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

Editorial on "Head-to-Head Comparison of PI-RADS Version 2 and 2.1 in Transition Zone Lesions for Detection of Prostate Cancer"

LEVEL OF EVIDENCE

5 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:587-588.

LinSEM: Linearizing segmentation evaluation metrics for medical images

Numerous algorithms are available for segmenting medical images. Empirical discrepancy metrics are commonly used in measuring the similarity or difference between segmentations by algorithms and "true" segmentations. However, one issue with the commonly used metrics is that the same metric value often represents different levels of "clinical acceptability" for different objects depending on their size, shape, and complexity of form. An ideal segmentation evaluation metric should be able to reflect degrees of acceptability directly from metric values and be able to show the same acceptability meaning by the same metric value for objects of different shape, size, and form. Intuitively, metrics which have a linear relationship with degree of acceptability will satisfy these conditions of the ideal metric. This issue has not been addressed in the medical image segmentation literature. In this paper, we propose a method called LinSEM for linearizing commonly used segmentation evaluation metrics based on corresponding degrees of acceptability evaluated by an expert in a reader study. LinSEM consists of two main parts: (a) estimating the relationship between metric values and degrees of acceptability separately for each considered metric and object, and (b) linearizing any given metric value corresponding to a given segmentation of an object based on the estimated relationship. Since algorithmic segmentations do not usually cover the full range of variability of acceptability, we create a set (SS) of simulated segmentations for each object that guarantee such coverage by using image transformations applied to a set (ST) of true segmentations of the object. We then conduct a reader study wherein the reader assigns an acceptability score (AS) for each sample in SS, expressing the acceptability of the sample on a 1 to 5 scale. Then the metric-AS relationship is constructed for the object by using an estimation method. With the idea that the ideal metric should be linear with respect to acceptability, we can then linearize the metric value of any segmentation sample of the object from a set (SA) of actual segmentations to its linearized value by using the constructed metric-acceptability relationship curve. Experiments are conducted involving three metrics - Dice coefficient (DC), Jaccard index (JI), and Hausdorff Distance (HD) - on five objects: skin outer boundary of the head and neck (cervico-thoracic) body region superior to the shoulders, right parotid gland, mandible, cervical esophagus, and heart. Actual segmentations (SA) of these objects are generated via our Automatic Anatomy Recognition (AAR) method. Our results indicate that, generally, JI has a more linear relationship with acceptability before linearization than other metrics. LinSEM achieves significantly improved uniformity of meaning post-linearization across all tested objects and metrics, except in a few cases where the departure from linearity was insignificant. This improvement is generally the largest for DC and HD reaching 8-25% for many tested cases. Although some objects (such as right parotid gland and esophagus for DC and JI) are close in their meaning between themselves before linearization, they are distant in this meaning from other objects but are brought close to other objects after linearization. This suggests the importance of performing linearization considering all objects in a body region and body-wide.

Role of MRI in Staging of Penile Cancer

Penile cancer is one of the male-specific cancers. Accurate pretreatment staging is crucial due to a plethora of treatment options currently available. The 8^th edition American Joint Committee on Cancer-Tumor Node and Metastasis (AJCC-TNM) revised the staging for penile cancers, with invasion of corpora cavernosa upstaged from T2 to T3 and invasion of urethra downstaged from T3 to being not separately relevant. With this revision, MRI is more relevant in local staging because MRI is accurate in identifying invasion of corpora cavernosa, while the accuracy is lower for detection of urethral involvement. The recent European Urology Association (EAU) guidelines recommend MRI to exclude invasion of the corpora cavernosa, especially if penis preservation is planned. Identification of satellite lesions and measurement of residual-penile-length help in surgical planning. When nonsurgical treatment modalities of the primary tumor are being considered, accurate local staging helps in decision-making regarding upfront inguinal lymph node dissection as against surveillance. MRI helps in detection and extent of inguinal and pelvic lymphadenopathy and is superior to clinical palpation, which continues to be the current approach recommended by National Comprehensive Cancer Network (NCCN) treatment guidelines. MRI helps the detection of "bulky" lymph nodes that warrant neoadjuvant chemotherapy and potentially identify extranodal extension. However, tumor involvement in small lymph nodes and differentiation of reactive vs. malignant lymphadenopathy in large lymph nodes continue to be challenging and the utilization of alternative contrast agents (superparamagnetic iron oxide), positron emission tomography (PET)-MRI along with texture analysis is promising. In locally recurrent tumors, MRI is invaluable in identification of deep invasion, which forms the basis of treatment. Multiparametric MRI, especially diffusion-weighted-imaging, may allow for quantitative noninvasive assessment of tumor grade and histologic subtyping to avoid biopsy undersampling. Further research is required for incorporation of MRI with deep learning and artificial intelligence algorithms for effective staging in penile cancer. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1612-1629.

Assessing Extraprostatic Extension with Multiparametric MRI of the Prostate: Mehralivand Extraprostatic Extension Grade or Extraprostatic Extension Likert Scale?

Purpose

To validate the MRI grading system proposed by Mehralivand et al in 2019 (the "extraprostatic extension [EPE] grade") in an independent cohort and to compare the Mehralivand EPE grading system with EPE interpretation on the basis of a five-point Likert score ("EPE Likert").

Materials and Methods

A total of 310 consecutive patients underwent multiparametric MRI according to a standardized institutional protocol before radical prostatectomy was performed by using the same 1.5-T MRI unit at a single institution between 2010 and 2012. Two radiologists blinded to clinical information assessed EPE according to standardized criteria. On the basis of the readings performed until 2017, the diagnostic performance of EPE Likert and Mehralivand EPE score were compared using receiver operating characteristics (ROC) and decision curve methodology against histologic EPE as standard of reference. Prediction of biochemical recurrence-free survival (BRFS) was assessed by Kaplan-Meier analysis and log rank test.

Results

Of the 310 patients, 80 patients (26%) had EPE, including 33 with radial distance 1.1 mm or greater. Interrater reliability was fair (weighted κ 0.47 and 0.45) for both EPE grade and EPE Likert. Sensitivity for identifying EPE using EPE grade versus EPE Likert was 0.83 versus 0.86 and 0.86 versus 0.91 for radiologist 1 and 2, respectively. Specificity was 0.48 versus 0.58 and 0.39 versus 0.70 (P < .05 for radiologist 2). There were no significant differences in the ROC area under the curve or on decision curve analysis. Both EPE grade and EPE Likert were significant predictors of BRFS.

