Differences in Gut Microbiota Profiles and Microbiota Steroid Hormone Biosynthesis in Men with and Without Prostate Cancer.

Background: Although prostate cancer (PCa) is the most common cancer in men in Western countries, there is significant variability in geographical incidence. This might result from genetic factors, discrepancies in screening policies, or differences in lifestyle. Gut microbiota has recently been associated with cancer progression, but its role in PCa is unclear. Objective: Characterization of the gut microbiota and its functions associated with PCa. Design setting and participants: In a prospective multicenter clinical trial (NCT02241122), the gut microbiota profiles of 181 men with a clinical suspicion of PCa were assessed utilizing 16S rRNA sequencing. Outcome measurements and statistical analysis: Sequences were assigned to operational taxonomic units, differential abundance analysis, and α- and ß-diversities, and predictive functional analyses were performed. Plasma steroid hormone levels corresponding to the predicted microbiota steroid hormone biosynthesis profiles were investigated. Results and limitations: Of 364 patients, 181 were analyzed, 60% of whom were diagnosed with PCa. Microbiota composition and diversity were significantly different in PCa, partially affected by Prevotella 9, the most abundant genus of the cohort, and significantly higher in PCa patients. Predictive functional analyses revealed higher 5-α-reductase, copper absorption, and retinol metabolism in the PCa-associated microbiome. Plasma testosterone was associated negatively with the predicted microbial 5-α-reductase level. Conclusions: Gut microbiota of the PCa patients differed significantly compared with benign individuals. Microbial 5-α-reductase, copper absorption, and retinol metabolism are potential mechanisms of action. These findings support the observed association of lifestyle, geography, and PCa incidence. Patient summary: In this report, we found that several microbes and potential functions of the gut microbiota are altered in prostate cancer compared with benign cases. These findings suggest that gut microbiota could be the link between environmental factors and prostate cancer.

Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors.

PURPOSE: The primary aim of this study was to evaluate if exposure to 5-alpha-reductase inhibitors (5-ARIs) modifies the effect of MRI for the diagnosis of clinically significant Prostate Cancer (csPCa) (ISUP Gleason grade ≥ 2). METHODS: This study is a multicenter cohort study including patients undergoing prostate biopsy and MRI at 24 institutions between 2013 and 2022. Multivariable analysis predicting csPCa with an interaction term between 5-ARIs and PIRADS score was performed. Sensitivity, specificity, and negative (NPV) and positive (PPV) predictive values of MRI were compared in treated and untreated patients. RESULTS: 705 patients (9%) were treated with 5-ARIs [median age 69 years, Interquartile range (IQR): 65, 73; median PSA 6.3 ng/ml, IQR 4.0, 9.0; median prostate volume 53 ml, IQR 40, 72] and 6913 were 5-ARIs naïve (age 66 years, IQR 60, 71; PSA 6.5 ng/ml, IQR 4.8, 9.0; prostate volume 50 ml, IQR 37, 65). MRI showed PIRADS 1-2, 3, 4, and 5 lesions in 141 (20%), 158 (22%), 258 (37%), and 148 (21%) patients treated with 5-ARIs, and 878 (13%), 1764 (25%), 2948 (43%), and 1323 (19%) of untreated patients (p < 0.0001). No difference was found in csPCa detection rates, but diagnosis of high-grade PCa (ISUP GG ≥ 3) was higher in treated patients (23% vs 19%, p = 0.013). We did not find any evidence of interaction between PIRADS score and 5-ARIs exposure in predicting csPCa. Sensitivity, specificity, PPV, and NPV of PIRADS ≥ 3 were 94%, 29%, 46%, and 88% in treated patients and 96%, 18%, 43%, and 88% in untreated patients, respectively. CONCLUSIONS: Exposure to 5-ARIs does not affect the association of PIRADS score with csPCa. Higher rates of high-grade PCa were detected in treated patients, but most were clearly visible on MRI as PIRADS 4 and 5 lesions. TRIAL REGISTRATION: The present study was registered at ClinicalTrials.gov number: NCT05078359.

Discrepancies between Radiology Specialists and Residents in Fracture Detection from Musculoskeletal Radiographs.

(1) Background: The aim of this study was to compare the competence in appendicular trauma radiograph image interpretation between radiology specialists and residents. (2) Methods: In this multicenter retrospective cohort study, we collected radiology reports from radiology specialists (N = 506) and residents (N = 500) during 2018-2021. As a reference standard, we used the consensus of two subspecialty-level musculoskeletal (MSK) radiologists, who reviewed all original reports. (3) Results: A total of 1006 radiograph reports were reviewed by the two subspecialty-level MSK radiologists. Out of the 1006 radiographs, 41% were abnormal. In total, 67 radiographic findings were missed (6.7%) and 31 findings were overcalled (3.1%) in the original reports. Sensitivity, specificity, positive predictive value, and negative predictive value were 0.86, 0.92, 0.91 and 0.88 respectively. There were no statistically significant differences between radiology specialists' and residents' competence in interpretation (p = 0.44). However, radiology specialists reported more subtle cases than residents did (p = 0.04). There were no statistically significant differences between errors made in the morning, evening, or night shifts (p = 0.57). (4) Conclusions: This study found a lack of major discrepancies between radiology specialists and residents in radiograph interpretation, although there were differences between MSK regions and in subtle or obvious radiographic findings. In addition, missed findings found in this study often affected patient treatment. Finally, there are MSK regions where the sensitivity or specificity is below 90%, and these should raise concerns and highlight the need for double reading and should be taken into consideration in radiology education.

