The Diagnostic Performance of Multiparametric Magnetic Resonance Imaging to Detect Significant Prostate Cancer

Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis

Prostate cancer (PCa) is a global health concern, ranking as the most common cancer among men in Western countries. Traditional diagnostic methods are invasive with adverse effects on patients. Due to the heterogeneous nature of PCa and their multifocality, tissue biopsies often yield false-negative results. To address these challenges, researchers are exploring innovative approaches, particularly in the realms of proteomics and metabolomics, to identify more reliable biomarkers and improve PCa diagnosis. Liquid biopsy (LB) has emerged as a promising non-invasive strategy for PCa early detection, biopsy selection, active surveillance for low-risk cases, and post-treatment and progression monitoring. Extracellular vesicles (EVs) are lipid-bilayer nanovesicles released by all cell types and play an important role in intercellular communication. EVs have garnered attention as a valuable biomarker resource in LB for PCa-specific biomarkers, enhancing diagnosis, prognostication, and treatment guidance. Metabolomics provides insight into the body's metabolic response to both internal and external stimuli, offering quantitative measurements of biochemical alterations. It excels at detecting non-genetic influences, aiding in the discovery of more accurate cancer biomarkers for early detection and disease progression monitoring. This review delves into the potential of EVs as a resource for LB in PCa across various clinical applications. It also explores cancer-related metabolic biomarkers, both within and outside EVs in PCa, and summarises previous metabolomic findings in PCa diagnosis and risk assessment. Finally, the article addresses the challenges and future directions in the evolving field of EV-based metabolomic analysis, offering a comprehensive overview of its potential in advancing PCa management.

The detection rate for prostate cancer in systematic and targeted prostate biopsy in biopsy-naive patients, according to the localization of the lesion at the mpMRI: A single-center retrospective observational study

OBJECTIVE

Evaluate the detection rates of systematic, targeted and combined cores at biopsy according to tumor positions in biopsy-naïve patients.

MATERIAL AND METHODS

A retrospective analysis of a single-center patient cohort (n = 501) that underwent transrectal prostate biopsy between January 2017 and December 2019 was performed. Multi-parametric MRI was executed as a prebiopsy investigation. Biopsy protocol included, for each patient, 12 systematic cores plus 3 to 5 targeted cores per lesion identified at the mpMRI. Pearson and McNemar chi-squared tests were used for statistical analysis to compare tumor location-related detection rates of systematic, targeted and combined (systematic + targeted) cores at biopsy.

RESULTS

Median age of patients was 70 years (IQR 62-72), with a median PSA of 8.5 ng/ml (IQR 5.7-15.6). Positive biopsies were obtained in 67.7% of cases. Overall, targeted cores obtained higher detection rates compared to systematic cores (54.3% vs. 43.1%, p < 0.0001). Differences in detection rates were, however, higher for tumors located at the apex (61.1% vs. 26.3%, p < 0.05) and anteriorly (44.4% vs. 19.3%, p < 0.05). Targeted cores similarly obtained higher detection rates in the posterior zone of the prostate gland for clinically significant prostate cancer. A poor agreement was reported between targeted and systematic cores for the apex and anterior zone of the prostate with, respectively κ = 0.028 and κ = -0.018.

CONCLUSION

A combined approach of targeted and systematic biopsy delivers the highest detection rate in prostate cancer (PCa). The location of the tumor could however greatly influence overall detection rates, indicating the possibility to omit (as for the base or posterior zone of the gland) or add (as for the apex or anterior zone of the gland) further targeted cores.

PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.

The impact of genomic biomarkers on a clinical risk prediction model for upgrading/upstaging among men with favorable-risk prostate cancer

BACKGROUND

The challenge of distinguishing indolent from aggressive prostate cancer (PCa) complicates decision-making for men considering active surveillance (AS). Genomic classifiers (GCs) may improve risk stratification by predicting end points such as upgrading or upstaging (UG/US). The aim of this study was to assess the impact of GCs on UG/US risk prediction in a clinicopathologic model.

METHODS

Participants had favorable-risk PCa (cT1-2, prostate-specific antigen [PSA] ≤15 ng/mL, and Gleason grade group 1 [GG1]/low-volume GG2). A prediction model was developed for 864 men at the University of California, San Francisco, with standard clinical variables (cohort 1), and the model was validated for 2267 participants from the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (cohort 2). Logistic regression was used to compute the area under the receiver operating characteristic curve (AUC) to develop a prediction model for UG/US at prostatectomy. A GC (Oncotype Dx Genomic Prostate Score [GPS] or Prolaris) was then assessed to improve risk prediction.

RESULTS

The prediction model included biopsy GG1 versus GG2 (odds ratio [OR], 5.83; 95% confidence interval [CI], 3.73-9.10); PSA (OR, 1.10; 95% CI, 1.01-1.20; per 1 ng/mL), percent positive cores (OR, 1.01; 95% CI, 1.01-1.02; per 1%), prostate volume (OR, 0.98; 95% CI, 0.97-0.99; per mL), and age (OR, 1.05; 95% CI, 1.02-1.07; per year), with AUC 0.70 (cohort 1) and AUC 0.69 (cohort 2). GPS was associated with UG/US (OR, 1.03; 95% CI, 1.01-1.06; p < .01) and AUC 0.72, which indicates a comparable performance to the prediction model.

CONCLUSIONS

GCs did not substantially improve a clinical prediction model for UG/US, a short-term and imperfect surrogate for clinically relevant disease outcomes.

Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis

Importance

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.

Objective

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.

Data Sources

PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022.

Study Selection

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.

Data Extraction and Synthesis

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.

Main Outcomes and Measures

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.

Results

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).

Conclusions and Relevance

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.

Significant prostate cancer risk after MRI-guided biopsy showing benign findings: Results from a cohort of 381 men

BACKGROUND

MRI-guided biopsy (MGB) contributes to the diagnosis of clinically significant Prostate Cancer (csPCa). However, there are no clear recommendations for the management of men after a negative MGB. The aim of this study was to assess the risk of csPCa after a first negative MGB.

METHODS

Between 2014 and 2020, we selected men with a PI-RADS score ≥ 3 on MRI and a negative MGB (showing benign findings) performed for suspected prostate cancer. MGB (targeted and systematic biopsies) was performed using fully integrated mobile fusion imaging (KOELIS). The primary endpoint was the rate of csPCa (defined as an ISUP grade ≥ 2) diagnosed after a first negative MGB.

RESULTS

A total of 381 men with a negative MGB and a median age of 65 (IQR: 59-69, range: 46-85) years were included. During the median follow-up of 31 months, 124 men (32.5%) had a new MRI, and 76 (19.9%) were referred for a new MGB, which revealed csPCa in 16 (4.2%) of them. We found no statistical difference in the characteristics of men diagnosed with csPCa compared with men with no csPCa after the second MGB.

CONCLUSION

We observed a risk of significant prostate cancer in 4% of men two years after a negative MRI-guided biopsy. Performing a repeat MRI could improve the selection of men who will benefit from a repeat MRI-guided biopsy, but a clear protocol is needed to follow these patients.

LEVEL OF EVIDENCE: 4

A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer

Objective

To explore the potential imaging biomarkers for predicting Traditional Chinese medicine (TCM) deficiency and excess syndrome in prostate cancer (PCa) patients by radiomics approach based on MR imaging.

Methods

A total of 121 PCa patients from 2 centers were divided into 1 training cohort with 84 PCa patients and 1 validation cohort with 37 PCa patients. The PCa patients were divided into deficiency and excess syndrome group according to TCM syndrome differentiation. Radiomic features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging and apparent diffusion coefficient images originated from diffusion-weighted imaging. A radiomic signature was constructed after reduction of dimension in training group by the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve.

Results

The radiomic scores of PCa with TCM excess syndrome group were statistically higher than those of PCa with TCM deficiency syndrome group among T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models. The area under ROC curves for T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models were 0.824, 0.824, 0.847 in the training cohort and 0.759, 0.750, 0.809 in the validation cohort, respectively. The apparent diffusion coefficient imaging model had the best discrimination in separating patients with TCM excess syndrome and deficiency syndrome, and its accuracy was 0.788, 0.778 in the training and validation cohort, respectively. The calibration curve demonstrated that there was a high consistency between the prediction of radiomic scores and the actual classification of TCM's deficiency and excess syndrome in PCa.