Conclusion

Mehralivand EPE grade and EPE Likert have equivalent diagnostic performance for predicting EPE and BRFS with a similar degree of observer dependence.© RSNA, 2020Keywords: MR-Imaging, Neoplasms-Primary, Observer Performance, Outcomes Analysis, Prostate, StagingSupplemental material is available for this article.See also the commentary by Choyke in this issue.

Prostate Multiparametric Magnetic Resonance Imaging Features Following Partial Gland Cryoablation

OBJECTIVE

To assess the qualitative and quantitative changes on prostate multiparametric magnetic resonance imaging (mpMRI) following partial gland ablation (PGA) with cryotherapy and correlate with histopathology.

METHODS

We used 3D Slicer to generate prostate models and segment ipsilateral (treated) and contralateral peripheral and transition zones in 10 men who underwent MRI/transrectal ultrasound fusion-guided PGA during 2017-2018. Pre- and post-PGA volumes of prostate segments were compared. Post-PGA mpMRI were categorized according to PI-RADS v2 and treatment response on mpMRI was assessed in a manner similar to the radiology evaluation framework following liver lesion ablation.

RESULTS

Median volume of ipsilateral peripheral and transition zones decreased from 10.9 mL and 13.0 mL to 7.2 mL and 10.8 mL (P = .005), respectively. Median volume of contralateral peripheral and transition zones also decreased from 12.1 mL and 12.5 mL to 9.9 mL to 10.4 mL (P = .005), respectively. Five men had clinically significant disease (Grade group ≥2) on post-PGA biopsy (3 within treatment field and 2 outside). Of the men with clinically significant prostate cancer, mpMRI revealed PI-RADS 3 lesions in 2. However, the treatment response framework did not detect residual disease.

CONCLUSION

PGA results in asymmetrical and significant reductions in prostate volume. Our results highlight the need for a separate assessment framework to enable standardization of the interpretation and reporting of post-PGA surveillance mpMRI. Moreover, our findings have significant implications for MRI-targeted surveillance biopsy following PGA with cryotherapy.

Histogram analysis from stretched exponential model on diffusion-weighted imaging: evaluation of clinically significant prostate cancer

OBJECTIVE

To evaluate the usefulness of histogram analysis of stretched exponential model (SEM) on diffusion-weighted imaging in evaluating clinically significant prostate cancer (CSC).

METHODS

A total of 85 patients with prostate cancer underwent 3 T multiparametric MRI, followed by radical prostatectomy. Histogram parameters of the tumor from the SEM [distributed diffusion coefficient (DDC) and α] and the monoexponential model [MEM; apparent diffusion coefficient (ADC)] were evaluated. The associations between parameters and Gleason score or Prostate Imaging Reporting and Data System v. 2 were evaluated. The area under the receiver operating characteristics curve was calculated to evaluate diagnostic performance of parameters in predicting CSC.

RESULTS

The values of histogram parameters of DDC and ADC were significantly lower in patients with CSC than in patients without CSC (p < 0.05), except for skewness and kurtosis. The value of the 25th percentile of α was significantly lower in patients with CSC than in patients without CSC (p = 0.014). Histogram parameters of ADC and DDC had significant weak to moderate negative associations with Gleason score or Prostate Imaging Reporting and Data System v. 2 (p < 0.001), except for skewness and kurtosis. For predicting CSC, the area under the curves of mean ADC (0.856), 50th percentile DDC (0.852), and 25th percentile α (0.707) yielded the highest values compared to other histogram parameters from each group.

CONCLUSION

Histogram analysis of the SEM on diffusion-weighted imaging may be a useful quantitative tool for evaluating CSC. However, the SEM did not outperform the MEM.

ADVANCES IN KNOWLEDGE

Histogram parameters of SEM may be useful for evaluating CSC.

Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

Automatic segmentation of prostatic zones on multiparametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89±0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (Deeplab V3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.

Predictive clinical features for negative histopathology of MRI/Ultrasound-fusion-guided prostate biopsy in patients with high likelihood of cancer at prostate MRI: Analysis from a urologic outpatient clinic1

OBJECTIVE

The aim of this study was to evaluate clinical features associated with benign histopathology of Prostate Imaging Reporting and Data System (PI-RADS) category 4 and 5 lesions.

MATERIALS AND METHODS

Between March 2015 and November 2020, 1161 patients underwent mpMRI/Ultrasound-fusion-guided prostate biopsy (FBx) and concurrent 12-core systematic prostate biopsy (SBx) at the Department of Urology of the Ludwig-Maximilians-University of Munich, Germany. 848/ 1161 (73%) patients presented with either PI-RADS 4 or 5 index lesion and were retrospectively evaluated. Multivariate analysis was performed to evaluate clinical parameters associated with a negative outcome of PI-RADS 4 or 5 category lesions after FBx. Area under the receiver operating characteristics (ROC) curve (AUC) was conducted using ROC-analysis.

RESULTS

676/848 (79.7%) patients with either PI-RADS 4 or 5 index lesion were diagnosed with prostate cancer (PCa) by FBx and 172/848 (20.3%) patients had a negative biopsy (including the concurrent systematic prostate biopsy), respectively. Prostate volume (P-Vol) (OR 0.99, 95% CI = 0.98-1.00, p = 0.038), pre-biopsy-status (OR 0.48, 95% CI = 0.29-0.79, p = 0.004) and localization of the lesion in the transitional zone (OR 0.28, 95% CI = 0.13-0.60, p = 0.001) were independent risk factors for a negative outcome of FBx. Age (OR 1.09, 95% CI = 1.05-1.13, p < 0.001) and PSA density (PSAD) (OR 75.92, 95% CI = 1.03-5584.61, p = 0.048) increased the risk for PCa diagnosis after FBx. The multivariate logistic regression model combining all clinical characteristics achieved an AUC of 0.802 (95% CI = 0.765-0.835; p < 0.001) with a sensitivity and specificity of 66% and 85%.

