Prostate Magnetic Resonance Research Articles

Predicting cancer detection rates from multiparametric prostate MRI

Introduction: Although the Prostate Imaging-Reporting and Data System (PI-RADS) categorization represents the standard method for assessing the risk of prostate cancer using prostate magnetic resonance imaging (MRI), there exists wide variation in cancer detection rates (CDRs) in real-world practice. We therefore evaluated the association of clinical and radiographic features with CDRs and developed a predictive model to improve clinical management. Methods: We identified men aged 18–89 years with elevated prostate-specific antigen (PSA) or on active surveillance for prostate cancer who underwent MRI-ultrasound (US) fusion biopsy or in-bore MRI-targeted biopsy. The associations of features with the per-lesion CDR (Gleason 6–10) and clinically significant (cs) CDR (Gleason 7–10) were examined using logistic regression, and results were operationalized into a predictive model. Results: Targeted biopsy was performed for 347 lesions in 281 patients. Overall, the CDR was 49.0% and the csCDR was 28.0%. On multivariable analysis, increasing PI-RADS category, no prior prostate biopsies, smaller prostate size, and increasing PSA density were independently associated with higher CDR, while 0–1 prior prostate biopsies, and a solitary PI-RADS 3–5 lesion were associated with higher csCDR. A predictive model provided a greater net benefit than a strategy of performing biopsy in all PI-RADS 3–5 lesions across a wide range of threshold probabilities. Conclusions: Several clinical and radiographic features are independently associated with the risk of prostate cancer in men undergoing MRI-targeted biopsy. A predictive model based on these features can improve clinical decisions regarding biopsy compared to the conventional strategy of performing biopsy for all PI-RADS 3–5 lesions.

Multi-label semantic segmentation of magnetic resonance images of the prostate gland

Prostate segmentation is a substantial factor in the diagnostic pathway of suspicious prostate lesions. Medical doctors are assisted by computer-aided detection and diagnosis systems systems and methods derived from artificial intelligence deep learning-based systems have to be trained on existing data. Especially freely available labeled prostate magnetic resonance imaging data is rare. With regard to this problem, we show a method to combine two existing small datasets to form a bigger one. We present a data processing pipeline consisting of a cascaded network architecture that is able to perform multi-label semantic segmentation, hence, capable of classifying each pixel in a T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{T}_{2}$$\\end{document}-weighted image not only to one class but to a subset of the prostate zones and classes of interest. This delivers richer information such as overlapping zones that are key to medical radiological examination. Additionally, we describe how to integrate expert knowledge in our deep learning system. To increase data variety for training and evaluation we use image augmentation on our two datasets—a freely available dataset and our new open-source dataset. To combine the datasets we denoise the contourings in our dataset by using an effective yet simple algorithm based on standard computer vision methods only. The performance of the presented methodology is compared and evaluated using the dice score metric and 5-fold cross-validation on all datasets. Although we trained on tiny datasets our method achieves excellent segmentation quality and is even able to detect prostate cancer. Our method to combine the two datasets reduces segmentation errors and increases data variety. The proposed architecture significantly improves performance by including expert knowledge via feature-map concatenation. On the initiative for collaborative computer vision benchmarking dataset we achieve on average dice scores of approximately 91% for the whole prostate gland, 67% for the peripheral zone and 75% for the prostate central gland. We find that image augmentation except contrast limited adaptive histogram equalisation did not have much influence on the segmentation quality. Derived and enhanced from existing methods we present an approach that is able to deliver multi-label semantic segmentation results for prostate magnetic resonance imaging. This approach is simple and could be applied to other applications of deep learning as well. It improves the segmentation results by a large margin. Once tweaked to the data, our denoising and combination algorithm delivers robust and accurate results even on data with segmentation errors.

Patients' anxieties and fears: a comparison between transrectal prostate biopsy and prostate MRI.