Conclusion

The radiomic signature based on MR imaging can be performed as a non-invasive, potential approach to discriminate TCM deficiency syndrome from excess syndrome in PCa, in which apparent diffusion coefficient imaging model has the best diagnostic efficiency.

Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review

The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.

68Ga-PSMA PET/CT-based multivariate model for highly accurate and noninvasive diagnosis of clinically significant prostate cancer in the PSA gray zone

BACKGROUND

The prostate-specific antigen (PSA) has been widely used in screening and early diagnosis of prostate cancer (PCa). However, in the PSA grey zone of 4-10 ng/ml, the sensitivity and specificity for diagnosing PCa are limited, resulting in considerable number of unnecessary and invasive prostate biopsies, which may lead to potential overdiagnosis and overtreatment. We aimed to predict clinically significant PCa (CSPCa) by combining the maximal standardized uptake value (SUVmax) based on 68Ga‑PSMA PET/CT and clinical indicators in men with gray zone PSA levels.

METHODS

81 patients with suspected PCa based on increased serum total PSA (TPSA) levels of 4 - 10 ng/mL who underwent transrectal ultrasound/magnetic resonance imaging (MRI)/PET fusion-guided biopsy were enrolled. Among them, patients confirmed by histopathology were divided into the CSPCa group and the non-CSPCa group, and data on PSA concentration, prostate volume (PV), PSA density (PSAD), free PSA (FPSA)/TPSA, Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) score, 68Ga-PSMA PET/CT imaging evaluation results and SUVmax were compared. Multivariate logistic regression analysis was performed to identify the independent predictors for CSPCa, thereby establishing a predictive model based on SUVmax that was evaluated by analyzing the receiver operating characteristic (ROC) curve and decision curve analysis.

RESULTS

Compared to non-CSPCa, CSPCa patients had smaller PVs (median, 31.40 mL), lower FPSA/TPSA (median, 0.12), larger PSADs (median, 0.21 ng/mL2) and higher PI-RADS scores (P < 0.05). The prediction model comprising 68Ga-PSMA PET/CT maximal standardized uptake value, PV and FPSA/TPSA had the highest AUC of 0.927 compared with that of other predictors alone (AUCs of 0.585 for PSA, 0.652 for mpMRI and 0.850 for 68Ga-PSMA PET/CT). The diagnostic sensitivity and specificity of the prediction model were 86.21% and 86.54%, respectively.

CONCLUSION

Given the low diagnostic accuracy of regular PSA tests, a new prediction model based on the 68Ga-PSMA PET/CT SUVmax, PV and FPSA/TPSA was developed and validated, and this model could provide a more satisfactory predictive accuracy for CSPCa. This study provides a noninvasive prediction model with high accuracy for the diagnosis of CSPCa in the PSA gray zone, thus may be better avoiding unnecessary biopsy procedures.

Evaluation of Multiparametric Magnetic Resonance Imaging and Correlation with Radical Prostatectomy Histopathology Specimen in Prostate Cancer

Magnetic resonance imaging (MRI) has shown a great potential in the evaluation and management of prostate cancer. In this study, we would like to evaluate the benefit of multiparametric MRI in the detection and localization of prostate cancer by comparing it with the gold standard of histopathology from radical prostatectomy. In this single-centre prospective study, 90 consecutive patients underwent radical prostatectomy from November 2016 to May 2018. All patients first underwent multiparametric (mp)-MRI, and all suspicious regions of interest were delineated and recorded on a 5-point scale as defined in prostate imaging reporting and data system version 2 (PI-RADS V2) score. All radical prostatectomy specimens, acquired after robotic radical prostatectomy with extended pelvic lymphadenectomy, were sent for histopathological examination (HPE). The mean age of the 90 patients was 65.3 years, and the mean serum prostate-specific antigen (PSA) was 16.9 ng/ml. The sensitivity and specificity of mp-MRI in the detection of the corresponding region of interest (ROI) on HPE were 67.4% and 89.3% respectively. Positive predictive value (PPV), negative predictive value (NPV), and accuracy of mp-MRI in the detection of corresponding ROI on HPE were 86.3%, 73.3%, and 78.3% respectively. The mp-MRI detected 96.8% solitary lesions and 61.7% multifocal lesions on the corresponding ROI on HPE. Multiparametric MRI has an excellent specificity and reasonable sensitivity for the diagnosis of prostate cancer. It is a good modality for the detection of solitary tumours, higher-grade tumours, detection of seminal vesicle invasion and extracapsular extension and helps in the decision-making process before radical prostatectomy, focal therapy or selecting an appropriate candidate for active surveillance.

Investigation and benchmarking of U-Nets on prostate segmentation tasks

In healthcare, a growing number of physicians and support staff are striving to facilitate personalized radiotherapy regimens for patients with prostate cancer. This is because individual patient biology is unique, and employing a single approach for all is inefficient. A crucial step for customizing radiotherapy planning and gaining fundamental information about the disease, is the identification and delineation of targeted structures. However, accurate biomedical image segmentation is time-consuming, requires considerable experience and is prone to observer variability. In the past decade, the use of deep learning models has significantly increased in the field of medical image segmentation. At present, a vast number of anatomical structures can be demarcated on a clinician's level with deep learning models. These models would not only unload work, but they can offer unbiased characterization of the disease. The main architectures used in segmentation are the U-Net and its variants, that exhibit outstanding performances. However, reproducing results or directly comparing methods is often limited by closed source of data and the large heterogeneity among medical images. With this in mind, our intention is to provide a reliable source for assessing deep learning models. As an example, we chose the challenging task of delineating the prostate gland in multi-modal images. First, this paper provides a comprehensive review of current state-of-the-art convolutional neural networks for 3D prostate segmentation. Second, utilizing public and in-house CT and MR datasets of varying properties, we created a framework for an objective comparison of automatic prostate segmentation algorithms. The framework was used for rigorous evaluations of the models, highlighting their strengths and weaknesses.

Predicting prostate cancer in men with PSA levels of 4-10 ng/mL: MRI-based radiomics can help junior radiologists improve the diagnostic performance

To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T₂-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.

Therapy decisions after diagnosis of prostate cancer in men with negative prostate MRI

BACKGROUND

To investigate the clinical implications of magnetic resonance imaging (MRI) negative prostate cancer (PCa) in a cohort of men undergoing transperineal prostate biopsy.

METHODS

We included all men without prior diagnosis of PCa undergoing transperineal template saturation ± fusion-guided targeted biopsy of the prostate between November 2014 and March 2018. Before biopsy, all patients underwent MRI and biopsies were performed irrespective of imaging results. Baseline characteristics, imaging, biopsy results, and follow-up information were retrieved from the patient charts. Patients were classified as either MRI negative (Prostate Imaging Reporting and Data System [PIRADS] ≤ 2) or positive (PIRADS ≥ 3). ISUP grade group 1 was defined as clinically nonsignificant (nsPCa) and ≥2 as clinically significant PCa (csPCa). Primary outcome was the individual therapeutic decision after diagnosis of PCa stratified according to MRI visibility. Secondary outcomes were the sensitivity and specificity of MRI, and the urooncological outcomes after radical prostatectomy (RP).

RESULTS

From 515 patients undergoing prostate biopsy, 171 (33.2%) patients had a negative and 344 (66.8%) a positive MRI. Pathology review stratified for MRI negative and positive cases revealed nsPCa in 27 (15.8%) and 32 (9.3%) and csPCa in 26 (15.2%) and 194 (56.4%) of the patients, respectively. The rate of active treatment in the MRI negative was lower compared with the MRI positive cohort (12.3% vs. 53.2%; odd ratio [OR] = 0.12; p < 0.001). While men with negative MRI were more likely to undergo active surveillance (AS) than MRI positive patients (18.1% vs. 10.8%; OR = 1.84; p = 0.027), they rarely underwent RP (6.4% vs. 40.7%, OR = 0.10; p < 0.001). Logistic regression revealed that a negative MRI was independently protective for active treatment (OR = 0.32, p = 0.014). The specificity, sensitivity, negative, and positive predictive value of MRI for detection of csPCa were 49.2%, 88.2%, 56.4%, and 84.8%, respectively. The rate of adverse clinicopathological outcome features (pT3/4, ISUP ≥4, or prostate-specific antigen [PSA]-persistence) following RP was 4.7% for men with MRI negative compared to 17.4% for men with MRI positive PCa (OR = 3.1, p = 0.19).