CONCLUSION

Lesions with high or highly likelihood of PCa on multiparametric magnetic resonance imaging (mpMRI) but subsequent negative prostate biopsy occur in a small amount of patients. Localization of the lesion in the transitional zone, prostate volume and prebiopsy were shown to be predictors for benign histopathology of category 4 or 5 lesions on mpMRI. Integration of these features into daily clinical routine could be used for risk-stratification of these patients after negative biopsy of PI-RADS 4 or 5 index lesions.

The role of gadolinium in magnetic resonance imaging for early prostate cancer diagnosis: A diagnostic accuracy study

Objective

Prostate lesions detected with multiparametric magnetic resonance imaging (mpMRI) are classified for their malignant potential according to the Prostate Imaging-Reporting And Data System (PI-RADS™2). In this study, we evaluate the diagnostic accuracy of the mpMRI with and without gadolinium, with emphasis on the added diagnostic value of the dynamic contrast enhancement (DCE).

Materials and methods

The study was retrospective for 286 prostate lesions / 213 eligible patients, n = 116/170, and 49/59% malignant for the peripheral (Pz) and transitional zone (Tz), respectively. A stereotactic MRI-guided prostate biopsy served as the histological ground truth. All patients received a mpMRI with DCE. The influence of DCE in the prediction of malignancy was analyzed by blinded assessment of the imaging protocol without DCE and the DCE separately.

Results

Significant (CSPca) and insignificant (IPca) prostate cancers were evaluated separately to enhance the potential effects of the DCE in the detection of CSPca. The Receiver Operating Characteristics Area Under Curve (ROC-AUC), sensitivity (Se) and specificity (Spe) of PIRADS-without-DCE in the Pz was 0.70/0.47/0.86 for all cancers (IPca and CSPca merged) and 0.73/0.54/0.82 for CSPca. PIRADS-with-DCE for the same patients showed ROC-AUC/Se/Spe of 0.70/0.49/0.86 for all Pz cancers and 0.69/0.54/0.81 for CSPca in the Pz, respectively, p>0.05 chi-squared test. Similar results for the Tz, AUC/Se/Spe for PIRADS-without-DCE was 0.75/0.61/0.79 all cancers and 0.67/0.54/0.71 for CSPca, not influenced by DCE (0.66/0.47/0.81 for all Tz cancers and 0.61/0.39/0.75 for CSPca in Tz). The added Se and Spe of DCE for the detection of CSPca was 88/34% and 78/33% in the Pz and Tz, respectively.

Conclusion

DCE showed no significant added diagnostic value and lower specificity for the prediction of CSPca compared to the non-enhanced sequences. Our results support that gadolinium might be omitted without mitigating the diagnostic accuracy of the mpMRI for prostate cancer.

Association of volume of self-directed versus assigned interpretive work with diagnostic performance of radiologists: an observational study

OBJECTIVES

To understand the sources of variability in diagnostic performance among experienced radiologists.

DESIGN

All prostate MRI examinations performed between 2016 and 2018 were retrospectively reviewed.

SETTING

University hospital in Japan.

PARTICIPANTS

Data derived from 334 pathology-proven cases (male, mean age: 70 years; range: 35-90 years) that were interpreted by 10 experienced radiologists were subjected to the analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

Diagnostic performance measures of the radiologists were compared with candidate factors, including interpretive volume of prostate MRIs, volume of self-directed and assigned total annual interpretive work, and years of experience. The potential influence of fatigue was also evaluated by examining the effect of the report's issue time.

RESULTS

There were 186 prostate cancer cases. Performance was based on accuracy, sensitivity and specificity (86%, 85% and 84%, respectively). While performance was not correlated with the volume of prostate MRIs, per se (ρ=-0.15, p=0.69; ρ=-0.01, p=0.99; ρ=-0.33, p=0.36) or the total MRIs assigned for each radiologist (p>0.6) or years of experience (p>0.4), all measures were strongly correlated with voluntary work represented by the interpretive volume of abdominal CTs (r=0.79, p<0.01; r=0.80, p<0.01; r=0.64, p=0.048). The performance did not differ based on the issue time of the report (morning, afternoon and evening) (χ²(2)=3.65, p=0.16).

CONCLUSIONS

Greater autonomy, represented as enhanced self-directed interpretive work, was most significantly correlated with the performance of prostate MRI interpretation. The lack of a correlation between the performance and assigned volume confirms the complexity of human learning. Together, these findings support the hypothesis that successful promotion of internal drivers could have a pervasive positive impact on improving diagnostic performance.

Association Between Tumor Multifocality on Multi-parametric MRI and Detection of Clinically-Significant Prostate Cancer in Lesions with Prostate Imaging Reporting and Data System (PI-RADS) Score 4

OBJECTIVE

To investigate whether presence of multifocality on multi-parametric magnetic resonance imaging would increase the likelihood of detecting clinically-significant prostate cancer in a PI-RADS 4 lesion.

METHODS

We identified patients with at least 1 PI-RADS 4 lesion who underwent multi-parametric magnetic resonance imaging-ultrasound fusion prostate biopsy. Patients were grouped into 1 of 4 cohorts-cohort 1 (a PI-RADS 4 index lesion and an additional PI-RADS 2 or 3 lesion), cohort 2 (single lesion with PI-RADS 4), cohort 3 (2 or more PI-RADS 4 lesions), or cohort 4 (a PI-RADS 4 lesion and an index lesion with PI-RADS 5). We compared the rate of grade group (GG) ≥ 2 pathology on targeted biopsy of PI-RADS 4 lesions between cohorts and evaluated clinical and radiological factors associated with cancer detection.

RESULTS

The overall rate of GG ≥ 2 pathology in the PI-RADS 4 lesions was 35.2%. The rate of GG ≥ 2 pathology in the cohorts 1, 2, 3, and 4 was 21.7%, 36.3%, 49.1%, and 42.7%, respectively (P< .001). On multivariable analysis, age (OR1.06, P < .001), clinical stage T2 (OR1.59, P= .03), prostate-specific antigen density (OR1.43, P < .001), peripheral zone lesion (OR1.62, P = .04), and study cohort (cohort 2 vs 1, OR1.93, P = .006; and cohort 3 vs 1, OR3.28, P < .001) were significantly associated with the risk of GG ≥ 2 in the PI-RADS 4 lesion.

CONCLUSION

On targeted biopsy of the PI-RADS 4 lesions, the proportion of GG ≥ 2 pathology is approximately 35%. Rate of GG ≥ 2 detection in PI-RADS 4 lesions might differ based on their location, multifocality, and PI-RADS classifications of other lesions identified.