Prostate biopsies are an invasive procedure that can lead to anxieties and fear before the examination. Prostate magnetic resonance imaging (MRI) is seen as a non-invasive test although it is known that "scanxiety" affects many patients. Transrectal ultrasound (TRUS)-guided prostate biopsies and multiparametric prostate MRI (mpMRI) are commonly used methods in patients with suspected prostate cancer (PCa). This study investigates fears and anxieties towards the TRUS and mpMRI. All patients scheduled for mpMRI or TRUS biopsy between January and December 2018 were asked to participate in this single-center study. A total of 196 completed questionnaires were returned and included. On a 5-point Likert scale the fear of the examination was lower for the mpMRI [1.53; 95% confidence interval (CI): 1.38 to 1.69] than for a TRUS biopsy (2.47; 95% CI: 2.21 to 2.71). In detail, patients with a scheduled TRUS biopsy had significantly higher levels for fear of pain [2.49 (95% CI: 2.19 to 2.78) vs. 1.51 (95% CI: 1.35 to 1.67); P<0.001] and fear of complications [2.71 (95% CI: 2.45 to 2.98) vs. 2.11 (95% CI: 1.89 to 2.32); P=0.001]. There was no relevant difference about the fact that patients knew what to expect [3.02 (95% CI: 2.68 to 3.35) vs. 2.99 (95% CI: 2.70 to 3.26); P=0.47] and the expectation that the examination will go over well [3.24 (95% CI: 2.92 to 3.57) vs. 3.27 (95% CI: 3.00 to 3.58); P=0.55]. On average, fear levels were moderate before mpMRI and TRUS biopsy. Patients are more afraid of TRUS biopsy than mpMRI but the differences were low. The biggest fear remains the fear of the result of the examinations independently of the method.

Preoperative prostate magnetic resonance imaging does not impact surgical outcomes of radical prostatectomy

ABSTRACT Objective: We reviewed our institutional experience of radical prostatectomy with and without preoperative multiparametric magnetic resonance imaging (mpMRI) to assess the impact of preoperative prostate mpMRI on surgical outcomes of radical prostatectomy. Methods: We identified patients at our institution who underwent radical prostatectomy for prostate cancer (PCa) between January 2012 and December 2017 (n = 1044). Using propensity scoring analysis, patients who underwent preoperative mpMRI (n = 285) were matched 1:1 to patients who did not receive preoperative mpMRI (n = 285). Multivariable regression analysis was performed to identify factors predictive of operative time, estimated blood loss (EBL), lymph node yield, rates of complications within 30 days, and positive surgical margin (PSM). Results: There were no significant differences in operative time, EBL, PSM, lymph node yield, or complication rates between the two cohorts. Multivariable analysis demonstrated that preoperative mpMRI was not predictive of the measured perioperative outcomes. Significant comorbidity (Charlson Comorbidity Index ≥3) was the sole predictor of perioperative complications (P = 0.015). Increasing biopsy Gleason score predicted increased lymph node yield (P &lt; 0.001). The probability of PSM was associated with increasing preoperative prostate-specific antigen (odds ratio 1.036, P = 0.009). Body mass index was a predictor of operative time (P = 0.016) and EBL (P = 0.001). Conclusions: Although preoperative mpMRI has an important role in the diagnosis and staging of PCa, it does not impact perioperative radical prostatectomy outcomes. Our findings do not support the routine use of preoperative mpMRI for surgical planning in patients already diagnosed with clinically localized PCa.

Prostate magnetic resonance imaging (MRI) in patients with hip implants-presetting a protocol using a phantom.

Metal structures are a source of artifacts that significantly complicate the interpretation of magnetic resonance imaging (MRI). The use of prostate MRI as a preliminary test in men with a suspicion on prostate cancer leads to an increased use of the test. The aim of this study was to solve a clinically significant problem: to ensure the reduction of artifacts from metal hip implants during prostate MRI. Another goal was to evaluate the impact of artifact reduction methods on quantitative measurements. The prostate gland (PG) phantom model was a cylinder filled with an aqueous solution of polyvinylpyrrolidone at the concentrations of 40%, 30%, and 20% [central zone (CZ), peripheral zone (PZ), and "lesion", respectively]. Phantom MRI study was conducted on Philips Ingenia 1.5T and Philips Ingenia 3T scanners. For 1.5 T, the reduction in the influence of artifacts inside region of interest (ROI) was observed, expressed in a decrease in the average apparent diffusion coefficient (ADC) (CZ, PZ, "lesion") for the manual artifact reduction (MAR) and ZOOM (title of software artifact reduction) techniques compared to the standard method. For 3T this effect was not detected. The same ADC results were obtained for Standard and MAR techniques, and increased ADC values for ZOOM. Despite the fact that the spread of ADC values on 3.0T scanners was minimal, there was a significant deviation of ADC values from the reference ones (up to 30.4%). Therefore, it is necessary to use a correction coefficient in the ADC calculation for the 3.0 T device. In the presented clinical case, high-quality tomograms were obtained without any artifacts, despite the presence of two hip replacement devices in the scanning area. The accurate prostate MRI in the presence of implants is essential for an accurate diagnosis. This approach allows to reduce artifacts from hip implants, to visualize PG and periprostatic tissue in the best way, and to detect malignant and benign changes.

