Prostate Magnetic Resonance Imaging Research Articles

Prostate cancer screening-current overview

The harm-to-benefit ratio of prostate cancer (PCa) screening remains controversial mainly due to the unfavorable test characteristics of prostate-specific antigen (PSA) as ascreening test. In this nonsystematic review, we present acurrent overview of the body of evidence on prostate cancer screening with afocus on the role of magnetic resonance imaging (MRI) of the prostate. Evidence generated in large randomized controlled trials showed that PSA-based screening significantly decreases cancer-specific mortality. The main obstacle in developing and implementing PCa screening strategies is the resulting overdiagnosis and as aconsequence overtreatment of indolent cancers. Opportunistic screening is characterized by an adverse benefit-to-harm ratio and should, therefore, not be recommended. The German Statutory Early Detection Program for prostate cancer, which consists of adigital rectal examination (DRE) as astand-alone screening test, is not evidence-based, neither specific nor sensitive enough and results in unnecessary diagnostics. The European Commission recently urged member states to develop population-based and organized risk-adapted PSA-based screening programs, which are currently tested in the ongoing German PROBASE trial. Finetuning of the diagnostic pathway following PSA-testing seems key to improve its positive and negative predictive value and thereby making PCa screening more accurate. Incorporation of prostatic MRI into screening strategies leads to more accurate diagnosis of clinically significant prostate cancer, while diagnosis of indolent cancers is reduced. In the future, molecular liquid-based biomarkers have the potential to complement or even replace PSA in PCa screening and further personalize screening strategies. Active surveillance as an alternative to immediate radical therapy of demographically increasing PCa diagnoses can potentially further improve the benefit-to-harm ratio of organized screening. Early detection of PCa should be organized on apopulation level into personalized and evidence-based screening strategies. Multiparametric MRI of the prostate may play akey role in this setting.

Navigating the evolving diagnostic and therapeutic landscape of low- and intermediate-risk prostate cancer

Abstract In this nonsystematic review of the literature, we explored the changing landscape of detection and treatment of low- and intermediate-risk prostate cancer (PCa). Through emphasizing improved cancer assessment with histology classification and genomics, we investigated key developments in PCa detection and risk stratification. The pivotal role of prostate magnetic resonance imaging (MRI) in the novel diagnostic pathway is examined, alongside the benefits and drawbacks of MRI-targeted biopsies for detection and tumor characterization. We also delved into treatment options, particularly active surveillance for intermediate-risk PCa. Outcomes are compared between intermediate- and low-risk patients, offering insights into tailored management. Surgical techniques, including Retzius-sparing surgery, precision prostatectomy, and partial prostatectomy for anterior cancer, are appraised. Each technique has the potential to enhance outcomes and minimize complications. Advancements in technology and radiobiology, including computed tomography (CT)/MRI imaging and positron emission tomography (PET) fusion, allow for precise dose adjustment and daily target monitoring with imaging-guided radiotherapy, opening new ways of tailoring patients’ treatments. Finally, experimental therapeutic approaches such as focal therapy open new treatment frontiers, although they create new needs in tumor identification and tracking during and after the procedure.

Long-term follow-up results of multiparametric prostate MRI and the prognostic value of PI-RADS: a single-center retrospective cohort study