Prostate cancer detection and segmentation on MRI using non-local mask R-CNN with histopathological ground truth.

BACKGROUND: Automatic detection and segmentation of intraprostatic lesions (ILs) on preoperative multiparametric-magnetic resonance images (mp-MRI) can improve clinical workflow efficiency and enhance the diagnostic accuracy of prostate cancer and is an essential step in dominant intraprostatic lesion boost. PURPOSE: The goal is to improve the detection and segmentation accuracy of 3D ILs in MRI by a proposed a deep learning (DL)-based algorithm with histopathological ground truth. METHODS: This retrospective study included 262 patients with in vivo prostate biparametric MRI (bp-MRI) scans and were divided into three cohorts based on their data analysis and annotation. Histopathological ground truth was established by using histopathology images as delineation reference standard on cohort 1, which consisted of 64 patients and was randomly split into 20 training, 12 validation, and 32 testing patients. Cohort 2 consisted of 158 patients with bp-MRI based lesion delineation, and was randomly split into 104 training, 15 validation, and 39 testing patients. Cohort 3 consisted of 40 unannotated patients, used in semi-supervised learning. We proposed a non-local Mask R-CNN and boosted its performance by applying different training techniques. The performance of non-local Mask R-CNN was compared with baseline Mask R-CNN, 3D U-Net and an experienced radiologist's delineation and was evaluated by detection rate, dice similarity coefficient (DSC), sensitivity, and Hausdorff Distance (HD). RESULTS: The independent testing set consists of 32 patients with histopathological ground truth. With the training technique maximizing detection rate, the non-local Mask R-CNN achieved 80.5% and 94.7% detection rate; 0.548 and 0.604 DSC; 5.72 and 6.36 95 HD (mm); 0.613 and 0.580 sensitivity for ILs of all Gleason Grade groups (GGGs) and clinically significant ILs (GGG > 2), which outperformed baseline Mask R-CNN and 3D U-Net. For clinically significant ILs, the model segmentation accuracy was significantly higher than that of the experienced radiologist involved in the study, who achieved 0.512 DSC (p = 0.04), 8.21 (p = 0.041) 95 HD (mm), and 0.398 (p = 0.001) sensitivity. CONCLUSION: The proposed DL model achieved state-of-art performance and has the potential to help improve radiotherapy treatment planning and noninvasive prostate cancer diagnosis.

Predicting the Need for Biopsy to Detect Clinically Significant Prostate Cancer in Patients with a Magnetic Resonance Imaging-detected Prostate Imaging Reporting and Data System/Likert ≥3 Lesion: Development and Multinational External Validation of the Imperial Rapid Access to Prostate Imaging and Diagnosis Risk Score.

BACKGROUND: Although multiparametric magnetic resonance imaging (MRI) has high sensitivity, its lower specificity leads to a high prevalence of false-positive lesions requiring biopsy. OBJECTIVE: To develop and externally validate a scoring system for MRI-detected Prostate Imaging Reporting and Data System (PIRADS)/Likert ≥3 lesions containing clinically significant prostate cancer (csPCa). DESIGN, SETTING, AND PARTICIPANTS: The multicentre Rapid Access to Prostate Imaging and Diagnosis (RAPID) pathway included 1189 patients referred to urology due to elevated age-specific prostate-specific antigen (PSA) and/or abnormal digital rectal examination (DRE); April 27, 2017 to October 25, 2019. INTERVENTION: Visual-registration or image-fusion targeted and systematic transperineal biopsies for an MRI score of ≥4 or 3 + PSA density ≥0.12 ng/ml/ml. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Fourteen variables were used in multivariable logistic regression for Gleason ≥3 + 4 (primary) and Gleason ≥4 + 3, and PROMIS definition 1 (any ≥4 + 3 or ≥6 mm any grade; secondary). Nomograms were created and a decision curve analysis (DCA) was performed. Models with varying complexity were externally validated in 2374 patients from six international cohorts. RESULTS AND LIMITATIONS: The five-item Imperial RAPID risk score used age, PSA density, prior negative biopsy, prostate volume, and highest MRI score (corrected c-index for Gleason ≥3 + 4 of 0.82 and 0.80-0.86 externally). Incorporating family history, DRE, and Black ethnicity within the eight-item Imperial RAPID risk score provided similar outcomes. The DCA showed similar superiority of all models, with net benefit differences increasing in higher threshold probabilities. At 20%, 30%, and 40% of predicted Gleason ≥3 + 4 prostate cancer, the RAPID risk score was able to reduce, respectively, 11%, 21%, and 31% of biopsies against 1.8%, 6.2%, and 14% of missed csPCa (or 9.6%, 17%, and 26% of foregone biopsies, respectively). CONCLUSIONS: The Imperial RAPID risk score provides a standardised tool for the prediction of csPCa in patients with an MRI-detected PIRADS/Likert ≥3 lesion and can support the decision for prostate biopsy. PATIENT SUMMARY: In this multinational study, we developed a scoring system incorporating clinical and magnetic resonance imaging characteristics to predict which patients have prostate cancer requiring treatment and which patients can safely forego an invasive prostate biopsy. This model was validated in several other countries.

Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs.

Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional neural network algorithms in joint effusion classification in pediatric and adult elbow radiographs. This retrospective study consisted of a total of 4423 radiographs in a 3-year period from 2017 to 2020. Data was randomly separated into training (n = 2672), validation (n = 892) and test set (n = 859). Two models using VGG16 as the base architecture were trained with either only lateral projection or with four projections (AP, LAT and Obliques). Three radiologists evaluated joint effusion separately on the test set. Accuracy, precision, recall, specificity, F1 measure, Cohen's kappa, and two-sided 95% confidence intervals were calculated. Mean patient age was 34.4 years (1-98) and 47% were male patients. Trained deep learning framework showed an AUC of 0.951 (95% CI 0.946-0.955) and 0.906 (95% CI 0.89-0.91) for the lateral and four projection elbow joint images in the test set, respectively. Adult and pediatric patient groups separately showed an AUC of 0.966 and 0.924, respectively. Radiologists showed an average accuracy, sensitivity, specificity, precision, F1 score, and AUC of 92.8%, 91.7%, 93.6%, 91.07%, 91.4%, and 92.6%. There were no statistically significant differences between AUC's of the deep learning model and the radiologists (p value > 0.05). The model on the lateral dataset resulted in higher AUC compared to the model with four projection datasets. Using deep learning it is possible to achieve expert level diagnostic accuracy in elbow joint effusion classification in pediatric and adult radiographs. Deep learning used in this study can classify joint effusion in radiographs and can be used in image interpretation as an aid for radiologists.

The Mount Sinai Prebiopsy Risk Calculator for Predicting any Prostate Cancer and Clinically Significant Prostate Cancer: Development of a Risk Predictive Tool and Validation with Advanced Neural Networking, Prostate Magnetic Resonance Imaging Outcome Database, and European Randomized Study of Screening for Prostate Cancer Risk Calculator.

Background: The European Association of Urology guidelines recommend the use of imaging, biomarkers, and risk calculators in men at risk of prostate cancer. Risk predictive calculators that combine multiparametric magnetic resonance imaging with prebiopsy variables aid as an individualized decision-making tool for patients at risk of prostate cancer, and advanced neural networking increases reliability of these tools. Objective: To develop a comprehensive risk predictive online web-based tool using magnetic resonance imaging (MRI) and clinical data, to predict the risk of any prostate cancer (PCa) and clinically significant PCa (csPCa) applicable to biopsy-naïve men, men with a prior negative biopsy, men with prior positive low-grade cancer, and men with negative MRI. Design setting and participants: Institutional review board-approved prospective data of 1902 men undergoing biopsy from October 2013 to September 2021 at Mount Sinai were collected. Outcome measurements and statistical analysis: Univariable and multivariable analyses were used to evaluate clinical variables such as age, race, digital rectal examination, family history, prostate-specific antigen (PSA), biopsy status, Prostate Imaging Reporting and Data System score, and prostate volume, which emerged as predictors for any PCa and csPCa. Binary logistic regression was performed to study the probability. Validation was performed with advanced neural networking (ANN), multi-institutional European cohort (Prostate MRI Outcome Database [PROMOD]), and European Randomized Study of Screening for Prostate Cancer Risk Calculator (ERSPC RC) 3/4. Results and limitations: Overall, 2363 biopsies had complete clinical information, with 57.98% any cancer and 31.40% csPCa. The prediction model was significantly associated with both any PCa and csPCa having an area under the curve (AUC) of 81.9% including clinical data. The AUC for external validation was calculated in PROMOD, ERSPC RC, and ANN for any PCa (0.82 vs 0.70 vs 0.90) and csPCa (0.82 vs 0.78 vs 0.92), respectively. This study is limited by its retrospective design and overestimation of csPCa in the PROMOD cohort. Conclusions: The Mount Sinai Prebiopsy Risk Calculator combines PSA, imaging and clinical data to predict the risk of any PCa and csPCa for all patient settings. With accurate validation results in a large European cohort, ERSPC RC, and ANN, it exhibits its efficiency and applicability in a more generalized population. This calculator is available online in the form of a free web-based tool that can aid clinicians in better patients counseling and treatment decision-making. Patient summary: We developed the Mount Sinai Prebiopsy Risk Calculator (MSP-RC) to assess the likelihood of any prostate cancer and clinically significant disease based on a combination of clinical and imaging characteristics. MSP-RC is applicable to all patient settings and accessible online.

Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer.

BACKGROUND: Accurate detection of clinically significant prostate cancer (csPCa), Gleason Grade Group ≥ 2, remains a challenge. Prostate MRI radiomics and blood kallikreins have been proposed as tools to improve the performance of biparametric MRI (bpMRI). PURPOSE: To develop and validate radiomics and kallikrein models for the detection of csPCa. STUDY TYPE: Retrospective. POPULATION: A total of 543 men with a clinical suspicion of csPCa, 411 (76%, 411/543) had kallikreins available and 360 (88%, 360/411) did not take 5-alpha-reductase inhibitors. Two data splits into training, validation (split 1: single center, n = 72; split 2: random 50% of pooled datasets from all four centers), and testing (split 1: 4 centers, n = 288; split 2: remaining 50%) were evaluated. FIELD STRENGTH/SEQUENCE: A 3 T/1.5 T, TSE T2-weighted imaging, 3x SE DWI. ASSESSMENT: In total, 20,363 radiomic features calculated from manually delineated whole gland (WG) and bpMRI suspicion lesion masks were evaluated in addition to clinical parameters, prostate-specific antigen, four kallikreins, MRI-based qualitative (PI-RADSv2.1/IMPROD bpMRI Likert) scores. STATISTICAL TESTS: For the detection of csPCa, area under receiver operating curve (AUC) was calculated using the DeLong's method. A multivariate analysis was conducted to determine the predictive power of combining variables. The values of P-value < 0.05 were considered significant. RESULTS: The highest prediction performance was achieved by IMPROD bpMRI Likert and PI-RADSv2.1 score with AUC = 0.85 and 0.85 in split 1, 0.85 and 0.83 in split 2, respectively. bpMRI WG and/or kallikreins demonstrated AUCs ranging from 0.62 to 0.73 in split 1 and from 0.68 to 0.76 in split 2. AUC of bpMRI lesion-derived radiomics model was not statistically different to IMPROD bpMRI Likert score (split 1: AUC = 0.83, P-value = 0.306; split 2: AUC = 0.83, P-value = 0.488). DATA CONCLUSION: The use of radiomics and kallikreins failed to outperform PI-RADSv2.1/IMPROD bpMRI Likert and their combination did not lead to further performance gains. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

How to read biparametric MRI in men with a clinical suspicious of prostate cancer: Pictorial review for beginners with public access to imaging, clinical and histopathological database.

Prostate Magnetic Resonance Imaging (MRI) is increasingly being used in men with a clinical suspicion of prostate cancer (PCa). Performing prostate MRI without the use of an intravenous contrast (IV) agent in men with a clinical suspicion of PCa can lead to reduced MRI scan time. Enabling a large array of different medical providers (from mid-level to specialized radiologists) to evaluate and potentially report prostate MRI in men with a clinical suspicion of PCa with a high accuracy could be one way to enable wide adoption of prostate MRI in men with a clinical suspicion of PCa. The aim of this pictorial review is to provide an insight into acquisition, quality control and reporting of prostate MRI performed without IV contrast agent in men with a clinical suspicion of PCa, aimed specifically at radiologists starting reporting prostate MRI, urologists, urology/radiology residents and mid-level medical providers without experience in reporting prostate MRI. Free public access (http://petiv.utu.fi/improd/and http://petiv.utu.fi/multiimprod/) to complete datasets of 161 and 338 men is provided. The imaging datasets are accompanied by clinical, laboratory and histopathological findings. Several topics are simplified in order to provide a solid base for the development of skills needed for an unsupervised review and potential reporting of prostate MRI in men with a clinical suspicion of PCa. The current review represents the first step towards enabling a large array of different medical providers to review and report accurately prostate MRI performed without IV contrast agent in men with a clinical suspicion of PCa.

Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice.

PURPOSE: To evaluate fitting quality and repeatability of four mathematical models for diffusion weighted imaging (DWI) during tumor progression in mouse xenograft model of prostate cancer. METHODS: Human prostate cancer cells (PC-3) were implanted subcutaneously in right hind limbs of 11 immunodeficient mice. Tumor growth was followed by weekly DWI examinations using a 7T MR scanner. Additional DWI examination was performed after repositioning following the fourth DWI examination to evaluate short term repeatability. DWI was performed using 15 and 12 b-values in the ranges of 0-500 and 0-2000 s/mm2, respectively. Corrected Akaike information criteria and F-ratio were used to evaluate fitting quality of each model (mono-exponential, stretched exponential, kurtosis, and bi-exponential). RESULTS: Significant changes were observed in DWI data during the tumor growth, indicated by ADCm, ADCs, and ADCk. Similar results were obtained using low as well as high b-values. No marked changes in model preference were present between the weeks 1-4. The parameters of the mono-exponential, stretched exponential, and kurtosis models had smaller confidence interval and coefficient of repeatability values than the parameters of the bi-exponential model. CONCLUSION: Stretched exponential and kurtosis models showed better fit to DWI data than the mono-exponential model and presented with good repeatability.