Imaging assessment of prostate cancer recurrence: advances in detection of local and systemic relapse.

Prostate cancer (PCa) relapse, defined either by persistent PSA levels (after RP) or biochemical recurrence (BCR), is a common occurrence. The imaging evaluation of patients experiencing PCa relapse has undergone significant advancements in the past decade, notably with the introduction of new Positron Emission Tomography (PET) tracers such as Prostate-specific membrane antigen (PSMA), and the progress in functional Magnetic Resonance Imaging (MRI). This article will explore the role of traditional imaging, the evolution of MRI towards the development of the Prostate Magnetic Resonance Imaging for Local Recurrence Reporting (PI-RR) scoring system, and how next-generation imaging is enhancing diagnostic accuracy in the setting of PCa relapse, which is essential for adopting personalized strategies that may ultimately impact outcomes.

Optimizing prostate cancer detection in transition zone: an analysis of apparent diffusion coefficient values in prostate magnetic resonance imaging evaluation with Prostate Imaging Reporting and Data System (PI-RADS) assessment.

Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for the early detection of clinically significant prostate cancer (csPCa). However, the achievement of accurate detection rates, particularly for transition zone (TZ) lesions, remains challenging. We investigated the relationship between apparent diffusion coefficient (ADC) values in Prostate Imaging Reporting and Data System (PI-RADS) 3-5 lesions and csPCa within the TZ. We retrospectively evaluated TZ lesions in patients who underwent 3.0 Tesla MRI followed by MRI-targeted/transrectal ultrasound fusion biopsies (MRI-FBx). Fusion biopsies were performed for potentially cancerous lesions, defined as lesions with PI-RADS scores 3-5. We analyzed 196 lesions for which fusion biopsies were performed. The overall prostate cancer (PCa) detection rate was 53.6% (105/196); csPCa constituted 33.7% (66/196) of cases. The minimum ADC value was significantly lower for patients with csPCa (484.9±112.3 µm2/s) than for patients with benign histology or non-csPCa (P<0.001). Older age, higher initial prostate-specific antigen level, larger region of interest, and minimum and mean ADC values were associated with the presence of csPCa. Multivariate analysis indicated that only the minimum ADC value was an independent predictor of csPCa. Using a cutoff minimum ADC value <561 µm2/s to detect csPCa in TZ lesions increased the detection rate to 57.4% (54/94). The minimum ADC value provides substantial additional information regarding the presence of csPCa in the TZ, potentially improving the detection rates for lesions rated as PI-RADS 3-5 and informing the need for follow-up biopsies in areas that are initially cancer-free.

Financial Implications of Prostate Magnetic Resonance Imaging Without Contrast.

Financial Implications of Prostate Magnetic Resonance Imaging Without Contrast.

Identifying Barriers to Timely Follow-up After Elevated PSA Screening: A Retrospective Analysis of a Large Healthcare System

ObjectivesTo examine elevated PSA follow-up within our system and identify areas for improvement in the timely diagnosis of prostate cancer. MethodsWe queried the Mass General Brigham’s Enterprise Data Warehouse from 2018-2021, identifying patients with elevated PSA and documented time to follow-up. Timely follow-up was defined as having a urologist appointment, prostate biopsy, or prostate magnetic resonance imaging within 6 months from diagnosis. We stratified the location of elevated PSA diagnosis to academic medical centers vs. community sites. Univariable and multivariable analyses were performed to identify factors impacting follow-up. ResultsWe included 28,346 patients, with 50.30%, 15.02%, and 34.69% receiving timely, untimely, and no follow-up during the study period, respectively. In multivariable analysis, patients seen at academic medical centers were more likely to receive follow-up care (OR=1.39, 95%CI 1.30-1.48). In a sensitivity analysis including two of our largest community hospitals as part of academic medical facilities, those following up at our main sites showed even higher odds of timely follow-up (OR=1.61, 95%CI 1.51-1.73). ConclusionOur study observed variations in follow-up rate between our academic medical centers and community sites. This finding highlights the need for efforts to improve consistency and timeliness of prostate cancer follow-up care across all facilities. By addressing interfacility disparities, we can facilitate the delivery of timely care to all patients.