CONCLUSION

Only few men with MRI negative PCa need active cancer treatment at the time of diagnosis, while the majority opts for AS. Omitting prostate biopsies and performing a follow-up MRI may be a safe alternative to reduce the number of unnecessary interventions.

A dynamic-static combination model based on radiomics features for prostate cancer using multiparametric MRI

Objective. To propose a new dynamic multiparametric magnetic resonance imaging (mpMRI) radiomics method for the detection of prostate cancer (PCa), and establish a combined model using dynamic and static radiomics features based on this method.Approach. A total of 166 patients (82 PCa patients and 84 non-PCa patients) were enrolled in the study, and 31 872 mpMRI images were performed in a radiomics workflow. The whole prostate segmentation and traditional static radiomics features extraction were performed on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI,bvalue of 10, 50, 100, 150, 200, 400, 600, 800, 1000, 1500 s mm^-2respectively), apparent diffusion coefficient (ADC), and T2-weighted imaging (T2WI) sequences respectively. Through the building of eachb-value DWI model and the analysis of the static key radiomics features, three types of dynamic features called standard discrete (SD), parameter (P) and relative change rate (RCR) were constructed. And the b-value parameters used to construct dynamic features were divided into three groups ('Df_', 'Db_' and 'Da_'): the front part (10-200 s mm^-2), the back part (400-1500 s mm^-2), and all (10-1500 s mm^-2) of theb-values set, respectively. Afterwards, the dynamic mpMRI model and combined model construction were constructed, and the PCa discrimination performance of each model was evaluated.Main results.The models based on dynamic features showed good potential for PCa identification. Where, the results of Db_SD, Da_P and Db_P models were encouraging (test cohort AUCs: 90.78%, 87.60%, 86.3%), which was better than the commonly used ADC model (AUC of ADC was 75.48%). Among the combined models, the models using static features of T2WI and dynamic features performed the best. The AUC of Db_SD + T2WI, Db_P + T2WI and Db_RCR + T2WI model was 92.90%, 91.29% and 81.46%.Significance.The dynamic-static combination model based on dynamic mpMRI radiomics method has a good effect on the identification of PCa. This method has broad application prospects in PCa individual diagnosis management.

Role of MRI in diagnosis of prostate cancer and correlation of results with transrectal ultrasound guided biopsy “TRUS”

Background

Prostate cancer is the most common cancer in elderly men, and the second leading cause of cancer-related death in developed countries. For a long time, TRUS is used in screening, diagnosis of prostate lesions. Recently the implementation of multi parametric MRI into a screening program currently seems to be the most promising technique to improve the early detection of prostate cancer.

Results

Thirty Patients were referred from urological outpatient clinics complaining of urological symptoms (dysuria, frequency and urine retention). The study was carried, and the patients were submitted to Ultrasonography, conventional magnetic resonance, diffusion weighted images and MR spectroscopy techniques, these results were correlated with histopathological data. In this study Conventional MRI has moderate sensitivity 81.8% and low specificity 37.3% in diagnosing prostate malignancy. Using of mpMRI combination of diffusion-weighted, Dynamic contrast enhanced and MR spectroscopic imaging is a promising approach for discriminating between benign and malignant lesions in the PZ and increase sensitivity 100% and specificity 96.6% in diagnosing prostate malignancy.

Conclusions

The standard for the definitive diagnosis of prostate cancer is trans-rectal ultrasound biopsy. However, TRUS guided biopsy has a significant sampling error and can miss up to 30% of cancers and may show underestimation of Gleason grade, especially in anteriorly located tumors. It may lead to an increase in complications. MRI has an essential role to play in making safer in diagnosis. It can aid in staging also and surgery or radiation treatment planning. Although T2W MRI has been used widely for diagnosis on the basis of its excellent soft tissue resolution, but its accuracy for the detection and localization of cancer prostate is unsatisfactory. The implementation of multi parametric MRI: MR spectroscopy, Dynamic contrast enhanced and diffusion weighted imaging into a diagnosis program improve the diagnostic performance. These advances are beginning to translate into better treatment selection and more accurate image-guided therapies. In addition, early detection of local recurrence.

The Prostate Health Index and multi-parametric MRI improve diagnostic accuracy of detecting prostate cancer in Asian populations

PURPOSE

The aim of this study was to evaluate the effectiveness of the Prostate Health Index (PHI) and prostate multi-parametric magnetic resonance imaging (mpMRI) in predicting prostate cancer (PCa) and clinically significant prostate cancer (csPCa) during initial prostate biopsy.

MATERIALS AND METHODS

In total, 343 patients underwent initial prostate biopsy and were screened by use of PHI and prostate-specific antigen (PSA) levels between April 2019 and July 2021. A subgroup of 232 patients also underwent prostate mpMRI. Logistic regression analysis was performed to evaluate the accuracies of PSA, PHI, and mpMRI as predictors of PCa or csPCa. These predictive accuracies were quantified by using the area under the receiver operating characteristic curve. The different predictive models were compared using the DeLong test.

RESULTS

Logistic regression showed that age, PSA, PHI, and prostate volume were significant predictors of both PCa and csPCa. In the mpMRI subgroup, age, PSA level, PHI, prostate volume, and mpMRI were predictors of both PCa and csPCa. The PHI (area under the curve [AUC]=0.693) was superior to the PSA level (AUC=0.615) as a predictor of PCa (p=0.038). Combining PHI and mpMRI showed the most accurate prediction of both PCa and csPCa (AUC=0.833, 0.881, respectively).

CONCLUSIONS

The most accurate prediction of both PCa and csPCa can be performed by combining PHI and mpMRI. In the absence of mpMRI, PHI is superior to PSA alone as a predictor of PCa, and adding PHI to PSA can increase the detection rate of both PCa and csPCa.

Combination of biparametric magnetic resonance imaging with prostate-specific antigen density to stratify the risk of significant prostate cancer: Initial biopsy and long-term follow-up results

OBJECTIVES

To assess whether the combination of biparametric magnetic resonance imaging with prostate-specific antigen density can properly stratify the risk of significant prostate cancer in patients undergoing prostate biopsies and how this approach affects the detection of prostate cancer during follow-up in patients who do not undergo prostate biopsy.

METHODS

In total, 411 biopsy-naïve patients who had elevated prostate-specific antigen levels and then underwent biparametric magnetic resonance imaging for suspicious prostate cancer were analyzed: 203 patients underwent prostate biopsies, whereas 208 patients did not. Significant prostate cancer detection rates stratified by the combination of Prostate Imaging Reporting and Data System score and prostate-specific antigen density were assessed in patients who underwent prostate biopsies. The cumulative incidence of prostate cancer detection during the follow-up was assessed in patients who omitted biopsy.

RESULTS

The negative predictive value for significant prostate cancer was 89% for Prostate Imaging Reporting and Data System scores 1-3, which increased to 97% when prostate-specific antigen density <0.15 ng/ml/cm³ was combined. Among patients who did not undergo biopsy, patients with Prostate Imaging Reporting and Data System scores 1-3 plus prostate-specific antigen density <0.15 ng/ml/cm³ included significantly less cases in which significant prostate cancer was detected during the follow-up, compared with the others (3.2% versus 17% at 36 months).

CONCLUSIONS

Restriction of prostate biopsies to patients with Prostate Imaging Reporting and Data System scores 4-5 or prostate-specific antigen density ≥0.15 ng/ml/cm³ proved to be the good biopsy strategy, effectively balancing risks and benefits.

Pre-operative magnetic resonance imaging can predict prostate cancer with risk for positive surgical margins

Purpose

Analysis of patients with pre-operative 3 T multiparametric prostate MRI (mpMRI) to determine reliable MRI-based risk predictors of patients at risk for positive surgical margins (PSM) in robotic assisted radical prostatectomy (RPE).

Methods

Consecutive patients with 3 T mpMRI and subsequent RPE from 01/2015 to 12/2018 were retrospectively included. Patients were compared regarding clinical and MRI related parameters such as length of capsular tumor contact (LCC) and distance to the membranous urethra (UD).