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests.

Whole Brain Adiabatic T1rho and Relaxation Along a Fictitious Field Imaging in Healthy Volunteers and Patients With Multiple Sclerosis: Initial Findings.

BACKGROUND: In preclinical models of multiple sclerosis (MS), both adiabatic T1rho (T1ρadiab ) and relaxation along a fictitious field (RAFF) imaging have demonstrated potential to noninvasively characterize MS. PURPOSE: To evaluate the feasibility of whole brain T1ρadiab and RAFF imaging in healthy volunteers and patients with MS. STUDY TYPE: Single institutional clinical trial. SUBJECTS: 38 healthy volunteers (24-69 years) and 21 patients (26-59 years) with MS. Five healthy volunteers underwent a second MR examination performed within 8 days. Clinical disease severity (The Expanded Disability Status Scale [EDSS] and The Multiple Sclerosis Severity Score [MSSS]) was evaluated at baseline and 1-year follow-up (FU). FIELD STRENGTH/SEQUENCE: RAFF in second rotating frame of reference (RAFF2) was performed at 3 T using 3D-fast-field echo with magnetization preparation, RF amplitude of 11.74 µT while the corresponding value for T1ρadiab was 13.50 µT. T1 -, T2 -, and FLAIR-weighted images were acquired with reconstruction voxel size 1.0 × 1.0 × 1.0 mm3 . ASSESSMENT: The parametric maps of T1ρadiab and RAFF2 (TRAFF2 ) were calculated using a monoexponential model. Semi-automatic segmentation of MS lesions, white matter (WM), and gray matter (GM), and WM tracks was performed using T1 -, T2 -, and FLAIR-weighted images. STATISTICAL TESTS: Regression analysis was used to evaluate correlation of T1ρadiab and TRAFF2 with age and disease severity while a Friedman test followed by Wilcoxon Signed Rank test for differences between tissue types. Short-term repeatability was evaluated on voxel level. RESULTS: Both T1ρadiab and TRAFF2 demonstrated good short-term repeatability with relative differences on voxel level in the range of 6.1%-11.9%. Differences in T1ρadiab and TRAFF2 between the tissue types in MS patients were significant (P < 0.05). T1ρadiab and TRAFF2 correlated (P < 0.001) with baseline EDSS/MSSM and disease progression at FU (P < 0.001). DATA CONCLUSION: Whole brain T1ρadiab and TRAFF2 at 3 T was feasible with significant differences in T1ρadiab and TRAFF2 values between tissues types and correlation with disease severity. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Prospective comparison of 18F-PSMA-1007 PET/CT, whole-body MRI and CT in primary nodal staging of unfavourable intermediate- and high-risk prostate cancer.

PURPOSE: To prospectively compare 18F-prostate-specific membrane antigen (PSMA)-1007 positron emission tomography (PET)/CT, whole-body magnetic resonance imaging (WBMRI) including diffusion-weighted imaging (DWI) and standard computed tomography (CT), in primary nodal staging of prostate cancer (PCa). METHODS: Men with newly diagnosed unfavourable intermediate- or high-risk PCa prospectively underwent 18F-PSMA-1007 PET/CT, WBMRI with DWI and contrast-enhanced CT within a median of 8 days. Six readers (two for each modality) independently reported pelvic lymph nodes as malignant, equivocal or benign while blinded to the other imaging modalities. Sensitivity, specificity and accuracy were reported according to optimistic (equivocal lesions interpreted as benign) and pessimistic (equivocal lesions interpreted as malignant) analyses. The reference standard diagnosis was based on multidisciplinary consensus meetings where available histopathology, clinical and follow-up data were used. RESULTS: Seventy-nine patients completed all the imaging modalities, except for one case of interrupted WBMRI. Thirty-one (39%) patients had pelvic lymph node metastases, which were detected in 27/31 (87%), 14/31 (45%) and 8/31 (26%) patients by 18F-PSMA-1007 PET/CT, WBMRI with DWI and CT, respectively (optimistic analysis). In 8/31 (26%) patients, only 18F-PSMA-1007 PET/CT detected malignant lymph nodes, while the other two imaging modalities were reported as negative. At the patient level, sensitivity and specificity values for 18F-PSMA-1007 PET/CT, WBMRI with DWI and CT in optimistic analysis were 0.87 (95%CI 0.71-0.95) and 0.98 (95%CI 0.89-1.00), 0.37 (95%CI 0.22-0.55) and 0.98 (95%CI 0.89-1.00) and 0.26 (95%CI 0.14-0.43) and 1.00 (95%CI 0.93-1.00), respectively. CONCLUSION: 18F-PSMA-1007 PET/CT showed significantly greater sensitivity in nodal staging of primary PCa than did WBMRI with DWI or CT, while maintaining high specificity. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov ID: NCT03537391.

