/563), 4.4% (25/563), and 2.5% (14/563) of the missed lesions were Gleason scores (GS) 3 + 3, 3 + 4, 4 + 3, and 8 - 10, respectively. Missed PCa lesions had significantly lower proportion of GS ≥ 7 (p < 0.001) and smaller size for overall (p < 0.001) and index subcohorts (p < 0.001), as compared to detected lesions. 34.5% (194) of overall and 71.2% (79) index missed lesions were larger than 1 cm. In 13.7% (71/518) of patients without MR detected PCa, 149 lesions were detected on WMHP, with 70 (47%) lesions ≥ 1 cm. In retrospective review of these lesions, 42.9% (30), 18.6% (13), 21.5% (15), 10% (7), and 7% (5) were PI-RADSv2 1, 2, 3, 4, and 5, respectively.

CONCLUSION

3T mpMRI has an excellent per patients diagnostic performance for PCa and majority of missed lesions are clinically nonsignificant.

Comparison of Gleason upgrading rates in transrectal ultrasound systematic random biopsies versus US-MRI fusion biopsies for prostate cancer

PURPOSE

Ultrasound-magnetic resonance imaging (US-MRI) fusion biopsy (FB) improves the detection of clinically significant prostate cancer (PCa). We aimed to compare the Gleason upgrading (GU) rates and the concordance of the Gleason scores in the biopsy versus final pathology after surgery in patients who underwent transrectal ultrasound (TRUS) systematic random biopsies (SRB) versus US-MRI FB for PCa.

MATERIALS AND METHODS

A retrospective analysis of data that were collected prospectively from January 2011 to June 2016 from patients who underwent prostate biopsy and subsequent radical prostatectomy. The study cohort was divided into two groups: US-MRI FB (Group A) and TRUS SRB (Group B). US-MRI FB was performed in patients with a previous MRI with a focal lesion with a Likert score ≥3; otherwise, a TRUS SRB was performed.

RESULTS

In total, 73 men underwent US-MRI FB, and 89 underwent TRUS SRB. The GU rate was higher in Group B (31.5% vs. 16.4%; p=0.027). According to the Gleason grade pattern, GU was higher in Group B than in Group A (40.4% vs. 23.3%; p=0.020). Analyses of the Gleason grading patterns showed that Gleason scores 3+4 presented less GU in Group A (24.1% vs. 52.6%; p=0.043). The Bland-Altman plot analysis showed a higher bias in Group B than in Group A (-0.27 [-1.40 to 0.86] vs. -0.01 [-1.42 to 1.39]). In the multivariable logistic regression analysis, the only independent predictor of GU was the use of TRUS SRB (2.64 [1.11 - 6.28]; p=0.024).

CONCLUSIONS

US-MRI FB appears to be related to a decrease in GU rate and an increase in concordance between biopsy and final pathology compared to TRUS SRB, suggesting that performing US-MRI FB leads to greater accuracy of diagnosis and better treatment decisions.