Results

Forty-nine of 179 patients (27%) had PSM in 70 different localizations, with the majority located at the capsule (57%, 40/70), mostly apical and/or posterior. The second most often PSM occurred at the apical urethra (22%, 15/70). PCA was visible on mpMRI at the localization of PSM in 93% at the capsule and in 80% at the urethra. PSA, PI-RADS classification, extraprostatic extension (EPE), and seminal vesicles infiltration (SVI) on MRI were significantly higher / more frequent in patients with PSM. LCC (AUC 0.710), EPE (AUC 0.693), and UD (1-AUC 0.673) predicted PSM (overall). An UD of ≤ 3.5 mm showed the highest accuracy of 95% (J = 0.946) for PSM at the urethra and a LCC of ≥ 22.5 mm with 77% (J = 0.378) for PSM at the capsule.

Conclusion

PSM occurred mostly in the apex and/or posteriorly at the capsule or at the apical urethra. LCC was the best MRI predictor for PSM at the capsule and UD for tumors with PSM at the apical urethra. Using these MRI parameters readers might pre-operatively determine PCA localizations at risk for PSM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-022-03543-z.

Recent advances of multi-modal ultrasound in image-guided prostate-targeted biopsy

Prostate-targeted biopsy is usually the preferred method over systematic biopsy because it can effectively detect prostate cancer using only a few puncture cores with fewer complications. With the development of ultrasound, it has gained multimodal technological upgrades, such as the emergence of contrast-enhanced ultrasound, ultrasound elastography, and three-dimensional ultrasonography. Moreover, multimodal ultrasound has played an increasingly significant role in prostate-targeted biopsies.

Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review

Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.

Evidence-based guideline recommendations on multiparametric magnetic resonance imaging in the diagnosis of clinically significant prostate cancer: A Cancer Care Ontario updated clinical practice guideline

INTRODUCTION

This clinical practice guideline is based on a systematic review to assess the use of multiparametric magnetic resonance imaging (mpMRI) in the diagnosis of clinically significant prostate cancer (csPCa) for biopsy-naive men and men with a prior negative transrectal ultrasound-guided systematic biopsy (TRUS-SB) at elevated risk.

METHODS

The methods of the clinical practice guideline included searches to September of 2020 of MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials. Internal and external reviews were conducted.

RESULTS

The recommendations are:Recommendation 1: For biopsy-naive patients at elevated risk of csPCa, mpMRI is recommended prior to biopsy in patients who are candidates for curative management with suspected clinically localized prostate cancer.- If the mpMRI is positive, mpMRI-targeted biopsy (TB) and TRUS-SB should be performed together to maximize detection of csPCa.- If the mpMRI is negative, consider forgoing any biopsy after discussion of the risks and benefits with the patient as part of shared decision-making and ongoing followup.Recommendation 2: In patients who had a prior negative TRUS-SB and demonstrate a high risk of having csPCa in whom curative management is being considered:- mpMRI should be performed.- If the mpMRI is positive, targeted biopsy should be performed. Concomitant TRUS-SB can be considered depending on the patient's risk profile and time since prior TRUS-SB biopsy.- If the mpMRI is negative, consider forgoing a TRUS-SB only after discussion of the risks and benefits with the patient as part of shared decision-making and ongoing followup.Recommendation 3: mpMRI should be performed and interpreted in compliance with the current Prostate Imaging Reporting & Data System (PI-RADS) guidelines.

Preoperative MRI PI-RADS scores are associated with prostate cancer upstaging on surgical pathology

INTRODUCTION

Prostate Imaging Reporting and Data System (PI-RADS) scores can help identify clinically significant prostate cancer and improve patient selection for prostate biopsies. However, the role of PI-RADS scores in patients already diagnosed with prostate cancer remains unclear. The purpose of this study was to evaluate the association of PI-RADS scores with prostate cancer upstaging. Upstaging on final pathology harbors a higher risk for biochemical recurrence with important implications for additional treatments, morbidity, and mortality.

METHODS

All patients from a single high-volume institution who underwent a prostate multiparametric magnetic resonance imaging and radical prostatectomy between 2016 and 2020 were included in this retrospective analysis. Univariable and multivariable analyses were conducted to investigate potential associations with upstaging events, defined by pT3, pT4, or N1 on final pathology. A logistic regression model was constructed for the prediction of upstaging events based on PI-RADS score, prostate-specific antigen density (PSA-D), and biopsy Gleason grade groups. We built receiver operative characteristic (ROC) curves to measure the area under the curve of different predictive models.

RESULTS

Two hundred and ninety-four patients were included in the final analysis. Upstaging events occurred in 137 (46.5%) of patients. On univariable analysis, patients who were upstaged on final pathology had significantly higher PI-RADS scores (odds ratio [OR] 2.34 95% confidence interval [CI] 1.64-3.40, p < 0.001) but similar PSA-D (OR 2.70 95% 0.94-8.43, p = 0.188) compared with patients who remained pT1 or pT2 on final pathology. On multivariable analysis, PI-RADS remained independently significantly associated with upstaging, suggesting it is an independent risk predictor for upstaging. Lymph node metastasis only occurred in patients with PI-RADS 4 or 5 lesions (n = 15). Our model using PSA-D, biopsy Gleason grade, and PI-RADS had a predictive AUC of 0.69 for upstaging events, an improvement from 0.59 using biopsy Gleason grade alone.

CONCLUSION

PI-RADS scores are independent predictors for upstaging events and may play an important role in forecasting biochemical recurrence and lymph node metastasis. Modern nomograms should be updated to include PI-RADS to predict lymph node metastases and the likelihood of biochemical recurrence more accurately.

Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate

BACKGROUND

Early diagnosis and treatment of prostate cancer (PCa) can be curative; however, prostate-specific antigen is a suboptimal screening test for clinically significant PCa. While prostate magnetic resonance imaging (MRI) has demonstrated value for the diagnosis of PCa, the acquisition time is too long for a first-line screening modality.

PURPOSE

To accelerate prostate MRI exams, utilizing a variational network (VN) for image reconstruction.

STUDY TYPE

Retrospective.

SUBJECTS

One hundred and thirteen subjects (train/val/test: 70/13/30) undergoing prostate MRI.

FIELD STRENGTH/SEQUENCE

3.0 T; a T2 turbo spin echo (TSE) T2-weighted image (T2WI) sequence in axial and coronal planes, and axial echo-planar diffusion-weighted imaging (DWI).

ASSESSMENT

Four abdominal radiologists evaluated the image quality of VN reconstructions of retrospectively under-sampled biparametric MRIs (bp-MRI), and standard bp-MRI reconstructions for 20 test subjects (studies). The studies included axial and coronal T2WI, DWI B50 seconds/mm² and B1000 seconds/mm (4-fold T2WI, 3-fold DWI), all of which were evaluated separately for image quality on a Likert scale (1: non-diagnostic to 5: excellent quality). In another 10 test subjects, three readers graded lesions on bp-MRI-which additionally included calculated B1500 seconds/mm² , and apparent diffusion coefficient map-according to the Prostate Imaging Reporting and Data System (PI-RADS v2.1), for both VN and standard reconstructions. Accuracy of PI-RADS ≥3 for clinically significant cancer was computed. Projected scan time of the retrospectively under-sampled biparametric exam was also computed.

STATISTICAL TESTS

One-sided Wilcoxon signed-rank test was used for comparison of image quality. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for lesion detection and grading. Generalized estimating equation with cluster effect was used to compare differences between standard and VN bp-MRI. A P-value of <0.05 was considered statistically significant.