Added value of systematic biopsy in men with a clinical suspicion of prostate cancer undergoing biparametric MRI-targeted biopsy: multi-institutional external validation study.

PURPOSE: We aimed to develop and externally validate a nomogram based on MRI volumetric parameters and clinical information for deciding when SBx should be performed in addition to TBx in man with suspicious prostate MRI. MATERIALS AND METHODS: Retrospective analyses of single (IMPROD, NCT01864135) and multi-institution (MULTI-IMPROD, NCT02241122) clinical trials. All men underwent a unique rapid biparametric magnetic resonance imaging (IMPROD bpMRI) consisting of T2-weighted imaging and three separate DWI acquisitions. Men with IMPROD bpMRI Likert scores of 3-5 were included. Logistic regression models were developed using IMPROD trial (n = 122) and validated using MULTI-IMPROD trial (n = 262) data. The model's performance was evaluated in the terms of PCa detection with Gleason Grade Group 1 (clinically insignificant prostate cancer, iPCa) and > 1 (clinically significant prostate cancer, csPCa). Net benefits and decision curve analyses (DCA) were compared. Combined biopsies were used for reference. RESULTS: The developed nomogram included age, PSA, prostate volume, MRI suspicion score (IMPROD bpMRI Likert or PIRADsv2.1 score), MRI-suspicion lesion volume percentage, and lesion location. All these variables were significant predictors of csPCa in SBx in multivariable analysis. In the validation cohort (n = 262) using different nomogram cutoffs, 19-43% of men would have avoided SBx while missing 1-4% of csPCa and avoiding detection of 9-20% of iPCa. Similar performance was found for nomograms using IMPROD bpMRI Likert score or v2.1. CONCLUSIONS: The developed nomogram demonstrated potential to select men with a clinical suspicion of PCa who would benefit from performing SBx in addition to TBx. Public access to the nomogram is provided at: https://petiv.utu.fi/multiimprod/ .

Combined Use of Prostate-specific Antigen Density and Magnetic Resonance Imaging for Prostate Biopsy Decision Planning: A Retrospective Multi-institutional Study Using the Prostate Magnetic Resonance Imaging Outcome Database (PROMOD).

BACKGROUND: Previous studies suggested that prostate-specific antigen (PSA) density (PSAd) combined with magnetic resonance imaging (MRI) may help avoid unnecessary prostate biopsy (PB) with a limited risk of missing clinically significant prostate cancer (csPCa; Gleason grade group [GGG] >1). OBJECTIVE: To define optimal diagnostic strategies based on the combined use of PSAd and MRI in patients at risk of prostate cancer (PCa). DESIGN, SETTING, AND PARTICIPANTS: A retrospective analysis of the international multicenter Prostate MRI Outcome Database (PROMOD), including 2512 men having undergone PSAd and prostate MRI before PB between 2013 and 2019, was performed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Rates of avoided PB, missed GGG 1, and csPCa according to 10 strategies based on PSAd values and MRI reporting scores (Prostate Imaging Reporting and Data System [PI-RADS]/Likert/IMPROD biparametric prostate MRI Likert). Decision curve analysis (DCA) was used to statistically compare the net benefit of each strategy. Combined systematic and targeted biopsies were used for reference. RESULTS AND LIMITATIONS: According to DCA, the best strategy in biopsy-naive patients was #7 (PI-RADS/Likert 4-5 or PI-RADS/Likert 3 if PSAd >0.2), which avoided 41.2% PBs while missed 44% of GGG 1 and 10.9% of csPCa cases. From a clinical standpoint, however, strategies with a lower risk of missing csPCa included #10 (PI-RADS/Likert 4-5 or PI-RADS 3 if PSAd >0.10 or PSAd >0.2), which avoided 27% PBs while missing 24.4% GGG 1 and 4% csPCa cases, or #5 (PI-RADS/Likert 3-5 or PSAd>0.15), which avoided 14.7% PBs while missing 9.3% GGG 1 and 1.7% csPCa cases. Similar results were found in patients with a previous negative biopsy. This study is limited by its retrospective nature, and no central review of MRI and histopathological findings. CONCLUSIONS: Combined PSAd and MRI findings allows individualization of the decision to perform PB on the basis of the risk of missing PCa that both patients and clinicians are ready to accept to avoid this procedure. PATIENT SUMMARY: We compared several biopsy strategies based on a combination of prostate magnetic resonance imaging findings and prostate-specific antigen density, providing a readily available tool for each center and practicing urologist to counsel patients about their individual risk of significant prostate cancer.

A Prospective Comparison of 18F-prostate-specific Membrane Antigen-1007 Positron Emission Tomography Computed Tomography, Whole-body 1.5 T Magnetic Resonance Imaging with Diffusion-weighted Imaging, and Single-photon Emission Computed Tomography/Computed Tomography with Traditional Imaging in Primary Distant Metastasis Staging of Prostate Cancer (PROSTAGE).