Risk factors for Gleason score upgrade from prostate biopsy to radical prostatectomy.

Accurate identification of prostate cancer Gleason grade group remains an important component of the initial management of clinically localized disease. However, Gleason score upgrading (GSU) from biopsy to radical prostatectomy can occur in up to a third of patients treated with surgery. Concern for disease undergrading remains a source of diagnostic uncertainty, contributing to both over-treatment of low-risk disease as well as under-treatment of higher-risk prostate cancer. This review examines the published literature concerning risk factors for GSU from time of biopsy to prostatectomy final pathology. Risk factors identified for Gleason upgrading include patient demographic and clinical factors including age, body mass index, race, prostate volume, and biomarker based assays, including prostate-specific antigen (PSA) density, and testosterone values. In addition, prostate magnetic resonance imaging (MRI) findings have also been associated with GSU. Biopsy-specific characteristics associated with GSU include lower number of biopsy cores and lack of targeted methodology, and possibly increasing percent biopsy core positivity. Recognition of risk factors for disease undergrading may prompt confirmatory testing including repeat sampling or imaging. Continued refinements in imaging guided biopsy techniques may also reduce sampling error contributing to undergrading.

Do Prostate Imaging-Reporting and Data System (PIRADS) lesions predict holmium laser enucleation of prostate outcomes?

Prostate magnetic resonance imaging (MRI) is used for prostate cancer (PCa) screening and risk stratification and is helpful for surgical planning for patients undergoing holmium laser enucleation of the prostate (HoLEP). There are few studies investigating the correlation between MRI Prostate Imaging-Reporting and Data System (PIRADS) lesion characteristics and HoLEP pathology and outcomes. We performed retrospective review of patients who underwent HoLEP between January 2021 and August 2023 by a single surgeon. Preoperative, intraoperative, and postoperative characteristics and outcomes were analyzed for all patients who had a documented preoperative prostate MRI. There were 334 patients without a pre-existing diagnosis of PCa and with a preoperative prostate MRI, of which 140 (42%) had at least one PIRADS lesion. There was a total of 203 PIRADS lesions: 91 (45%) in the peripheral zone (PZ), 106 (52%) in the transition zone (TZ), and 6 (2%) not specified. Incidental PCa was noted in 44 (13%) patients at time of HoLEP. Presence or location of lesion was not significantly associated with rate or grade of incidental PCa on pathology. Greater number of lesions and lesion size correlated with longer procedure times. Lesion number, size, or grade were not found to correlate with cancer grade or rate of cancer. Grade, presence, location, size, and number of PIRADS lesions on preoperative prostate MRI for patients with an appropriate prior PCa workup were not significantly associated with incidental PCa or higher PCa grade on HoLEP pathology.

Associations Between Prostate Magnetic Resonance Imaging, Genomic Testing, and Treatment for Localized Prostate Cancer.

Although prostate MRI and tissue-based gene expression (genomic) tests improve staging and estimates of prostate cancer prognosis, their association with the intensity of treatment patients receive is not well understood. We performed a retrospective cohort study of Medicare beneficiaries diagnosed with clinically localized prostate cancer in 2013 through 2017 in the Surveillance, Epidemiology, and End Results database. The primary study outcome was the receipt of treatment intensification in the first 12 months after diagnosis (defined as the addition of androgen deprivation therapy among patients receiving radiation or pelvic lymphadenectomy among those undergoing radical prostatectomy). We assessed associations between the receipt of prostate MRI and genomic testing and treatment intensification, adjusting for clinical and sociodemographic factors and further stratifying the analyses by risk status. We identified 37,064 patients with clinically localized prostate cancer, including 6398, 22,011, and 5976 with low, intermediate, and high D'Amico-risk disease, respectively. Among all treated patients, receipt of prostate MRI was associated with increased odds of treatment intensification (odds ratio 1.76, 95% CI 1.65-1.88, P < .001). In contrast, genomic testing was not significantly associated. Among treated patients with high-risk disease, genomic testing was associated with decreased odds of intensified treatment (odds ratio 0.59, 95% CI 0.35-1.00, P = .05). Prostate MRI was associated with intensified treatment across risk strata, while genomic testing was associated with lower intensity of treatment among high-risk disease. Additional study is needed to determine whether use of imaging and risk stratification tools leads to improved long-term patient outcomes.