RESULTS

Three of four readers rated no significant difference for overall quality between the standard and VN axial T2WI (Reader 1: 4.00 ± 0.56 (Standard), 3.90 ± 0.64 (VN) P = 0.33; Reader 2: 4.35 ± 0.74 (Standard), 3.80 ± 0.89 (VN) P = 0.003; Reader 3: 4.60 ± 0.50 (Standard), 4.55 ± 0.60 (VN) P = 0.39; Reader 4: 3.65 ± 0.99 (Standard), 3.60 ± 1.00 (VN) P = 0.38). All four readers rated no significant difference for overall quality between standard and VN DWI B1000 seconds/mm² (Reader 1: 2.25 ± 0.62 (Standard), 2.45 ± 0.75 (VN) P = 0.96; Reader 2: 3.60 ± 0.92 (Standard), 3.55 ± 0.82 (VN) P = 0.40; Reader 3: 3.85 ± 0.72 (Standard), 3.55 ± 0.89 (VN) P = 0.07; Reader 4: 4.70 ± 0.76 (Standard); 4.60 ± 0.73 (VN) P = 0.17) and three of four readers rated no significant difference for overall quality between standard and VN DWI B50 seconds/mm² (Reader 1: 3.20 ± 0.70 (Standard), 3.40 ± 0.75 (VN) P = 0.98; Reader 2: 2.85 ± 0.81 (Standard), 3.00 ± 0.79 (VN) P = 0.93; Reader 3: 4.45 ± 0.72 (Standard), 4.05 ± 0.69 (VN) P = 0.02; Reader 4: 4.50 ± 0.69 (Standard), 4.45 ± 0.76 (VN) P = 0.50). In the lesion evaluation study, there was no significant difference in the number of PI-RADS ≥3 lesions identified on standard vs. VN bp-MRI (P = 0.92, 0.59, 0.87) with similar sensitivity and specificity for clinically significant cancer. The average scan time of the standard clinical biparametric exam was 11.8 minutes, and this was projected to be 3.2 minutes for the accelerated exam.

DATA CONCLUSION

Diagnostic accelerated biparametric prostate MRI exams can be performed using deep learning methods in <4 minutes, potentially enabling rapid screening prostate MRI.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 5.

The Additive Diagnostic Value of Prostate-specific Membrane Antigen Positron Emission Tomography Computed Tomography to Multiparametric Magnetic Resonance Imaging Triage in the Diagnosis of Prostate Cancer (PRIMARY): A Prospective Multicentre Study

BACKGROUND

Multiparametric magnetic resonance imaging (MRI) is validated for the detection of clinically significant prostate cancer (csPCa), although patients with negative/equivocal MRI undergo biopsy for false negative concerns. In addition, ⁶⁸Ga-PSMA-11 positron emission tomography/computed tomography (prostate-specific membrane antigen [PSMA]) may also identify csPCa accurately.

OBJECTIVE

This trial aimed to determine whether the combination of PSMA + MRI was superior to MRI in diagnostic performance for detecting csPCa.

DESIGN, SETTING, AND PARTICIPANTS

A prospective multicentre phase II imaging trial was conducted. A total of 296 men were enrolled with suspected prostate cancer, with no prior biopsy or MRI, recent MRI (6 mo), and planned transperineal biopsy based on clinical risk and MRI. In all, 291 men underwent MRI, pelvic-only PSMA, and systematic ± targeted biopsy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Sensitivity, specificity, and predictive values (negative predictive value [NPV] and positive predictive value) for csPCa were determined for MRI, PSMA, and PSMA + MRI. PSMA + MRI was defined as negative for PSMA negative Prostate Imaging Reporting and Data System (PI-RADS) 2/3 and positive for either MRI PI-RADS 4/5 or PSMA positive PI-RADS 2/3; csPCa was any International Society of Urological Pathology (ISUP) grade group ≥2 malignancy.

RESULTS AND LIMITATIONS

Of the patients, 56% (n = 162) had csPCa; 67% had PI-RADS 3-5, 73% were PSMA positive, and 81% were combined PSMA + MRI positive. Combined PSMA + MRI improved NPV compared with MRI alone (91% vs 72%, test ratio = 1.27 [1.11-1.39], p < 0.001). Sensitivity also improved (97% vs 83%, p < 0.001); however, specificity was reduced (40% vs 53%, p = 0.011). Five csPCa cases were missed with PSMA + MRI (four ISUP 2 and one ISUP 3). Of all men, 19% (56/291) were PSMA + MRI negative (38% of PI-RADS 2/3) and could potentially have avoided biopsy, risking delayed csPCa detection in 3.1% men with csPCa (5/162) or 1.7% (5/291) overall.

CONCLUSIONS

PSMA + MRI improved NPV and sensitivity for csPCa in an MRI triaged population. Further randomised studies will determine whether biopsy can safely be omitted in men with a high clinical suspicion of csPCa but negative combined imaging.

PATIENT SUMMARY

The combination of magnetic resonance imaging (MRI) + prostate-specific membrane antigen positron emission tomography reduces false negatives for clinically significant prostate cancer (csPCa) compared with MRI, potentially allowing a reduction in the number of prostate biopsies required to diagnose csPCa.

A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks

Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detection. In this work, we have statically applied five most frequently used augmentation techniques (random rotation, horizontal flip, vertical flip, random crop, and translation) to prostate diffusion-weighted magnetic resonance imaging training dataset of 217 patients separately and evaluated the effect of each method on the accuracy of prostate cancer detection. The augmentation algorithms were applied independently to each data channel and a shallow as well as a deep convolutional neural network (CNN) was trained on the five augmented sets separately. We used area under receiver operating characteristic (ROC) curve (AUC) to evaluate the performance of the trained CNNs on a separate test set of 95 patients, using a validation set of 102 patients for finetuning. The shallow network outperformed the deep network with the best 2D slice-based AUC of 0.85 obtained by the rotation method.

Does PSA level affect the choice of prostate puncture methods among MRI-ultrasound fusion targeted biopsy, transrectal ultrasound systematic biopsy or the combination of both?

OBJECTIVES

To explore whether prostate-specific antigen (PSA) affects the choice of prostate puncture methods by comparing MRI-ultrasound fusion targeted biopsy (MRI-TBx) with transrectal ultrasound systematic biopsy (TRUS-SBx) in the detection of prostate cancer (PCa), clinically significant prostate cancer (csPCa) and non-clinically significant prostate cancer (nsPCa) in different PSA groups (<10.0,10.0-20.0 and>20.0 ng ml-1).

METHODS

A total of 190 patients with 215 lesions who underwent both MRI-TBx and TRUS-SBx were included in this retrospective study. PSA was measured pre-operatively and stratified to three levels. The detection rates of PCa, csPCa and nsPCa through different methods (MRI-TBx, TRUS-SBx, or MRI-TBx +TRUS SBx) were compared with stratification by PSA.

RESULTS

Among the 190 patients, the histopathological results revealed PCa in 126 cases, including 119 csPCa. In PSA <10.0 ng ml-1 group, although the detection rates of PCa and csPCa by MRI-TBx were higher than those of TRUS-SBx, no significant differences were observed (p = 0.741; p = 0.400). In PSA 10.0-20.0 ng ml-1 group, difference between the detection rate of csPCa with TRUS-SBx and the combined method was statistically significant (p = 0.044). As for PSA >20.0 ng ml-1, MRI-TBx had a higher csPCa rate than TRUS-SBx with no statistical significance noted (p = 0.600).

CONCLUSION

MRI-TBx combined with TRUS-SBx could be suitable as a standard detection approach for csPCa in patients with PSA 10.0-20.0 ng ml-1. As for PSA >20.0 and <10.0 ng ml-1, both MRI-TBx and TRUS-SBx might provide effective solutions for tumor detection.

ADVANCES IN KNOWLEDGE

This study gives an account of choosing appropriate prostate puncture methods through PSA level.

The current value of histological findings in negative prostate biopsies to predict the future risk of clinically significant prostate cancer

BACKGROUND

Repeat prostate biopsy (PBx) is recommended under persistent suspicion of prostate cancer (PCa) or in the face of the following findings: atypical small acinar proliferation (ASAP); extense (≥3 biopsy sites) high-grade prostatic intraepithelial neoplasia (HGPIN); or HGPIN with atypical glands; suspicious for adenocarcinoma (PIN-ATYP). Nowadays; multiparametric magnetic resonance imaging (mpMRI) and mpMRI targeted PBx (MRI-TBx) are recommended in repeat PBx. Our objective was to analyze the current value of ASAP; mHGPIN; PIN-ATYP and other histological findings to predict clinically significant PCa (csPCa) risk.

METHODS

Retrospective analysis of 377 repeat PBxs. MRI-TBx was performed when Prostate Imaging-Reporting and Data System (PI-RADS) score >3 and 12-core transrectal ultrasound (TRUS) systematic PBx when ≤2. ASAP; HGPIN; mHGPIN; PIN-ATYP; and 8 other histological findings were prospectively reported in negative PBx. CsPCa was defined as ISUP group grade >2.