We aim to examine the long-term outcomes of patients who underwent multiparametric prostate magnetic resonance imaging (mp-MRI) for suspected prostate cancer (PCa), specifically based on their initial Prostate Imaging Reporting and Data System (PI-RADS) categories and various clinical factors. Our secondary aim is to evaluate the prognostic value of the PI-RADS through the National Comprehensive Cancer Network (NCCN) risk group distribution. This research was conducted as a single-center retrospective cohort study in a tertiary care hospital. A total of 1,359 cases having at least one histopathological examination after the initial mp-MRI and/or adequate clinical/radiological follow-up data were included in the clinically significant PCa (cs-PCa) diagnosis-free survival analysis. Initial mp-MRI dates were accepted as the start of follow-up for the time-to-event analysis. The event was defined as cs-PCa diagnosis (International Society of Urological Pathology ≥2). Patients who were not diagnosed with cs-PCa during follow-up were censored according to predefined literature-based criteria at the end of the maximum follow-up duration with no reasonable suspicion of PCa and no biopsy indication. The impact of various factors on survival was assessed using a log-rank test and multivariable Cox regression. Subsequently, 394 cases diagnosed with PCa during follow-up were evaluated, based on initial PI-RADS categories and NCCN risk groups. Three main risk factors for cs-PCa diagnosis during follow-up were an initial PI-RADS 5 category, initial PI-RADS 4 category, and high MRI-defined PSA density (mPSAD), with average hazard ratios of 29.52, 14.46, and 3.12, respectively. The PI-RADS 3 category, advanced age group, and biopsy-naïve status were identified as additional risk factors (hazard ratios: 2.03, 1.54-1.98, and 1.79, respectively). In the PI-RADS 1-2 cohort, 1, 3, and 5-year cs-PCa diagnosis-free survival rates were 99.1%, 96.5%, and 93.8%, respectively. For the PI-RADS 3 cohort, 1, 3, and 5-year cs-PCa diagnosis-free survival rates were 94.9%, 90.9%, and 89.1%, respectively. For the PI-RADS 4 cohort, 1, 3, and 5-year cs-PCa diagnosis-free survival rates were 56.6%, 55.1%, and 55.1%, respectively. These rates were found to all be 24.2% in the PI-RADS 5 cohort. Considering the 394 cases diagnosed with PCa during follow-up, PI-RADS ≥4 cases were more likely to harbor unfavorable PCa compared to PI-RADS ≤3 cases (P < 0.001). In the PI-RADS 3 subgroup analysis, a low mPSAD (<0.15 ng/mL2) was found to be a protective prognostic factor against unfavorable PCa (P = 0.005). The PI-RADS category has a significant impact on patient management and provides important diagnostic and prognostic information. Higher initial PI-RADS categories are associated with decreased follow-up losses, a shorter time to PCa diagnosis, increased biopsy rates, a higher likelihood of developing cs-PCa during follow-up, and a worse PCa prognosis. Combining mPSAD with PI-RADS categories could enhance diagnostic stratification in the identification of cs-PCa.

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.

Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS). The images were reconstructed with and without DLR in super-resolution mode. The image quality was rated in six domains. Signal-to-noise ratio (SNR), and image sharpness were measured. The mean acquisition time was 281±23 s for the standard and 140±12 s for the short acquisition (P<0.0001). DLR images had higher sharpness compared to non-DLR (P<0.001). Short and short-DLR had lower SNR than the standard and standard-DLR (P<0.001). The perceived image quality of short-DLR was rated better in all categories compared to the standard sequence (P<0.001 to P=0.004). All domains of subjective evaluation were correlated with measured image sharpness (P<0.001). T2-TSE acquisition of the prostate accelerated using CS combined with DLR reconstruction provides images with increased sharpness that have a superior quality as perceived by human readers compared to standard T2-TSE. The perceived image quality is correlated with measured image contrast.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Attempting to Improve Prostate MR Image Quality at Scale through the ACR Learning Network

Attempting to Improve Prostate MR Image Quality at Scale through the ACR Learning Network

Improving Prostate MR Image Quality in Practice – Initial results from the ACR Prostate MR Image Quality Improvement Collaborative

ObjectiveVariability in prostate MRI quality is an increasingly recognized problem that negatively affects patient care. This report aims to describe the results and key learnings of the first cohort of the ACR Learning Network Prostate MR Image Quality Improvement Collaborative. MethodsTeams from five organizations in the U.S. were trained on a structured improvement method. After reaching a consensus on image quality and auditing their images using the Prostate Imaging Quality (PI-QUAL) system, teams conducted a current state analysis to identify barriers to obtaining high-quality images. Through plan-do-study-act cycles involving frontline staff, each site designed and tested interventions targeting image quality key drivers. The percentage of exams meeting quality criteria (i.e., PI-QUAL score ≥ 4) was plotted on a run chart, and project progress was reviewed in weekly meetings. At the collaborative level, the goal was to increase the percentage of exams with PI-QUAL ≥ 4 to at least 85%. ResultsAcross 2380 exams audited, the mean weekly rates of prostate MR exams meeting image quality criteria increased from 67% (range: 60-74%) at baseline to 87% (range: 80-97%) upon program completion. The most commonly employed interventions were MR protocol adjustments, development and implementation of patient preparation instructions, personell training and development of an auditing process mechanism. ConclusionA Learning Network model, where organizations share knowledge and work together toward a common goal, can improve prostate MR image quality at multiple sites simultaneously. The inaugural cohort's key learnings provide a roadmap for improvement on a broader scale.