BACKGROUND: Computed tomography (CT) and bone scintigraphy (BS) are the imaging modalities currently used for distant metastasis staging of prostate cancer (PCa). OBJECTIVE: To compare standard staging modalities with newer and potentially more accurate imaging modalities. DESIGN, SETTING, AND PARTICIPANTS: This prospective, single-centre trial (NCT03537391) enrolled 80 patients with newly diagnosed high-risk PCa (International Society of Urological Pathology grade group ≥3 and/or prostate-specific antigen [PSA] ≥20 and/or cT ≥ T3; March 2018-June 2019) to undergo primary metastasis staging with two standard and three advanced imaging modalities. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The participants underwent the following five imaging examinations within 2 wk of enrolment and without a prespecified sequence: BS, CT, 99mTc-hydroxymethylene diphosphonate (99mTc-HMDP) single-photon emission computed tomography (SPECT)-CT, 1.5 T whole-body magnetic resonance imaging (WBMRI) using diffusion-weighted imaging, and 18F-prostate-specific membrane antigen-1007 (18F-PSMA-1007) positron emission tomography(PET)-CT. Each modality was reviewed by two independent experts blinded to the results of the prior studies, who classified lesions as benign, equivocal, or malignant. Pessimistic and optimistic analyses were performed to resolve each equivocal diagnosis. The reference standard diagnosis was defined using all available information accrued during at least 12 mo of clinical follow-up. Patients with equivocal reference standard diagnoses underwent MRI and/or CT to search for the development of anatomical correspondence. PSMA PET-avid lesions without histopathological verification were rated to be malignant only if there was a corresponding anatomical finding suspicious for malignancy at the primary or follow-up imaging. RESULTS AND LIMITATIONS: Seventy-nine men underwent all imaging modalities except for one case of interrupted MRI. The median interval per patient between the first and the last imaging study was 8 d (interquartile range [IQR]: 6-9). The mean age was 70 yr (standard deviation: 7) and median PSA 12 ng/mL (IQR:7-23). The median follow-up was 435 d (IQR: 378-557). Metastatic disease was detected in 20 (25%) patients. The imaging modality 18F-PSMA-1007 PET-CT had superior sensitivity and highest inter-reader agreement. The area under the receiver-operating characteristic curve (AUC) values for bone metastasis detection with PSMA PET-CT were 0.90 (95% confidence interval [CI]: 0.85-0.95) and 0.91 (95% CI: 0.87-0.96) for readers 1 and 2, respectively, while the AUC values for BS, CT, SPECT-CT, and WBMRI were 0.71 (95% CI: 0.58-0.84) and 0.8 (95% CI: 0.67-0.92), 0.53 (95% CI: 0.39-0.67) and 0.66 (95% CI: 0.54-0.77), 0.77 (95% CI: 0.65-0.89) and 0.75 (95% CI: 0.62-0.88), and 0.85 (95% CI: 0.74-0.96) and 0.67 (95% CI: 0.54-0.80), respectively, for the other four pairs of readers. The imaging method 18F-PSMA-1007 PET-CT detected metastatic disease in 11/20 patients in whom standard imaging was negative and influenced clinical decision making in 14/79 (18%) patients. In 12/79 cases, false positive bone disease was reported only by PSMA PET-CT. Limitations included a nonrandomised study setting and few histopathologically validated suspicious lesions. CONCLUSIONS: Despite the risk of false positive bone lesions, 18F-PSMA-1007 PET-CT outperformed all other imaging methods studied for the detection of primary distant metastasis in high-risk PCa. PATIENT SUMMARY: In this report, we compared the diagnostic performance of conventional and advanced imaging. It was found that 18F-prostate-specific membrane antigen-1007 positron emission tomography/computed tomography (18F-PSMA-1007 PET-CT) was superior to the other imaging modalities studied for the detection of distant metastasis at the time of initial diagnosis of high-risk prostate cancer. PSMA PET-CT also appears to detect some nonmetastatic bone lesions.

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

OBJECTIVES: To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential function, ADCm. METHODS: One hundred twelve patients with prostate cancer (PCa) underwent 2 prostate MRI examinations on the same day. PCa areas were annotated using whole mount prostatectomy sections. Two U-Net-based convolutional neural networks were trained on three different ADCm b value settings for (a) slice- and (b) lesion-level detection and (c) segmentation of csPCa. Short-term test-retest repeatability was estimated using intra-class correlation coefficient (ICC(3,1)), proportionate agreement, and dice similarity coefficient (DSC). A 3-fold cross-validation was performed on training set (N = 78 patients) and evaluated for performance and repeatability on testing data (N = 34 patients). RESULTS: For the three ADCm b value settings, repeatability of mean ADCm of csPCa lesions was ICC(3,1) = 0.86-0.98. Two CNNs with U-Net-based architecture demonstrated ICC(3,1) in the range of 0.80-0.83, agreement of 66-72%, and DSC of 0.68-0.72 for slice- and lesion-level detection and segmentation of csPCa. Bland-Altman plots suggest that there is no systematic bias in agreement between inter-scan ground truth segmentation repeatability and segmentation repeatability of the networks. CONCLUSIONS: For the three ADCm b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility. KEY POINTS: • For the three ADCm b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. • The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility.