RESULTS

Incidence of ASAP; multifocal HGPIN (mHGPIN) and PINATYP was 4.2%; 39.7% and 3.7% respectively; and csPCa rate was statistically similar among men with these histological findings. However; the rate of csPCa was 22.2% when proliferative inflammatory atrophy (PIA) was present; and 36.1% when it was not. PIA was the only histological finding which predicted lower risk of csPCa; with an OR of 0.54 (95%CI: 0.308-0.945; P = .031). In addition; PIA was an independent predictor of a model combining clinical variables and mpMRI which reached area under de ROC curve of 0.86 (95%CI: 0.83-0.90).

CONCLUSION

PIA emerged as the only predictive histological finding of csPCa risk and can contribute to a predictive model. mHGPIN failed to predict csPCa risk. The low incidence of ASAP (4.2%) and PIN-ATYP (3.7%) prevented us from drawing conclusions.

Preoperative histogram parameters of dynamic contrast-enhanced MRI as a potential imaging biomarker for assessing the expression of Ki-67 in prostate cancer

Purpose

To investigate whether preoperative histogram parameters of dynamic contrast‐enhanced MRI (DCE‐MRI) can assess the expression of Ki‐67 in prostate cancer (PCa).

Materials and methods

A consecutive series of 76 patients with pathology‐proven PCa who underwent routine DCE‐MRI scans were retrospectively recruited. Quantitative parameters including the volume transfer constant (K^trans), rate contrast (K_ep), extracellular‐extravascular volume fraction (V_e), and plasma volume (V_p) by outlining the three‐dimensional volume of interest (VOI) of all lesions were processed. Then, the histogram analyses of these quantitative parameters were performed. The Spearman rank correlation analysis was used to evaluate the correlation of these parameters and Ki‐67 expression of PCa. Receiver operating characteristic (ROC) curve analysis was adopted to evaluate the efficacy of these quantitative histogram parameters in identifying high Ki‐67 expression from low Ki‐67 expression of PCa.

Results

Eighty‐eight PCa lesions were enrolled in this study, including 31 lesions with high Ki‐67 expression and 57 lesions with low Ki‐67 expression. The median, mean, 75th percentile, and 90th percentile derived from K^trans and K_ep had a moderately positive correlation with Ki‐67 expression (r = 0.361–0.450, p < 0.05), in which both the median and mean of K^trans had the highest positive correlation (r = 0.450, p < 0.05). The diagnostic efficacy of the K^trans median, mean, 75th percentile, and 90th percentile, along with the K_ep‐based median and mean was assessed by the ROC curve. The area under the curve (AUC) of the mean for K^trans was the highest (0.826). When the cut‐off of the mean for K^trans was ≥0.47/min, its Youden index, sensitivity, and specificity were 0.625, 0.871, and 0.754, respectively. The AUC of the median of K_ep was the lowest (0.772).

Conclusion

The histogram of DCE‐MRI quantitative parameters is correlated with Ki‐67 expression, which has the potential to noninvasively assess the expression of Ki‐67 with patients of PCa.

The utility of magnetic resonance imaging in prostate cancer diagnosis in the Australian setting

Objectives

To investigate the utility of Magnetic Resonance Imaging (MRI) for prostate cancer diagnosis in the Australian setting.

Patients and methods

All consecutive men who underwent a prostate biopsy (transperineal or transrectal) at Royal Melbourne Hospital between July 2017 to June 2019 were included, totalling 332 patients. Data were retrospectively collected from patient records. For each individual patient, the risk of prostate cancer diagnosis at biopsy based on clinical findings was determined using the European Randomized study of Screening for Prostate Cancer (ERSPC) risk calculator, with and without incorporation of MRI findings.

Results

MRI has good diagnostic accuracy for clinically significant prostate cancer. A PI‐RADS 2 or lower finding has a negative predictive value of 96% for clinically significant cancer, and a PI‐RADS 3, 4 or 5 MRI scan has a sensitivity of 93%. However, MRI has a false negative rate of 6.5% overall for clinically significant prostate cancers. Pre‐ biopsy MRI may reduce the number of unnecessary biopsies, as up to 50.0% of negative or ISUP1 biopsies have MRI PI‐RADS 2 or lower. Incorporation of MRI findings into the ERSPC calculator improved predictive performance for all prostate cancer diagnoses (AUC 0.77 vs 0.71, P = .04), but not for clinically significant cancer (AUC 0.89 vs 0.87, P = .37).

Conclusion

MRI has good sensitivity and negative predictive value for clinically significant prostate cancers. It is useful as a pre‐biopsy tool and can be used to significantly reduce the number of unnecessary prostate biopsies. However, MRI does not significantly improve risk predictions for clinically significant cancers when incorporated into the ERSPC risk calculator.

The current value of histological findings in negative prostate biopsies to predict the future risk of clinically significant prostate cancer

INTRODUCTION

Repeat prostate biopsy (PBx) is recommended under persistent suspicion of prostate cancer (PCa) or in the face of the following findings: atypical small acinar proliferation (ASAP), extense (≥3 biopsy sites) high-grade prostatic intraepithelial neoplasia (HGPIN), or HGPIN with atypical glands, suspicious for adenocarcinoma (PIN-ATYP). Nowadays, multiparametric magnetic resonance imaging (mpMRI) and mpMRI targeted PBx (MRI-TBx) are recommended in repeat PBx. Our objective was to analyze the current value of ASAP, mHGPIN, PIN-ATYP and other histological findings to predict clinically significant PCa (csPCa) risk.

METHODS

Retrospective analysis of 377 repeat PBxs. MRI-TBx was performed when Prostate Imaging-Reporting and Data System (PI-RADS) score>3 and 12-core transrectal ultrasound (TRUS) systematic PBx when≤2. ASAP, HGPIN, mHGPIN, PIN-ATYP, and 8 other histological findings were prospectively reported in negative PBx. CsPCa was defined as ISUP group grade>2.

RESULTS

Incidence of ASAP, multifocal HGPIN (mHGPIN) and PINATYP was 4.2%, 39.7% and 3.7% respectively, and csPCa rate was statistically similar among men with these histological findings. However, the rate of csPCa was 22.2% when proliferative inflammatory atrophy (PIA) was present, and 36.1% when it was not. PIA was the only histological finding which predicted lower risk of csPCa, with an OR of .54 (95% CI: .308-.945, P=.031). In addition, PIA was an independent predictor of a model combining clinical variables and mpMRI which reached area under de ROC curve of .86 (95% CI: .83-.90).

CONCLUSIONS

PIA emerged as the only predictive histological finding of csPCa risk and can contribute to a predictive model. mHGPIN failed to predict csPCa risk. The low incidence of ASAP (4.2%) and PIN-ATYP (3.7%) prevented us from drawing conclusions.

Magnetic resonance imaging-guided prostate biopsy-A review of literature

Objective

Multiparametric magnetic resonance imaging (MP-MRI) helps to identify lesion of prostate with reasonable accuracy. We aim to describe the various uses of MP-MRI for prostate biopsy comparing different techniques of MP-MRI guided biopsy.

Materials and methods

A literature search was performed for "multiparametric MRI", "MRI fusion biopsy", "MRI guided biopsy", "prostate biopsy", "MRI cognitive biopsy", "MRI fusion biopsy systems", "prostate biopsy" and "cost analysis". The search operation was performed using the operator "OR" and "AND" with the above key words. All relevant systematic reviews, original articles, case series, and case reports were selected for this review.

Results

The sensitivity of MRI targeted biopsy (MRI-TB) is between 91%-93%, and the specificity is between 36%-41% in various studies. It also has a high negative predictive value (NPV) of 89%-92% and a positive predictive value (PPV) of 51%-52%. The yield of MRI fusion biopsy (MRI-FB) is similar, if not superior to MR cognitive biopsy. In-bore MRI-TB had better detection rates compared to MR cognitive biopsy, but were similar to MR fusion biopsy.

Conclusions

The use of MRI guidance in prostate biopsy is inevitable, subject to availability, cost, and experience. Any one of the three modalities (i.e. MRI cognitive, MRI fusion and MRI in-bore approach) can be used. MRI-FB has a fine balance with regards to accuracy, practicality and affordability.

Consecutive transperineal prostatic template biopsies employing cognitive and systematic approach: a single center study

INTRODUCTION

The role of transperineal template biopsy for prostate cancer diagnosis is well established. Pre-biopsy multiparametric magnetic resonance imaging (MRI) is used in most centers for planning of prostate biopsies and staging. Cognitive and software fusion techniques are increasingly getting popular.