Evaluation of the relationship between prostate volume and prostate specific antigen in patients diagnosed with benign prostatic hyperplasia

Aims: The aim of this study was to investigate the relationship between prostate volume and prostate specific antigen (PSA) levels in patients diagnosed with benign prostatic hyperplasia (BPH). In addition, it is aimed to show that BPH may be one of the causes of PSA elevation in patients without prostate cancer. Methods: Prostate volume was calculated in 60 BPH patients who underwent prostate magnetic resonance imaging (MRI) imaging in radiology department between December 1, 2023 and March 1, 2024 due to elevated PSA. The relationship between prostate volume and the tpsa and fpsa values of the patients in the last month was evaluated. Results: There was a positive correlation between age and total prostate specific antigen (tpsa) and free prostate specific antigen (fpsa) in patients with benign prostatic hyperplasia. However, the correlation between age and fpsa was stronger. Similarly, a positive correlation was found between prostate volume and tpsa and fpsa. The correlation between increased prostate volume and increased fpsa was also stronger. Conclusion: In our study, a positive correlation was found between prostate volume and tpsa and fpsa in patients diagnosed with BPH. It was determined that one of the reasons for the increase in PSA values may be the increase in prostate volume.

The Added Value of Prostate Magnetic Resonance Imaging to Patient Selection.

There has been a rapid increase in the utilization of magnetic resonance imaging (MRI) for prostate cancer detection. The objective of this study was to measure the increase in utilization of MRI before prostate biopsy and the effects on the distribution of Prostate Imaging Reporting and Data System (PI-RAD) scores and Gleason grades over a 5-year interval in an integrated health system. The authors conducted a retrospective analysis of prostate MRI studies prior to biopsy in the calendar years of 2017 and 2022. Peak PI-RADS score, peak Gleason grade of suspected prostatic lesions, and the number of biopsy cores were collected from radiology reports and pathology reports from patients' electronic health records, respectively. All statistical tests were 2-tailed with a significance level set at p < 0.05. Categorical data analyses were performed using Mann-Whitney tests. Continuous data analyses were performed using t-tests. The total number of prostate MRIs and the number of MRIs with subsequent biopsy respectively increased by 178% and 215% over a 5-year interval (2017-2022). There was a higher proportion of MRI studies with an associated biopsy given a PI-RADS score of ≥ 3 (91%) and a Gleason grade of ≥ 7 (61%) in 2022 than in 2017 (PI-RADS: 75%; Gleason: 28%). Increased utilization of prostate MRI has been associated with a higher proportion of biopsies with high PI-RADS and Gleason scores consistent with improved patient selection in this integrated health system.

FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

Expected impact of MRI-targeted biopsy interreader variability among uropathologists on ProScreen prostate cancer screening trial: a pre-trial validation study

PurposeProstate cancer (PCa) histology, particularly the Gleason score, is an independent prognostic predictor in PCa. Little is known about the inter-reader variability in grading of targeted prostate biopsy based on magnetic resonance imaging (MRI). The aim of this study was to assess inter-reader variability in Gleason grading of MRI-targeted biopsy among uropathologists and its potential impact on a population-based randomized PCa screening trial (ProScreen).MethodsFrom June 2014 to May 2018, 100 men with clinically suspected PCa were retrospectively selected. All men underwent prostate MRI and 86 underwent targeted prostate of the prostate. Six pathologists individually reviewed the pathology slides of the prostate biopsies. The five-tier ISUP (The International Society of Urological Pathology) grade grouping (GG) system was used. Fleiss’ weighted kappa (κ) and Model-based kappa for associations were computed to estimate the combined agreement between individual pathologists.ResultsGG reporting of targeted prostate was highly consistent among the trial pathologists. Inter-reader agreement for cancer (GG1–5) vs. benign was excellent (Model-based kappa 0.90, Fleiss’ kappa κ = 0.90) and for clinically significant prostate cancer (csPCa) (GG2–5 vs. GG0 vs. GG1), it was good (Model-based kappa 0.70, Fleiss’ kappa κ 0.67).ConclusionsInter-reader agreement in grading of MRI-targeted biopsy was good to excellent, while it was fair to moderate for MRI in the same cohort, as previously shown. Importantly, there was wide consensus by pathologists in assigning the contemporary GG on MRI-targeted biopsy suggesting high reproducibility of pathology reporting in the ProScreen trial.