Negative Predictive Value of Biparametric Prostate Magnetic Resonance Imaging in Excluding Significant Prostate Cancer: A Pooled Data Analysis Based on Clinical Data from Four Prospective, Registered Studies.

BACKGROUND: Multiparametric prostate magnetic resonance imaging (mpMRI) can be considered the gold standard in prostate magnetic resonance imaging (MRI). Biparametric prostate MRI (bpMRI) is faster and could be a feasible alternative to mpMRI. OBJECTIVE: To determine the negative predictive value (NPV) of Improved Prostate Cancer Diagnosis (IMPROD) bpMRI as a whole and in clinical subgroups in primary diagnostics of clinically significant prostate cancer (CSPCa). DESIGN, SETTING, AND PARTICIPANTS: This is a pooled data analysis of four prospective, registered clinical trials investigating prebiopsy IMPROD bpMRI. Men with a clinical suspicion of prostate cancer (PCa) were included. INTERVENTION: Prebiopsy IMPROD bpMRI was performed, and an IMPROD bpMRI Likert scoring system was used. If suspicious lesions (IMPROD bpMRI Likert score 3-5) were visible, targeted biopsies in addition to systematic biopsies were taken. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Performance measures of IMPROD bpMRI in CSPCa diagnostics were evaluated. NPV was also evaluated in clinical subgroups. Gleason grade ≥3 + 4 in any biopsy core taken was defined as CSPCa. RESULTS AND LIMITATIONS: A total of 639 men were included in the analysis. The mean age was 64 yr, mean prostate-specific antigen level was 8.9 ng/ml, and CSPCa prevalence was 48%. NPVs of IMPROD bpMRI Likert scores 3-5 and 4-5 for CSPCa were 0.932 and 0.909, respectively, and the corresponding positive predictive values were 0.589 and 0.720. Only nine of 132 (7%) men with IMPROD bpMRI Likert score 1-2 had CSPCa and none with Gleason score >7. Thus, 132 of 639 (21%) study patients could have avoided biopsies without missing a single Gleason >7 cancer in the study biopsies. In the subgroup analysis, no clear outlier was present. The limitation is uncertainty of the true CSPCa prevalence. CONCLUSIONS: IMPROD bpMRI demonstrated a high NPV to rule out CSPCa. IMPROD bpMRI Likert score 1-2 excludes Gleason >7 PCa in the study biopsies. PATIENT SUMMARY: We investigated the feasibility of prostate magnetic resonance imaging (MRI) with the Improved Prostate Cancer Diagnosis (IMPROD) biparametric MRI (bpMRI) protocol in excluding significant prostate cancer. In this study, highly aggressive prostate cancer was excluded using the publicly available IMPROD bpMRI protocol (http://petiv.utu.fi/multiimprod/).

Impact of biparametric prebiopsy prostate magnetic resonance imaging on the diagnostics of clinically significant prostate cancer in biopsy naïve men.

Background: The objective of this study was to compare the prevalence of clinically significant prostate cancer (CSPCa) in men with biparametric prebiopsy prostate magnetic resonance imaging (MRI) and lesion-targeted biopsies (TBs) to the group of men without prebiopsy MRI in an initial biopsy session.Methods: The MRI group consists of men enrolled into four prospective clinical trials investigating a biparametric MRI (bpMRI) and TB while the non-MRI group was a retrospective cohort of men collected from an era prior to a clinical use of a prostate MRI. All men had standard biopsies (SBs). In the MRI group, men had additional TBs from potential cancer-suspicious lesions. CSPCa was defined as Gleason score ≥3 + 4 in any biopsy core taken. All the patients were prostate biopsy naïve.Results: The MRI group consists of 507 while the non-MRI group 379 men. Mean age and prostate specific antigen (PSA) level differed significantly (p < 0.05) between the groups: In the MRI group, 64 years and 7.6 ng/ml, respectively, and in the non-MRI group 68 years and 8.2 ng/ml, respectively. Significantly (p < 0.05) more CSPCa was diagnosed with initial biopsies in the MRI group (48%) compared to non-MRI group (34%). In men with no CSPCa diagnosed during the initial biopsies, significantly fewer (p < 0.05) men had upgrading re-biopsies in the MRI group (5%) than in the non-MRI group (19%) during the follow up.Conclusions: Prebiopsy bpMRI with TBs combined with SBs could lead to earlier diagnoses of CSPCa compared with men without prebiopsy prostate MRI used in initial PCa diagnostics.

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.