METHODS

We retrospectively reviewed patients who underwent transperineal template biopsies from January 2016 till December 2018. This included patients on active surveillance, previous negative transrectal ultrasonography biopsies with persistently raised prostate-specific antigen/abnormal prostate on digital rectal examination and de-novo template biopsies. Two specialist uro-radiologists reported all the scans and the biopsies were performed by one experienced urologist. The cognitive biopsies were performed for PIRADS 3-5 lesions on MRI. Total of 330 patients underwent transperineal template biopsies and cognitive target biopsies were carried out in 75 patients who were included in the study. We evaluated the results as positive/negative cognitive biopsies and also according to the PIRAD scoring. Only the patients with prostate cancer on template biopsy histology were included.

RESULTS

Fifty-seven (76%) of the cognitive biopsies were positive out of total 75.[Table: see text].

CONCLUSIONS

Combined cognitive and systematic biopsies have excellent diagnostic rate especially for PIRAD 4-5 MRI areas.

PI-RADS: what is new and how to use it

The prostate imaging reporting and data system (PI-RADS) has revolutionized the use of magnetic resonance imaging (MRI) for the management of prostate cancer (PCa). The most recent version 2.1, PI-RADS v2.1, provides specific refinements in the performance, relaxing some recommendations which were not found to be helpful, while reinforcing and clarifying others. The interpretation of T2-weighted imaging (T2WI) in the transition zone (TZ), and the overall assessment of TZ nodules, now allows for a clearer distinction between those which are clearly benign and those which might warrant tissue sampling. Additional changes also resolve discrepancies in T2WI and diffusion-weighted imaging (DWI) of the peripheral zone (PZ). PI-RADS v2.1 is a simpler, more straightforward, and more reproducible method to better communicate between physicians regarding findings on prostate MRI.

Who Can Avoid Biopsy of Magnetic Resonance Imaging-Negative Lobes without Compromising Significant Cancer Detection among Men with Unilateral Magnetic Resonance Imaging-Positive Lobes?

OBJECTIVES

To assess whether biopsy of multiparametric magnetic resonance imaging (MRI)-negative lobes can be avoided without compromising significant cancer (SC) detection among men with unilateral MRI-positive lobes.

METHODS

From April 2013 to April 2019, 322 men with elevated prostate-specific antigen (PSA <20 ng/mL) and unilateral MRI-positive lobes underwent targeted 4-core and systematic 14-core biopsy. MRI findings were prospectively collected and evaluated according to the Prostate Imaging-Reporting and Data System (PI-RADS) version 2, and scores ≥3 were considered positive. SC was defined as Gleason score ≥3 + 4 or maximal cancer length ≥5 mm. We developed predictive models of overall cancer and SC in MRI-negative lobes and evaluated the performance of these models.

RESULTS

Detection rates of overall cancer/SC were 69%/61% for the overall cohort, 58%/48% for MRI-positive lobes, and 36%/20% for MRI-negative lobes. Age ≥75 years, PSA density ≥0.3, and PI-RADS ≥4 were independently predictive of both overall cancer and SC in MRI-negative lobes; 1 point was assigned for each risk factor, and the predictive score was defined as the sum of points (0-3) for both overall cancer and SC. Areas under the curve of the model for overall cancer/SC were 0.67/0.71. In the decision curve analysis, the model was of value above the threshold probability of 13%/6% for detecting overall cancer/SC in MRI-negative lobes. Of 40 men with score 0, overall cancer/SC was detected in the MRI-negative lobe in 4 (10%)/1 (2.5%).

CONCLUSION

Biopsies of MRI-negative lobes may be avoided without compromising SC detection using our predictive model.

Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: A systematic review and meta-analysis

OBJECTIVE

To evaluate Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions' impact on the diagnostic test accuracy (DTA) of MRI for prostate cancer (PC) and to derive the prevalence of PC within each PI-RADS category.

METHODS

MEDLINE and Embase were searched until April 10, 2020 for studies reporting on the DTA of MRI by PI-RADS category. Accuracy metrics were calculated using a bivariate random-effects meta-analysis with PI-RADS three lesions treated as a positive test, negative test, and excluded from the analysis. Differences in DTA were assessed utilizing meta-regression. PC prevalence within each PI-RADS category was estimated with a proportional meta-analysis.

RESULTS

In total, 26 studies reporting on 12,913 patients (4,853 with PC) were included. Sensitivities for PC in the positive, negative, and excluded test groups were 96% (95% confidence interval [CI] 92-98), 82% (CI 75-87), and 95% (CI 91-97), respectively. Specificities for the positive, negative, and excluded test groups were 33% (CI 23-44), 71% (CI 62-79), and 52% (CI 37-66), respectively. Meta-regression demonstrated higher sensitivity (p < 0.001) and lower specificity (p < 0.001) in the positive test group compared to the negative group. Clinically significant PC prevalences were 5.9% (CI 0-17.1), 11.4% (CI 6.5-17.3), 24.9% (CI 18.4-32.0), 55.7% (CI 47.8-63.5), and 81.4% (CI 75.9-86.4) for PI-RADS categories 1, 2, 3, 4 and 5, respectively.

CONCLUSION

PI-RADS category 3 lesions can significantly impact the DTA of MRI for PC detection. A low prevalence of clinically significant PC is noted in PI-RADS category 1 and 2 cases.

ADVANCES IN KNOWLEDGE

Inclusion or exclusion of PI-RADS category 3 lesions impacts the DTA of MRI for PC detection.

Understanding the diagnosis of prostate cancer

Prostate cancer continues to be the most commonly diagnosed cancer, and the second leading cause of cancer death among Australian men. Prostate-specific antigen testing is personalised (not dichotomous in nature) and its interpretation should take into account the patient's age, symptoms, previous results and medication (eg, 5-α reductase inhibitors such as dutasteride). Multiparametric magnetic resonance imaging of the prostate has been proven to have a 93% sensitivity for detecting clinically significant prostate cancer. It has the potential to decrease unnecessary prostate biopsies by around 27%. International Society of Urological Pathology (ISUP) grade 1 (Gleason score 6) has been shown to have very little, if any, risk of metastasis ISUP grade 1 (Gleason score 3 +3 = 6) and low percentage ISUP grade 2 (Gleason score 3 + 4 [< 10%] = 7) can be offered active surveillance. The goal of active surveillance is to defer treatment but is still curative when required. With better imaging (magnetic resonance imaging and emerging prostate-specific membrane antigen positron emission tomography-computed tomography) and transperineal prostate biopsy, more men can be offered screening after discussion of risks and benefits, knowing that overdiagnosis has been minimised and radical treatment is reserved for only the most aggressive disease.

The "Is mpMRI Enough" or IMRIE Study: A Multicentre Evaluation of Prebiopsy Multiparametric Magnetic Resonance Imaging Compared with Biopsy

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) is now recommended prebiopsy in numerous healthcare regions based on the findings of high-quality studies from expert centres. Concern remains about reproducibility of mpMRI to rule out clinically significant prostate cancer (csPCa) in real-world settings.

OBJECTIVE

To assess the diagnostic performance of mpMRI for csPCa in a real-world setting.

DESIGN, SETTING, AND PARTICIPANTS

A multicentre, retrospective cohort study, including men referred with raised prostate-specific antigen (PSA) or an abnormal digital rectal examination who had undergone mpMRI followed by transrectal or transperineal biopsy, was conducted. Patients could be biopsy naïve or have had previous negative biopsies.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary definition for csPCa was International Society of Urological Pathology (ISUP) grade group (GG) ≥2 (any Gleason ≥7); the accuracy for other definitions was also evaluated.

RESULTS AND LIMITATIONS

Across ten sites, 2642 men were included (January 2011-November 2018). Mean age and PSA were 65.3yr (standard deviation [SD] 7.8yr) and 7.5ng/ml (SD 3.3ng/ml), respectively. Of the patients, 35.9% had "negative MRI" (scores 1-2); 51.9% underwent transrectal biopsy and 48.1% had transperineal biopsy, with 43.4% diagnosed with csPCa overall. The sensitivity and negative predictive value (NPV) for ISUP GG≥2 were 87.3% and 87.5%, respectively. The NPVs were 87.4% and 88.1% for men undergoing transrectal and transperineal biopsy, respectively. Specificity and positive predictive value of MRI were 49.8% and 49.2%, respectively. The sensitivity and NPV increased to 96.6% and 90.6%, respectively, when a PSA density threshold of 0.15ng/ml/ml was used in MRI scores 1-2; these metrics increased to 97.5% and 91.2%, respectively, for PSA density 0.12ng/ml/ml. ISUP GG≥3 (Gleason ≥4+3) was found in 2.4% (15/617) of men with MRI scores 1-2. They key limitations of this study are the heterogeneity and retrospective nature of the data.