New Prostate MRI Scoring Systems (PI-QUAL, PRECISE, PI-RR, and PI-FAB): AJR Expert Panel Narrative Review.

Multiparametric MRI (mpMRI), interpreted using PI-RADS, improves the initial detection of clinically significant prostate cancer (PCa). Prostate MR image quality has increasingly recognized relevance to the use of mpMRI for PCa diagnosis. Additionally, mpMRI is increasingly used in scenarios beyond initial detection, including active surveillance and assessment for local recurrence after prostatectomy, radiation therapy, or focal therapy. Acknowledging these evolving demands, specialized prostate MRI scoring systems beyond PI-RADS have emerged, to address distinct scenarios and unmet needs. Examples include Prostate Imaging Quality (PI-QUAL) for assessment of image quality of mpMRI, Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) recommendations for evaluation of serial mpMRI examinations during active surveillance, Prostate Imaging for Recurrence Reporting System (PI-RR) for assessment for local recurrence after prostatectomy or radiation therapy, and Prostate Imaging after Focal Ablation (PI-FAB) for assessment for local recurrence after focal therapy. These systems' development and early uptake signal a compelling shift towards prostate MRI standardization in different scenarios, and ongoing research will help refine their roles in practice. This AJR Expert Panel Narrative Review critically examines these new prostate MRI scoring systems (PI-QUAL, PRECISE, PI-RR, and PI-FAB), analyzing the available evidence, delineating current limitations, and proposing solutions for improvement.

3T multiparametric MRI’s accuracy in detecting prostate cancer using Prostate Imaging Reporting and Data System (PIRADS) version 2.1 with prostate biopsy as a reference

BackgroundMultiparametric magnetic resonance imaging (MRI) is valuable in detecting prostate cancer due to its high sensitivity to malignant lesions. It is commonly utilized to improve the identification of clinically significant cancers within the prostate. This study aimed to correlate the findings from 3T multiparametric MRI of the prostate using the updated Prostate Imaging Reporting and Data System version 2.1 (PIRADSv2.1) from 2019 with reference to prostate biopsy results. Additionally, PIRADSv2.1 was used to calculate the sensitivity, specificity, positive predictive value, and negative predictive value of the 3T multiparametric MRI of the prostate.Methods and materialsA retrospective study was conducted at a tertiary center, wherein we identified patients who underwent a prostate biopsy between June 2019 and June 2021 and had a corresponding MRI of the prostate performed at the same institution, evaluated with PIRADSv2.1 criteria.ResultsA total of 50 patients were eligible for final analysis. The prevalence of prostate cancer was 69% (95% confidence interval (CI) 54–81%). Receiver operating characteristic (ROC) curves were generated for 3T multiparametric MRI of the prostate using PIRADSv2.1 to diagnose prostate cancer; the area under the ROC curve was 0.81 (95% CI 0.68–0.95, p < 0.001). The sensitivity, specificity, positive predictive value, and negative predictive value of the 3T multiparametric prostate MRI using PIRADSv2.1 were 74.0%, 87.0%, 92.9%, and 59.1%, respectively.ConclusionsPIRADSv2.1 exhibited good overall performance in the diagnosis of prostate cancer.

The performance of artificial intelligence in prostate magnetic resonance imaging screening

Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.

Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer

Multiple strategies integrating magnetic resonance imaging (MRI) and clinical data have been proposed to determine the need for a prostate biopsy in men with suspected clinically significant prostate cancer (csPCa) (Gleason score ≥3 + 4). However, inconsistencies across different strategies create challenges for drawing a definitive conclusion. To determine the optimal prostate biopsy decision-making strategy for avoiding unnecessary biopsies and minimizing the risk of missing csPCa by combining MRI Prostate Imaging Reporting & Data System (PI-RADS) and clinical data. PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022. English-language studies that evaluated men with suspected but not confirmed csPCa who underwent MRI PI-RADS followed by prostate biopsy were included. Each study had proposed a biopsy plan by combining PI-RADS and clinical data. Studies were independently assessed for eligibility for inclusion. Quality of studies was appraised using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the Newcastle-Ottawa Scale. Mixed-effects meta-analyses and meta-regression models with multimodel inference were performed. Reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Independent risk factors of csPCa were determined by performing meta-regression between the rate of csPCa and PI-RADS and clinical parameters. Yields of different biopsy strategies were assessed by performing diagnostic meta-analysis. The analyses included 72 studies comprising 36 366 patients. Univariable meta-regression showed that PI-RADS 4 (β-coefficient [SE], 7.82 [3.85]; P = .045) and PI-RADS 5 (β-coefficient [SE], 23.18 [4.46]; P < .001) lesions, but not PI-RADS 3 lesions (β-coefficient [SE], -4.08 [3.06]; P = .19), were significantly associated with a higher risk of csPCa. When considered jointly in a multivariable model, prostate-specific antigen density (PSAD) was the only clinical variable significantly associated with csPCa (β-coefficient [SE], 15.50 [5.14]; P < .001) besides PI-RADS 5 (β-coefficient [SE], 9.19 [3.33]; P < .001). Avoiding biopsy in patients with lesions with PI-RADS category of 3 or less and PSAD less than 0.10 (vs <0.15) ng/mL2 resulted in reducing 30% (vs 48%) of unnecessary biopsies (compared with performing biopsy in all suspected patients), with an estimated sensitivity of 97% (vs 95%) and number needed to harm of 17 (vs 15). These findings suggest that in patients with suspected csPCa, patient-tailored prostate biopsy decisions based on PI-RADS and PSAD could prevent unnecessary procedures while maintaining high sensitivity.

Segmentation versus detection: Development and evaluation of deep learning models for prostate imaging reporting and data system lesions localisation on Bi‐parametric prostate magnetic resonance imaging

AbstractAutomated prostate cancer detection in magnetic resonance imaging (MRI) scans is of significant importance for cancer patient management. Most existing computer‐aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results. The authors have (1) carefully compared performances of most‐developed segmentation and object detection methods in localising prostate imaging reporting and data system (PIRADS)‐labelled prostate lesions on MRI scans; (2) proposed an additional customised set of lesion‐level localisation sensitivity and precision; (3) proposed efficient ways to ensemble the segmentation and object detection methods for improved performances. The ground‐truth (GT) perspective lesion‐level sensitivity and prediction‐perspective lesion‐level precision are reported, to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions. The two networks are trained independently on 549 clinical patients data with PIRADS‐V2 as GT labels, and tested on 161 internal and 100 external MRI scans. At the lesion level, nnDetection outperforms nnUNet for detecting both PIRADS ≥ 3 and PIRADS ≥ 4 lesions in majority cases. For example, at the average false positive prediction per patient being 3, nnDetection achieves a greater Intersection‐of‐Union (IoU)‐based sensitivity than nnUNet for detecting PIRADS ≥ 3 lesions, being 80.78% ± 1.50% versus 60.40% ± 1.64% (p &lt; 0.01). At the voxel level, nnUnet is in general superior or comparable to nnDetection. The proposed ensemble methods achieve improved or comparable lesion‐level accuracy, in all tested clinical scenarios. For example, at 3 false positives, the lesion‐wise ensemble method achieves 82.24% ± 1.43% sensitivity versus 80.78% ± 1.50% (nnDetection) and 60.40% ± 1.64% (nnUNet) for detecting PIRADS ≥ 3 lesions. Consistent conclusions are also drawn from results on the external data set.

Variation of b Value for Axial DWI Prostate MR Imaging in Cancer: Which b Value is Optimal?

Background: Diffusion Weighted Imaging (DWI) is a technique that visualizes the movement of water molecules from areas of higher to lower concentration. In DWI sequences, the b-value parameter affects the signal intensity in the images. The objective of this study is to describe the differences in anatomical image information between b-value variations of 1500s/mm2, 2000s/mm2, and 2500s/mm2 in MRI prostate case cancer and to determine the most optimal b-value. Method: This study used a pre-exeachimental approach with a 1.5T Philips MRI machine at a private hospital in South Jakarta from March to April 2023. The sample consisted of 12 Prostate MRI images with DWI sequences and different b-values. Visual grading assessment was eachformed by three radiology specialists, and the data were analyzed using the Friedman test in SPSS. Results: The results showed a significant difference in anatomical image information based on the b-value variations used, with a p-value of 0.05 (0.02). The highest mean rank was obtained from the Friedman test for the b-value of 2000s/mm2. Conclusion: The significant difference in anatomical image information occurred due to differences in signal intensity and noise levels. Prostate cancer tissues appeared more hyperintense with lower noise in images with higher b-values, and vice versa. Additionally, the b-value of 2000s/mm2 resulted in the most optimal signal intensity and noise level. In conclusion, the use of b-value variations in axial DWI images of prostate cancer Prostate MRI to differences in anatomical image information, with the most optimal image information observed at a b-value of 2000s/mm2.