Prevalence and clinical significance of incidental findings on multiparametric prostate MRI

PURPOSE

To assess the prevalence and clinical significance of incidental findings (IFs) detected at multiparametric prostate MRI examination.

MATERIALS AND METHODS

Multiparametric prostate MRIs of 647 consecutive patients (mean age 67.1 ± 8.0 years) were retrospectively evaluated by two radiologists recording the presence of all extra-prostatic IFs. Findings were classified as related to or not related to genitourinary system and divided into three classes, according to their clinical significance, as follows: group 1, not significant or scarcely significant; group 2, moderately or potentially significant; and group 3, significant. Differences in distribution of IFs between patients ≤ 65 years old and patients > 65 years old were assessed using Pearson's χ² or Fisher's exact test. Statistical significance level was set at p < 0.05.

RESULTS

Incidental findings (n = 461) were present in 341 (52.7%) patients, while 306 (47.3%) patients did not have any extra-prostatic IF. Overall, IFs were significantly more common in patients > 65 years old (n = 225, 57.0%) compared to patients ≤ 65 years old (n = 116, 46.0%, p = 0.007). There were 139 (30.2%) IFs related to genitourinary system and 322 (69.8%) IFs not related to genitourinary system. Group 3 IFs were almost exclusively present in patients > 65 years old (2.8%, p = 0.034) and included 7 (1.1%) bladder carcinomas, 3 (0.5%) testicle tumors, 2 (0.3%) rectal cancers. Twenty-seven (4.2%) of the 647 patients underwent surgical treatment for IFs not directly related to prostate cancer.

CONCLUSION

IFs not related to prostate cancer may be frequently encountered on multiparametric prostate MRI, and they are significantly more common in patients > 65 years old.

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

Objectives

To create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa).

Methods

This study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3–5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets.

Results

The best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980–0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920–0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution.

Conclusions

The phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa.

Key Points

• T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance.

• Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest.

• The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06488-y) contains supplementary material, which is available to authorized users.

Transperineal prostate biopsy with cognitive magnetic resonance imaging/biplanar ultrasound fusion: description of technique and early results

OBJECTIVE

To describe our technique and early results performing transperineal prostate biopsy using cognitive magnetic resonance imaging (MRI)/biplanar ultrasound fusion. Key components of this technique include use of the PrecisionPoint Transperineal Access System (Perineologic, Cumberland, MD) and simultaneous transrectal ultrasound guidance in the axial and sagittal planes.

PATIENTS AND METHODS

In total, 95 patients (38 studied retrospectively and 57 studied prospectively) underwent a transperineal MRI-targeted prostate biopsy using the technique detailed in this manuscript. All biopsies were performed by a single urologist (MAG). Data were collected with respect to cancer detection rates, tolerability, and complications. The subset of patients who were studied prospectively was assessed for complications by telephone interviews performed at 4-6 days and 25-31 days following the prostate biopsy.

RESULTS

Between February 2018 and June 2019, 95 men underwent a transperineal prostate biopsy using MRI/biplanar ultrasound fusion guidance. Patients had a total of 124 PI-RADS 3-5 lesions that were targeted for biopsy. In total, 108 (87.1%) lesions were found to harbor prostate cancer of any grade. Grade group ≥ 2 prostate cancer was found in 81 (65.3%) of targeted lesions. The detection rates for grade group ≥ 1 and grade group ≥ 2 prostate cancer rose with increasing PI-RADS score. In 65 (68.4%) cases, the patient's highest grade prostate cancer was found within an MRI target. Additionally, 12 of 55 (21.8%) patients who were found to have no or grade group 1 prostate cancer on systematic biopsy were upgraded to grade group ≥ 2 prostate cancer with MRI targeting. Only 1 (1.1%) patient received periprocedural antibiotics and no patient experienced an infectious complication. Self-limited hematuria and hematospermia were commonly reported following the procedure (75.4% and 40.4%, respectively) and only 1 (1.1%) patient developed urinary retention.

CONCLUSIONS

We demonstrate the safety and feasibility of performing transperineal prostate biopsy using cognitive MRI/biplanar ultrasound fusion guidance. The described technique affords the safety benefits of the transperineal approach as well as obviates the need for a formal fusion platform. Additionally, this method can conveniently be performed under local anesthesia with acceptable tolerability.

Can the addition of clinical information improve the accuracy of PI-RADS version 2 for the diagnosis of clinically significant prostate cancer in positive MRI?

AIM

To report prostate cancer (PCa) prevalence in Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) categories and investigate the potential to avoid unnecessary, magnetic resonance imaging (MRI)-guided in-bore biopsies by adding clinical and biochemical patient characteristics.

MATERIALS AND METHODS

The present institutional review board-approved, prospective study on 137 consecutive men with 178 suspicious lesions on 3 T MRI was performed. Routine data collected for each patient included patient characteristics (age, prostate volume), clinical background information (prostate-specific antigen [PSA] levels, PSA density), and PI-RADS v2 scores assigned in a double-reading approach.

RESULTS

Histopathological evaluation revealed a total of 93/178 PCa (52.2%). The mean age was 66.3 years and PSA density was 0.24 ng/ml² (range, 0.04-0.89 ng/ml). Clinically significant PCa (csPCa, Gleason score >6) was confirmed in 50/93 (53.8%) lesions and was significantly associated with higher PI-RADS v2 scores (p=0.0044). On logistic regression analyses, age, PSA density, and PI-RADS v2 scores contributed independently to the diagnosis of csPCa (p=7.9×10^-7, p=0.097, and p=0.024, respectively). The resulting area under the receiver operating characteristic curve (AUC) to predict csPCa was 0.76 for PI-RADS v2, 0.59 for age, and 0.67 for PSA density. The combined regression model yielded an AUC of 0.84 for the diagnosis of csPCa and was significantly superior to each single parameter (p≤0.0009, respectively). Unnecessary biopsies could have been avoided in 50% (64/128) while only 4% (2/50) of csPCa lesions would have been missed.

CONCLUSIONS

Adding age and PSA density to PI-RADS v2 scores improves the diagnostic accuracy for csPCa. A combination of these variables with PI-RADS v2 can help to avoid unnecessary in-bore biopsies while still detecting the majority of csPCa.