Prostate magnetic resonance imaging utilization and its relationship with advanced prostate cancer detection

BackgroundThe rise in advanced prostate cancer has coincided with increased use of Magnetic Resonance Imaging (MRI), leading to the hypothesis that this increase in surveillance registries is an artifact of more sensitive imaging tools. We assessed the association between regional variation in prostate MRI and advanced prostate cancer diagnoses. MethodsWe utilized SEER-Medicare data (2004–2015), including men > 65 diagnosed with localized prostate cancer. The predictor variable was the utilization of prostate MRI in each hospital referral region (HRR, representing regional healthcare markets). We compared the proportion of disease recorded as locally advanced or of regional risk group (cT3, cT4, and cN1) which would plausibly have been detected by prostate MRI. We conducted adjusted multivariable analysis and performed correlation analysis with Spearman rank coefficient at the level of the HRR. Sensitivity analysis for years 2011 to 2015 was conducted. ResultsOf 98,921 men diagnosed, 4.01% had locally advanced or regional disease. The median prostate MRI utilization rate was 4.58% (IQR [3.03%, 8.12%]). Adjusted multivariable analysis revealed no statistically significant correlation between MRI utilization and proportion of advanced prostate cancer (aOR = 1.01, 95% CI, [0.99,1.03]) in each region. The correlation between MRI usage and advanced diagnosis was not significant (Spearman Ρ = 0.09, P = 0.4). Sensitivity analysis conducted between 2011 and 2015 showed similar results (aOR = 1.008, 95% CI, [0.989, 1.027]; Spearman Ρ = 0.16, P = 0.1). ConclusionsDuring our study period, HRR-level utilization of MRI was not associated with higher incidences of advanced prostate cancer. This suggests the rising advanced prostate cancer diagnoses observed in this period are unlikely an artifact of greater sensitivity of modern imaging tests, but potentially due to other factors such as changes in screening or risk factors. With increased utilization and evolving techniques in recent years, the association between MRI and advanced prostate cancer detection warrants continued monitoring.

Does the type of the previous biopsy affect the fusion prostate biopsy results?

BackgroundIt has been more than a decade since fusion prostate biopsy (FPB) has been used in the diagnosis of prostate cancer (PCa). Therefore, patients with a previous history of negative FPB and ongoing suspicion of PCa are beginning to emerge. This study investigated whether the first biopsy type (standard or fusion) should be effective in deciding on a second biopsy. MethodsMale patients aged 40–75, with a serum prostate-specific antigen (PSA) value of less than 10 ng/mL and a negative biopsy history within the last 24 months, who underwent FPB in our clinic due to persistent PSA elevation and/or suspicious multiparametric prostate magnetic resonance imaging (MpMRI) findings were included to the study. Patients were divided into groups according to the type of first biopsy (Group 1; those whose first biopsy was FPB, Group 2; those whose first biopsy was standard prostate biopsy). Some demographic and clinical data of the groups, as well as PCa detection rates, were compared. A p value of less than 0.05 was considered statistically significant. ResultsA total of 275 patients (Group 1: 84, Group 2: 191) were included in this study. The groups were similar in terms of age, PSA values before the first biopsy, PSA values before the second biopsy, family history of PCa, and prostate volume. PCa was detected at a higher rate in Group 2 than Group 1 (23% vs 15.5%, p = 0.044). ConcluisonThe data obtained from this study indicate that the type of initial biopsy should be taken into account when deciding on FPB in secondary patients with a previous negative biopsy history.