Cumulative cancer locations on prostate biopsy and active surveillance outcomes in the MRI era.

To validate the use of a cumulative cancer locations (CCLO) score, a measurement of tumor volume on biopsy, and to develop a novel magnetic resonance imaging(MRI)-informed CCLO (mCCLO) score to predict clinical outcomes on active surveillance (AS). The CCLO score is a sum of uniquely involved sextants with prostate cancer on diagnostic + confirmatory biopsy. The mCCLO score incorporates MRI findings into the CCLO score. Participants included 1284 individuals enrolled on AS between 1994 and 2022, 343 of whom underwent prostate MRI. The primary outcome was grade reclassification (GR) to grade group ≥2 disease; the secondary outcome was receipt of definitive treatment. Increasing CCLO and mCCLO risk groups were associated with higher risk of GR and undergoing definitive treatment (both p < 0.001). On multivariable analysis, increasing mCCLO score was associated with higher risk of GR and receipt of definitive treatment (hazard ratios [HRs] per 1-unit increase: 1.26 [95% confidence interval [CI]: 1.12-1.41] and 1.21 [95% CI: 1.07-1.36], respectively). The model using mCCLO score to predict GR (c-index: 0.671; 95% CI: 0.621-0.721) performed at least as well as models using the number of cores positive for cancer (0.664 [0.613-0.715]; p = 0.7) and the maximum percentage of cancer in a core (0.641 [0.585-0.696]; p = 0.14). The CCLO score is a valid, objective metric to predict GR and receipt of treatment in a large AS cohort. The ability of the MRI-informed mCCLO to predict GR is on par with traditional metrics of tumor volume but is more descriptive and may benefit from greater reproducibility. The mCCLO score can be implemented as a shorthand, informative tool for counseling patients about whether to remain on AS.

Association between 3D membranous urethral parameters and urinary continence recovery after RARP

ObjectivesTo evaluate whether the urinary continence (UC) recovery after robotic-assisted radical prostatectomy (RARP) relates to the membranous urethral length (MUL) and the membranous urethral complex volume (MUV).Materials and methods120 patients who underwent RARP were enrolled according to the different times of UC recovery and examined using prostate magnetic resonance imaging (MRI) before surgery. The membranous urethral (MU) parameters were measured using the three-Dimensional (3D) model reconstructed by holographic technology, such as total MUV (tMUV), exposed MUV (eMUV), full MUL (fMUL) and exposed MUL (eMUL). Statistical software SPSS 26.0 was used to analyze the data and compare the MU parameters and baseline data in different groups.ResultsPatients with larger tMUV (p = 0.038), eMUV (p = 0.003), longer fMUL (p = 0.025), eMUL (p = 0.044) had better UC after removal of the catheter, and eMUV (OR = 1.002, 95%CI = 1.001–1.004, p = 0.004) was a predictor; the patients with younger age (p = 0.021), lower VPSS score (p = 0.004) and larger eMUV (p = 0.012) and longer eMUL (p = 0.049) had better UC recovery one month after RARP while eMUV (OR = 1.002, 95% CI = 1.000–1.003, p = 0.008) and VPSS score (OR = 0.886, 95% CI = 0.806–0.973, p = 0.011) were independent risk factors; The patients with younger age (p = 0.018), larger tMUV (p = 0.029), eMUV (p = 0.016) had better UC recovery three months after RARP. eMUV (OR = 1.002, 95% CI = 1.000–1.004, p = 0.042) and age (OR = 0.904, 95% CI = 0.818–0.998, p = 0.046) were independent risk factors.ConclusionThis clinical study shows that patients with larger MUV and longer MUL can return to UC earlier after surgery. Among that, eMUV is a better predictor.

Comparison of Magnetic Resonance Imaging–Based Risk Calculators to Predict Prostate Cancer Risk

Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Prostate MRI followed by prostate biopsy. The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.