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.

Similarities and differences between Likert and PIRADS v2.1 scores of prostate multiparametric MRI: a pictorial review of histology-validated cases

The UK National Institute for Health and Care Excellence (NICE) 2019 "Prostate cancer: diagnosis and management" guidelines have recommended that all patients suspected of prostate cancer undergo multiparametric magnetic resonance imaging (mpMRI) prior to biopsy. The Likert scoring system is advocated for mpMRI reporting based on multicentre studies that have demonstrated its effectiveness within the National Health Service (NHS). In recent years, there has been considerable drive towards standardised prostate reporting, which led to the development of "Prostate Imaging-Reporting And Data System" (PI-RADS). The PI-RADS system has been adopted by the majority of European countries and within the US. This paper reviews these systems indicating the similarities and specific differences that exist between PI-RADS and Likert assessment through a series of histologically proven clinical cases.

PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer

PURPOSE

To determine the diagnostic performance of texture analysis of prostate MRI for the diagnosis of prostate cancer among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions.

MATERIALS AND METHODS

Forty-three patients with at least 1 PI-RADS 3 lesion on prostate MRI performed between June 2016 and January 2019 were retrospectively included. Reference standard was pathological analysis of radical prostatectomy specimens or MRI-targeted biopsies. Texture analysis extraction of target lesions was performed on axial T2-weighted images and apparent diffusion coefficient (ADC) maps using a radiomic software. Lesions were categorized as prostate cancer (Gleason score [GS] ≥ 6), and no prostate cancer. Statistical analysis was performed using the generalized linear model (GLM) regression and the discriminant analysis (DA). AUROC with 95% confidence intervals were calculated to assess the diagnostic performance of standalone features and predictive models for the diagnosis of prostate cancer (GS ≥ 6) and clinically-significant prostate cancer (GS ≥ 7).

RESULTS

The analysis of 46 PI-RADS 3 lesions (ie, 27 [58.7%] no prostate cancers; 19 [41.3%] prostate cancers) revealed 9 and 6 independent texture parameters significantly correlated with the final histopathological results on T2-weighted and ADC maps images, respectively. The resulting GLM and DA predictive models for the diagnosis of prostate cancer yielded an AUROC of 0.775 and 0.779 on T2-weighted images or 0.815 and 0.821 on ADC maps images. For the diagnosis of clinically-significant prostate cancer, the resulting GLM and DA predictive models for the diagnosis of prostate cancer yielded an AUROC of 0.769 and 0.817 on T2-weighted images or 0.749 and 0.744 on ADC maps images.

CONCLUSION

Texture analysis of PI-RADS 3 lesions on T2-weighted and ADC maps images helps identifying prostate cancer. The good diagnostic performance of the combination of multiple radiomic features for the diagnosis of prostate cancer may help predicting lesions where aggressive management may be warranted.

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images with cancerous locations identified by radiologists and pathologists. First, 307 prostate MR images were classified using a well-established deep neural network without locational information of cancers. Subsequently, we assessed whether the deep learning-focused regions overlapped the radiologist-identified targets. Furthermore, pathologists provided histopathological diagnoses on 896 pathological images, and we compared the deep learning-focused regions with the genuine cancer locations through 3D reconstruction of pathological images. The area under the curve (AUC) for MR images classification was sufficiently high (AUC = 0.90, 95% confidence interval 0.87-0.94). Deep learning-focused regions overlapped radiologist-identified targets by 70.5% and pathologist-identified cancer locations by 72.1%. Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning. Deep learning algorithms can achieve highly accurate image classification without necessarily identifying radiological targets or cancer locations. Deep learning may find clues that can help a clinical diagnosis even if the cancer is not visible.

Comparison of PI-RADS version 2 and PI-RADS version 2.1 for the detection of transition zone prostate cancer

PURPOSE

To compare the diagnostic performance of PI-RADS v2 and v2.1 for detecting transition zone prostate cancer (TZPC) on multiparametric prostate MRI (mpMRI).

METHOD

Fifty-eight patients with elevated PSA levels underwent mpMRI at 3 T including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), and subsequent MRI-transrectal ultrasonography fusion-guided prostate-targeted biopsy (MRGB). The standard of reference was MRGB-derived histopathology. Two readers independently assessed each TZ lesion, assigning a score of 1-5 for T2WI, a score of 1-5 for DWI, and the overall PI-RADS assessment category according to PI-RADS v2 and v2.1. The diagnostic performance of the two methods was compared in terms of inter-reader agreement, diagnostic sensitivity, diagnostic specificity, and area under the ROC curve (AUC).

RESULTS

Of the 58 patients, 26 were diagnosed with PC (GS = 3 + 3, n = 9; GS = 3 + 4, n = 9; GS = 3 + 5, n = 1; GS = 4 + 3, n = 4; GS = 4 + 4, n = 3) and 32 with benign lesions. Regarding inter-reader agreement of overall PI-RADS assessment category, the kappa value was 0.580 for v2 and 0.645 for v2.1. For both readers, there was no difference in diagnostic sensitivity between the versions (p ≥ 0.500). For reader 1, the diagnostic specificity was higher for v2.1 (p = 0.002), and was similar for reader 2 (p = 1.000). For both readers, AUC tended to be higher for v2.1 than for v2, but the difference was not significant (0.786 vs. 0.847 for reader 1, p = 0.052; and 0.808 vs. 0.858 for reader 2, p = 0.197).

CONCLUSIONS

These results suggest that compared with PI-RADS v2, PI-RADS v2.1 could be preferable for evaluating TZ lesions.

MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology

Personalized medicine aims at offering optimized treatment options and improved survival for cancer patients based on individual variability. The success of precision medicine depends on robust biomarkers. Recently, the requirement for improved non-biologic biomarkers that reflect tumor biology has emerged and there has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics. Radiomics as a methodological approach can be applied to any image and most studies have focused on PET, CT, ultrasound, and MRI. Here, we aim to present an overview of the radiomics workflow as well as the major challenges with special emphasis on the use of multiparametric MRI datasets. We then reviewed recent studies on radiomics in the field of pelvic oncology including prostate, cervical, and colorectal cancer.