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

Prognostic Value of MRI-Assessed Central Gland Volume for Biochemical Recurrence after Prostate Cancer Radiotherapy

Introduction: Increasing central gland volume has been associated with less aggressive prostate cancer, theorized to be secondary to biomechanical stress from benign prostatic hyperplasia (BPH). The aim of this study was to evaluate pretreatment prostate magnetic resonance imaging (MRI) metrics and clinical characteristics in predicting biochemical recurrence (BCR) after definitive radiotherapy (RT) for prostate cancer. Methods: In this retrospective single institution study, we identified men in our database with prostate cancer who underwent MRI within 6 months prior to completing definitive RT from May 2011 to February 2023. Total prostate volume, central gland volume, and peripheral zone volume were measured by a radiologist using manual segmentation. The primary objective was to determine the association of central gland volume with biochemical recurrence, defined by Phoenix criteria. A multivariable Cox proportional hazards regression model was constructed and adjusted for clinical factors. Results: A total of 373 men (median age 68 years) were included, with a median follow-up of 28 months. 13 (3.5%) were low risk, 97 (26%) favorable intermediate ris, 201 (53.9%) unfavorable intermediate risk, and 62 (16.6%) high risk. 54 (14.5%) patients received conventionally fractionated RT, 105 (28.2%), moderately hypofractionated RT, 121 (32.4%), high-dose rate brachytherapy, and 93 (24.9%) stereotactic body RT. The 3 and 5 year rates of BCR were 7.8% and 18.3%, respectively. Taking into account NCCN risk group and RT type, higher central gland volume was associated with decreased risk of BCR (HR: 0.73, 95% CI: 0.55-0.98, p=0.03). No significant association was seen with peripheral zone volume, PI-RADS score, or RT type. Conclusions: Increased central gland volume on pretreatment prostate MRI is independently associated with a lower risk of biochemical recurrence after definitive radiation for prostate cancer. BPH appears to confer favorable outcomes in RT patients and represents a novel, easily quantifiable metric in risk-stratification.

Impact of prostate MRI image quality on diagnostic performance for clinically significant prostate cancer (csPCa).

With the widespread clinical application of prostate magnetic resonance imaging (MRI), there has been an increasing demand for lesion detection and accurate diagnosis in prostate MR, which relies heavily on satisfactory image quality. Focusing on the primary sequences involved in Prostate Imaging Reporting and Data System (PI-RADS), this study have evaluated common quality issues in clinical practice (such as signal-to-noise ratio (SNR), artifacts, boundaries, and enhancement). The aim of the study was to determine the impact of image quality on clinically significant prostate cancer (csPCa) detection, positive predictive value (PPV) and radiologist's diagnosis in different sequences and prostate zones. This retrospective study included 306 patients who underwent prostate MRI with definitive pathological reports from February 2021 to December 2022. All histopathological specimens were evaluated according to the recommendations of the International Society of Urological Pathology (ISUP). An ISUP Grade Group ≥ 2 was considered as csPCa. Three radiologists from different centers respectively performed a binary classification assessment of image quality in the following ten aspects: (1) T2WI in the axial plane: SNR, prostate boundary conditions, the presence of artifacts; (2) T2WI in the sagittal or coronal plane: prostate boundary conditions; (3) DWI: SNR, delineation between the peripheral and transition zone, the presence of artifacts, the matching of DWI and T2WI images; (4) DCE: the evaluation of obturator artery enhancement, the evaluation of dynamic contrast enhancement. Fleiss' Kappa was used to determine the inter-reader agreement. Wilson's 95% confidence interval (95% CI) was used to calculate PPV. Chi-square test was used to calculate statistical significance. A p-value < 0.05 was considered statistically significant. High-quality images had a higher csPCa detection rate (56.5% to 64.3%) in axial T2WI, DWI, and DCE, with significant statistical differences in SNR in axial T2WI (p 0.002), the presence of artifacts in axial T2WI (p 0.044), the presence of artifacts in DWI (p < 0.001), and the matching of DWI and T2WI images (p < 0.001). High-quality images had a higher PPV (72.5% to 78.8%) and showed significant statistical significance in axial T2WI, DWI, and DCE. Additionally, we found that PI-RADS 3 (24.0% to 52.9%) contained more low-quality images compared to PI-RADS 4-5 (20.6% to 39.3%), with significant statistical differences in the prostate boundary conditions in axial T2WI (p 0.048) and the presence of artifacts in DWI (p 0.001). Regarding the relationship between csPCa detection and image quality in different prostate zones, this study found that significant statistical differences were only observed between high- (63.5% to 75.7%) and low-quality (30.0% to 50.0%) images in the peripheral zone (PZ). Prostate MRI quality may have an impact on the diagnostic performance. The poorer image quality is associated with lower csPCa detection rates and PPV, which can lead to an increase in radiologist's ambiguous diagnosis (PI-RADS 3), especially for the lesions located at PZ.