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment

Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose To compare the performance of clinical assessment to a deep learning system optimized for segmentation trained with T2-weighted and diffusion MRI in the task of detection and segmentation of lesions suspicious for sPC. Materials and Methods In this retrospective study, T2-weighted and diffusion prostate MRI sequences from consecutive men examined with a single 3.0-T MRI system between 2015 and 2016 were manually segmented. Ground truth was provided by combined targeted and extended systematic MRI-transrectal US fusion biopsy, with sPC defined as International Society of Urological Pathology Gleason grade group greater than or equal to 2. By using split-sample validation, U-Net was internally validated on the training set (80% of the data) through cross validation and subsequently externally validated on the test set (20% of the data). U-Net-derived sPC probability maps were calibrated by matching sextant-based cross-validation performance to clinical performance of Prostate Imaging Reporting and Data System (PI-RADS). Performance of PI-RADS and U-Net were compared by using sensitivities, specificities, predictive values, and Dice coefficient. Results A total of 312 men (median age, 64 years; interquartile range [IQR], 58-71 years) were evaluated. The training set consisted of 250 men (median age, 64 years; IQR, 58-71 years) and the test set of 62 men (median age, 64 years; IQR, 60-69 years). In the test set, PI-RADS cutoffs greater than or equal to 3 versus cutoffs greater than or equal to 4 on a per-patient basis had sensitivity of 96% (25 of 26) versus 88% (23 of 26) at specificity of 22% (eight of 36) versus 50% (18 of 36). U-Net at probability thresholds of greater than or equal to 0.22 versus greater than or equal to 0.33 had sensitivity of 96% (25 of 26) versus 92% (24 of 26) (both P > .99) with specificity of 31% (11 of 36) versus 47% (17 of 36) (both P > .99), not statistically different from PI-RADS. Dice coefficients were 0.89 for prostate and 0.35 for MRI lesion segmentation. In the test set, coincidence of PI-RADS greater than or equal to 4 with U-Net lesions improved the positive predictive value from 48% (28 of 58) to 67% (24 of 36) for U-Net probability thresholds greater than or equal to 0.33 (P = .01), while the negative predictive value remained unchanged (83% [25 of 30] vs 83% [43 of 52]; P > .99). Conclusion U-Net trained with T2-weighted and diffusion MRI achieves similar performance to clinical Prostate Imaging Reporting and Data System assessment. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Padhani and Turkbey in this issue.

Prostate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine-Learning Techniques

BACKGROUND

Accurate risk stratification of men with a clinical suspicion of prostate cancer (cSPCa) remains challenging despite the increasing use of MRI.

PURPOSE

To evaluate the diagnostic accuracy of a unique biparametric MRI protocol (IMPROD bpMRI) combined with clinical and molecular markers in men with cSPCa.

STUDY TYPE

Prospective single-institutional clinical trial (NCT01864135).

SUBJECTS

Eighty men with cSPCa.

FIELD STRENGTH/SEQUENCE

3T, surface array coils. Two T₂ -weighted and three diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm² ; 2) b-values 0,1500 s/mm² ; 3) b-values 0, 2000 s/mm² .

ASSESSMENT

IMPROD bpMRI examinations were qualitatively (IMPROD bpMRI Likert score) and quantitatively (DWI-based Gleason grade score) prospectively reported. Men with IMPROD bpMRI Likert 3-5 had two targeted biopsies followed by 12-core systematic biopsies (SB); those with IMPROD bpMRI Likert 1-2 had only SB. Additionally, 2-core from normal-appearing prostate areas were obtained for the mRNA expression of ACSM1, AMACR, CACNA1D, DLX1, PCA3, PLA2G7, RHOU, SPINK1, SPON2, TMPRSS2-ERG, and TDRD1 measured by quantitative reverse-transcription polymerase chain reaction.

STATISTICAL TESTS

Univariate and multivariate analysis using regularized least-squares, feature selection and tournament leave-pair-out cross-validation (TLPOCV), as well as 10 random splits of the data in training-testing sets, were used to evaluate the mRNA, clinical and IMPROD bpMRI parameters in detecting clinically significant prostate cancer (SPCa) defined as Gleason score ≥ 3 + 4. The evaluation metric was the area under the curve (AUC).

RESULTS

IMPROD bpMRI Likert demonstrated the highest TLPOCV AUC of 0.92. The tested clinical variables had AUC 0.56-0.73, while the mRNA and additional IMPROD bpMRI parameters had AUC 0.50-0.67 and 0.65-0.89 respectively. The combination of clinical and mRNA biomarkers produced TLPOCV AUC of 0.87, the highest TLPOCV performance without including IMPROD bpMRI Likert.

DATA CONCLUSION

The qualitative IMPROD bpMRI Likert score demonstrated the highest accuracy for SPCa detection compared with the tested clinical variables and mRNA biomarkers.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1540-1553.

Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score

BACKGROUND

Biochemical recurrence (BCR) affects a significant proportion of patients who undergo robotic-assisted laparoscopic prostatectomy (RALP).

PURPOSE

To evaluate the performance of a routine clinical prostate multiparametric magnetic resonance imaging (mpMRI) and Decipher genomic classifier score for prediction of biochemical recurrence in patients who underwent RALP.

STUDY TYPE

Retrospective cohort study.

SUBJECTS

Ninety-one patients who underwent RALP performed by a single surgeon, had mpMRI before RALP, Decipher taken from RALP samples, and prostate specific antigen (PSA) follow-up for >3 years or BCR within 3 years, defined as PSA >0.2 mg/ml. FIELD STRENGTH/SEQUENCE: mpMRI was performed at 27 different institutions using 1.5T (n = 10) or 3T scanners and included T₂ w, diffusion-weighted imaging (DWI), or dynamic contrast-enhanced (DCE) MRI.

ASSESSMENT

All mpMRI studies were reported by one reader using Prostate Imaging Reporting and Data System v. 2.1 (PI-RADsv2.1) without knowledge of other findings. Eighteen (20%) randomly selected cases were re-reported by reader B to evaluate interreader variability.

STATISTICAL TESTS

Univariate and multivariate analysis using greedy feature selection and tournament leave-pair-out cross-validation (TLPOCV) were used to evaluate the performance of various variables for prediction of BCR, which included clinical (three), systematic biopsy (three), surgical (six: RALP Gleason Grade Group [GGG], extracapsular extension, seminal vesicle invasion, intraoperative surgical margins [PSM], final PSM, pTNM), Decipher (two: Decipher score, Decipher risk category), and mpMRI (eight: prostate volume, PSA density, PI-RADv2.1 score, MRI largest lesion size, summed MRI lesions' volume and relative volume [MRI-lesion-percentage], mpMRI ECE, mpMRI seminal vesicle invasion [SVI]) variables. The evaluation metric was the area under the curve (AUC).

RESULTS

Forty-eight (53%) patients developed BCR. The best-performing individual features with TLPOCV AUC of 0.73 (95% confidence interval [CI] 0.64-0.82) were RALP GGG, MRI-lesion-percentage followed by biopsy GGG (0.72, 0.62-0.82), and Decipher score (0.71, 0.60-0.82). The best performance was achieved by feature selection of Decipher+Surgery and MRI + Surgery variables with TLPOCV AUC of 0.82 and 0.81, respectively DATA CONCLUSION: Relative lesion volume measured on a routine clinical mpMRI failed to outperform Decipher score in BCR prediction.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1075-1085.

Multimodality Imaging of Prostate Cancer

Prostate cancer is a very heterogeneous disease, and contemporary management is focused on identification and treatment of the prognostically adverse high-risk tumors while minimizing overtreatment of indolent, low-risk tumors. In recent years, imaging has gained increasing importance in the detection, staging, posttreatment assessment, and detection of recurrence of prostate cancer. Several imaging modalities including conventional and functional methods are used in different clinical scenarios with their very own advantages and limitations. This continuing medical education article provides an overview of available imaging modalities currently in use for prostate cancer followed by a more specific section on the value of these different imaging modalities in distinct clinical scenarios, ranging from initial diagnosis to advanced, metastatic castration-resistant prostate cancer. In addition to established imaging indications, we will highlight some potential future applications of contemporary imaging modalities in prostate cancer.

Machine learning applications in prostate cancer magnetic resonance imaging

With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its 'black box' nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.

PI-RADS version 2: optimal time range for determining positivity of dynamic contrast-enhanced MRI in peripheral zone prostate cancer

AIM

To analyse the optimal time cut-off for determining positivity of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in peripheral zone (PZ) prostate cancer (PCa).

MATERIALS AND METHODS

A consecutive series of 89 patients with PZ PCa who had undergone diffusion-weighted imaging (DWI) and subtraction DCE MRI were included. An experienced reader visually analysed the earliest time after contrast medium injection to visualise the best contrast between an index tumour and normal PZ on DCE MRI (i.e., best contrast time). The best contrast time cut-off for clinically significant cancer (csPCa) according to Epstein criteria or International Society of Urological Pathology (ISUP) grade ≥2 was analysed by an experienced reader, and applied to a less-experienced reader. For the index lesion of DWI category 3, the added value of DCE MRI (increased true positive and negative rates of PI-RADSv2 for csPCa) was evaluated using the cut-off time.

RESULTS

The best contrast time cut-off for csPCa was ≤72 seconds for Epstein criteria and ≤56 seconds for ISUP grade ≥2 by an experienced reader. The weighted kappa to determine positivity of DCE MRI was 0.622 for ≤72 seconds and 0.527 for ≤56 seconds between the two readers. The added value of DCE MRI was 55-75% by an experienced reader and 39.1-69.6% by a less-experienced reader.

CONCLUSION

For interpreting PI-RADSv2, imaging findings within 60-72 seconds following contrast media injection seem to reliably determine positivity of DCE MRI in PZ, and have added value for detecting csPCa.

Three-year experience of a dedicated prostate mpMRI pre-biopsy programme and effect on timed cancer diagnostic pathways

AIM

To evaluate the effect of pre-biopsy magnetic resonance imaging (MRI) on cancer diagnostic times, and to report MRI-directed pathology outcomes.

MATERIALS AND METHODS

In total, 1483 patients were referred with prostate cancer suspicion during a 30-month period. Upfront MRI was performed in 745 patients: 332 MRIs in the 15 months prior to dedicated scanning slots (group 1), and 413 in the 15 months post-introduction (group 2). A further 88 patients had initial MRI following clinical assessment. Biopsy via the transrectal (TR) or transperineal (TP) approach was performed, with MRI/ultrasound fusion for MRI targets. Clinically significant cancer (csPCa) was defined as Gleason ≥3+4. Negative MRIs were defined as Likert 1-2. Per-case clinical decisions were taken to biopsy or not.

RESULTS

44.4% of patients avoided biopsy. 484/833 (58.1%) MRIs were negative; 37.4% of these patients had biopsy with a negative predictive value (NPV) of 92.8% for Gleason ≥3+4 and 98.3% for ≥4+3. Overall prostate cancer prevalence was 34.3% (24.6% csPCa). In 323 MRI-positive cases, any cancer was present in 78.9% (csPCa 60.4%). Of the 1483 patients, 1232 (83.1%) completed all diagnostic tests within 28 days. Upfront MRI patients met this standard in 621/833 (74.5%), improving from 66.9% to 81.1% with reserved slots (group 2) with a reduced diagnostic time from median 25.5 to 20.9 days. Biopsy scheduling delayed the pathway in 69.7%, with MRI responsible in 22.3%, reducing to 10.3% in group 2. TP biopsies met the 28-day standard in significantly less cases (29.7%), compared to TR (67.4%, p<0.0001).

CONCLUSION

Reserved MRI slots reduces time-to-diagnosis, and upfront MRI safely avoids biopsy in a significant proportion of men, whilst maintaining expected csPCa detection rates.

PI-RADS version 2.1: one small step for prostate MRI

Multiparametric (mp) prostate magnetic resonance imaging (MRI) is playing an increasingly prominent role in the diagnostic work-up of patients with suspected prostate cancer. Performing mpMRI before biopsy offers several advantages including biopsy avoidance under certain clinical circumstances and targeting biopsy of suspicious lesions to enable the correct diagnosis. The success of the technique is heavily dependent on high-quality image acquisition, interpretation, and report communication, all areas addressed by previous versions of the Prostate Imaging-Reporting and Data System (PI-RADS) recommendations. Numerous studies have validated the approach, but the widespread adoption of PI-RADS version 2 has also highlighted inconsistencies and limitations, particularly relating to interobserver variability for evaluation of the transition zone. These limitations are addressed in the recently released version 2.1. In this article, we highlight the key changes proposed in PI-RADS v2.1 and explore the background reasoning and evidence for the recommendations.