Perspectives on technology: Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability.

To explore the topic of Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability, including a discussion of major sources, mitigation approaches, and future directions. A narrative review of PI-RADS interobserver variability. PI-RADS was developed in 2012 to set technical standards for prostate magnetic resonance imaging (MRI), reduce interobserver variability at interpretation, and improve diagnostic accuracy in the MRI-directed diagnostic pathway for detection of clinically significant prostate cancer. While PI-RADS has been validated in selected research cohorts with prostate cancer imaging experts, subsequent prospective studies in routine clinical practice demonstrate wide variability in diagnostic performance. Radiologist and biopsy operator experience are the most important contributing drivers of high-quality care among multiple interrelated factors including variability in MRI hardware and technique, image quality, and population and patient-specific factors such as prostate cancer disease prevalence. Iterative improvements in PI-RADS have helped flatten the curve for novice readers and reduce variability. Innovations in image quality reporting, administrative and organisational workflows, and artificial intelligence hold promise in improving variability even further. Continued research into PI-RADS is needed to facilitate benchmark creation, reader certification, and independent accreditation, which are systems-level interventions needed to uphold and maintain high-quality prostate MRI across entire populations.

Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm.

Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to regrade initial prostate biopsies in 2 independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio = 3.3, P = .04), whereas upgrading of the initial biopsy by contemporary uropathologist reviews was not associated with this outcome. In a contemporary validation cohort that underwent prostate magnetic resonance imaging before initial biopsy (n = 169), upgrading of the initial biopsy (all contemporary GG1 by uropathologist grading) by AIRAProstate was associated with grade reclassification on AS (hazard ratio = 1.7, P = .03). These results demonstrate the utility of a DL-based grading algorithm in PCa risk stratification for AS.

A new parameter to increase the predictive value of multiparametric prostate magnetic resonance imaging for clinically significant prostate cancer in targeted biopsies: lesion density

AimTo investigate the predictive value of lesion length in multiparametric prostate magnetic resonance imaging with respect to prostate volume for clinically significant prostate cancer diagnosis in targeted biopsies. Materials and methodsThe data of biopsy-naïve patients in the Turkish Urooncology Association Prostate Cancer Database who underwent targeted prostate biopsies were included in this study. Lesion density is calculated as the ratio of lesion length (mm) in MR to prostate volume (cc). The biopsy results were divided into either clinically significant or insignificant cancer and benign groups. The difference in parameters between groups is evaluated by multivariable analysis to determine independent risk factors for clinically significant prostate cancer diagnosis. ResultsA total of 590 lesion biopsies were included in the study. In univariable analysis, prostate-specific antigen (PSA), PSA density, number of cores taken, lesion length, lesion density, patient age, and digital rectal examination findings were found to be different at a statistically significant level between groups (P values, respectively: 0.001, <0.001, <0.001, <0.001, <0.001, 0.012, 0.001). Subgroup analysis demonstrated that the lesion density was still significantly different between groups for all Prostate Imaging - Reporting and Data System (PI-RADS) 3, 4, and 5 subgroups (P values, respectively: 0.001, <0.001, <0.001). The multivariable analysis demonstrated that lesion density, along with the number of cores taken and the PI-RADS score of the lesion is an independent risk factor for predicting clinically significant prostate cancer, with the highest odds ratio among all parameters (OR: 27.31 [CI: 7.9–94.0]). ConclusionThis study demonstrated that lesion size with respect to prostate volume is an important independent risk factor for the prediction of clinically significant prostate cancer in the lesion-targeted biopsy. Combined with the PI-RADS score and parameters like digital rectal examination (DRE) findings and PSA density may further increase predictive power and help clinicians decide whether to perform a biopsy in low-risk patients or perform a re-biopsy for high-risk patients subsequent to an initial negative biopsy.