PI-RADS Steering Committee: The PI-RADS Multiparametric MRI and MRI-directed Biopsy Pathway

High-quality evidence shows that MRI in biopsy-naive men can reduce the number of men who need prostate biopsy and can reduce the number of diagnoses of clinically insignificant cancers that are unlikely to cause harm. In men with prior negative biopsy results who remain under persistent suspicion, MRI improves the detection and localization of life-threatening prostate cancer with greater clinical utility than the current standard of care, systematic transrectal US-guided biopsy. Systematic analyses show that MRI-directed biopsy increases the effectiveness of the prostate cancer diagnosis pathway. The incorporation of MRI-directed pathways into clinical care guidelines in prostate cancer detection has begun. The widespread adoption of the Prostate Imaging Reporting and Data System (PI-RADS) for multiparametric MRI data acquisition, interpretation, and reporting has promoted these changes in practice. The PI-RADS MRI-directed biopsy pathway enables the delivery of key diagnostic benefits to men suspected of having cancer based on clinical suspicion. Herein, the PI-RADS Steering Committee discusses how the MRI pathway should be incorporated into routine clinical practice and the challenges in delivering the positive health impacts needed by men suspected of having clinically significant prostate cancer.

Validation of IMPROD biparametric MRI in men with clinically suspected prostate cancer: A prospective multi-institutional trial

BACKGROUND

Magnetic resonance imaging (MRI) combined with targeted biopsy (TB) is increasingly used in men with clinically suspected prostate cancer (PCa), but the long acquisition times, high costs, and inter-center/reader variability of routine multiparametric prostate MRI limit its wider adoption.

METHODS AND FINDINGS

The aim was to validate a previously developed unique MRI acquisition and reporting protocol, IMPROD biparametric MRI (bpMRI) (NCT01864135), in men with a clinical suspicion of PCa in a multi-institutional trial (NCT02241122). IMPROD bpMRI has average acquisition time of 15 minutes (no endorectal coil, no intravenous contrast use) and consists of T2-weighted imaging and 3 separate diffusion-weighed imaging acquisitions. Between February 1, 2015, and March 31, 2017, 364 men with a clinical suspicion of PCa were enrolled at 4 institutions in Finland. Men with an equivocal to high suspicion (IMPROD bpMRI Likert score 3-5) of PCa had 2 TBs of up to 2 lesions followed by a systematic biopsy (SB). Men with a low to very low suspicion (IMPROD bpMRI Likert score 1-2) had only SB. All data and protocols are freely available. The primary outcome of the trial was diagnostic accuracy-including overall accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value-of IMPROD bpMRI for clinically significant PCa (SPCa), which was defined as a Gleason score ≥ 3 + 4 (Gleason grade group 2 or higher). In total, 338 (338/364, 93%) prospectively enrolled men completed the trial. The accuracy and NPV of IMPROD bpMRI for SPCa were 70% (113/161) and 95% (71/75) (95% CI 87%-98%), respectively. Restricting the biopsy to men with equivocal to highly suspicious IMPROD bpMRI findings would have resulted in a 22% (75/338) reduction in the number of men undergoing biopsy while missing 4 (3%, 4/146) men with SPCa. The main limitation is uncertainty about the true PCa prevalence in the study cohort, since some of the men may have PCa despite having negative biopsy findings.

CONCLUSIONS

IMPROD bpMRI demonstrated a high NPV for SPCa in men with a clinical suspicion of PCa in this prospective multi-institutional clinical trial.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02241122.

Supervised risk predictor of central gland lesions in prostate cancer using 1 H MR spectroscopic imaging with gradient offset-independent adiabaticity pulses

BACKGROUND

Due to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge.

PURPOSE

To evaluate the efficacy of in vivo 3D ¹ H MR spectroscopic imaging (3D ¹ H MRSI) with a semi-localized adiabatic selective refocusing (sLASER) sequence and gradient-modulated offset-independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low- vs. high-risk cancer, 3) low- vs. intermediate-risk cancer, and 4) intermediate- vs. high-risk cancer voxels.

STUDY TYPE

Prospective.

SUBJECTS

Thirty-six patients with biopsy-proven central gland prostate cancer.

FIELD STRENGTH/SEQUENCE

3T MRI / 3D ¹ H MRSI using GOIA-sLASER.

ASSESSMENT

Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and ¹ H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing.

STATISTICAL TESTS

Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset.

RESULTS

High-quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low- vs. high-risk cancer and low- vs. intermediate-risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate- vs. high-risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%.

DATA CONCLUSION

The GOIA-sLASER sequence with an external phased-array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low- vs. high-risk cancer, and low- vs. intermediate-risk cancer.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1926-1936.

[Predictive biomarkers in oncologic uropathology]

BACKGROUND

Tumors of the genitourinary system are common. In recent years, our understanding of their molecular background and therefore the number of potential predictive biomarkers has massively increased.

OBJECTIVES

The aim of the current work is to give an overview of recent (molecular) developments and predictive biomarkers in urologic oncology and to give a perspective of what might become relevant in the future of the field.

MATERIAL AND METHODS

We considered the recent literature and study data and combined it with our own expertise in tumors of the urinary system, kidneys, and prostate.

RESULTS AND CONCLUSIONS

The molecular subtypes of muscle-invasive urothelial bladder cancer (MIBC) hold a predictive and prognostic significance and correlate with clinicopathological features. Immune therapy with checkpoint inhibitors (CPI) has a major role in urothelial carcinoma (UC), but also in renal cell carcinoma and a subgroup of prostate cancers. The first-line use in UC is restricted to PD-L1-"positive" cases (≥IC2/3, CPS ≥ 10). Further predictive markers are currently under evaluation, while the predictive significance of tumor mutational burden (TMB) is under debate. In addition to a subgroup of renal cell carcinomas, a subgroup of prostate carcinomas with alterations in the DNA repair system might benefit from a customized therapy approach (PARP inhibitors, platin-containing chemotherapy). The multitude of potentially therapy-relevant molecular alterations and related predictive biomarkers calls for the implementation of sophisticated molecular analyses in daily routine. This will lead to an even more rapid dynamic in the field of genitourinary pathology.