CONCLUSIONS

Multiparametric MRI when used in real-world settings is able to rule out csPCa accurately, suggesting that about one-third of men might avoid an immediate biopsy. Men should be counselled about the risk of missing some significant cancers.

PATIENT SUMMARY

Multiparametric magnetic resonance imaging (MRI) is a useful tool for ruling out prostate cancer, especially when combined with prostate-specific antigen density (PSAD). Previous results published from specialist centres can be reproduced at smaller institutions. However, patients and their clinicians must be aware that an early diagnosis of clinically significant prostate cancer could be missed in nearly 10% of patients by relying on MRI and PSAD alone.

What to expect from a non-suspicious prostate MRI? A review

BACKGROUND

Many guidelines now recommend multiparametric MRI (mpMRI) prior to an initial or repeat prostate biopsy. However, clinical decision making for men with a non-suspicious mpMRI (Likert or PIRADS score 1-2) varies.

OBJECTIVES

To review the most recent literature to answer three questions. (1) Should we consider systematic biopsy if mpMRI is not suspicious? (2) Are there additional predictive factors that can help decide which patient should have a biopsy? (3) Can the low visibility of some cancers be explained and what are the implications?

SOURCES

A narrative review was performed in Medline databases using two searches with the terms "MRI" and "prostate cancer" and ("diagnosis" or "biopsy") and ("non-suspicious" or "negative" or "invisible"); "prostate cancer MRI visible". References of the selected articles were screened for additional articles.

STUDY SELECTION

Studies published in the last 5 years in English language were assessed for eligibility and selected if data was available to answer one of the three study questions.

RESULTS

Considering clinically significant cancer as ISUP grade≥2, the negative predictive value (NPV) of mpMRI in various settings and populations ranges from 76% to 99%, depending on cancer prevalence and the type of confirmatory reference test used. NPV is higher among patients with prior negative biopsy (88-96%), and lower for active surveillance patients (85-90%). The PSA density (PSAd) with a threshold of PSAd<0.15ng/ml/ml was the most studied and relevant predictive factor used in combination with mpMRI to rule out clinically significant cancer. Finally, mpMRI-invisible tumours appear to differ from a histopathological and genetic point of view, conferring clinical advantage to invisibility.

LIMITATIONS

Most published data come from expert centres and results may not be reproducible in all settings.

CONCLUSION

mpMRI has high diagnostic accuracy and in cases of negative mpMRI, PSA density can be used to determine which patient should have a biopsy. Growing knowledge of the mechanisms and genetics underlying MRI visibility will help develop more accurate risk calculators and biomarkers.

Post Prostatectomy Pathologic Findings of Patients With Clinically Significant Prostate Cancer and no Significant PI-RADS Lesions on Preoperative Magnetic Resonance Imaging

OBJECTIVES

We present postprostatectomy pathology results from a series of prostate cancer (Pca) Gleason grade group ≥2 patients who did not have findings suggestive of cancer on preoperative pelvic magnetic resonance imaging (MRI).

METHODS

We performed an institutional retrospective study of prostate magnetic resonance imaging (MRI) examinations done from October 2015 to February 2018. We identified patients who underwent prostatectomy for Pca Gleason ≥3 + 4 diagnosed on prostate biopsy with no associated MRI findings suggestive of malignancy and analyzed their postprostatectomy pathologic findings and MRI imaging results.

RESULTS

At our institution, 850 men with Pca received MRI between 2015 and 2018, and 156/850 patients received robotic-assisted radical prostatectomy. Thirty-three patients (33/156 = 21%) had negative MRI for PIRAD 3 or greater but had a biopsy showing significant Pca. Their mean (range) age was 62.7 (50-86) years. Their median (interquartile range) PSA, and PSA density were, 4.6 (3.7) ng/mL and 0.12 (0.05) ng/mL/cm², respectively; all not significantly different from patients with visible lesions on MRI who underwent surgery. On post prostatectomy pathology, 27/33 (82%) men had Pca Gleason score 7 or greater. The most common pattern was infiltrative growth with cancer glands intermingling between benign glands.

CONCLUSION

We describe the pathologic and imaging findings in an extensive series of men with clinically significant Pca with no significant lesions on preoperative MRI. Our results support the importance of patient counseling on the risk of missing significant Pca on MRI in isolation from other clinical variables.

Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in the Detection of Clinically Significant Prostate Cancer in the Prostate Imaging Reporting and Data System Era: A Systematic Review and Meta-analysis

CONTEXT

Prebiopsy multiparametric magnetic resonance imaging (mpMRI) is increasingly used in prostate cancer diagnosis. The reported negative predictive value (NPV) of mpMRI is used by some clinicians to aid in decision making about whether or not to proceed to biopsy.

OBJECTIVE

We aim to perform a contemporary systematic review that reflects the latest literature on optimal mpMRI techniques and scoring systems to update the NPV of mpMRI for clinically significant prostate cancer (csPCa).

EVIDENCE ACQUISITION

We conducted a systematic literature search and included studies from 2016 to September 4, 2019, which assessed the NPV of mpMRI for csPCa, using biopsy or clinical follow-up as the reference standard. To ensure that studies included in this analysis reflect contemporary practice, we only included studies in which mpMRI findings were interpreted according to the Prostate Imaging Reporting and Data System (PIRADS) or similar Likert grading system. We define negative mpMRI as either (1) PIRADS/Likert 1-2 or (2) PIRADS/Likert 1-3; csPCa was defined as either (1) Gleason grade group ≥2 or (2) Gleason grade group ≥3. We calculated NPV separately for each combination of negative mpMRI and csPCa.

EVIDENCE SYNTHESIS

A total of 42 studies with 7321 patients met our inclusion criteria and were included for analysis. Using definition (1) for negative mpMRI and csPCa, the pooled NPV for biopsy-naïve men was 90.8% (95% confidence interval [CI] 88.1-93.1%). When defining csPCa using definition (2), the NPV for csPCa was 97.1% (95% CI 94.9-98.7%). Calculation of the pooled NPV using definition (2) for negative mpMRI and definition (1) for csPCa yielded the following: 86.8% (95% CI 80.1-92.4%). Using definition (2) for both negative mpMRI and csPCa, the pooled NPV from two studies was 96.1% (95% CI 93.4-98.2%).

CONCLUSIONS

Multiparametric MRI of the prostate is generally an accurate test for ruling out csPCa. However, we observed heterogeneity in the NPV estimates, and local institutional data should form the basis of decision making if available.

PATIENT SUMMARY

The negative predictive values should assist in decision making for clinicians considering not proceeding to biopsy in men with elevated age-specific prostate-specific antigen and multiparametric magnetic resonance imaging reported as negative (or equivocal) on Prostate Imaging Reporting and Data System/Likert scoring. Some 7-10% of men, depending on the setting, will miss a diagnosis of clinically significant cancer if they do not proceed to biopsy. Given the institutional variation in results, it is of upmost importance to base decision making on local data if available.

The predictive value of the prostate health index vs. multiparametric magnetic resonance imaging for prostate cancer diagnosis in prostate biopsy

PURPOSE

To compare the ability of Prostate Health Index (PHI) to diagnose csPCa, with that of total PSA, PSA density (PSAD) and the multiparametric magnetic resonance (mpMRI) of the prostate.

METHODS

We analysed a group of 395 men planned for a prostate biopsy who underwent a mpMRI of the prostate evaluated using the PIRADS v1 criteria. All patients had their PHI measured before prostate biopsy. In patients with an mpMRI suspicious lesions, an mpMRI/ultrasound software fusion-guided biopsy was performed first, with 12 core systematic biopsy performed in all patients. A ROC analysis was performed for PCa detection for total PSA, PSAD, PIRADS score and PHI; with an AUC curve calculated for all criteria and a combination of PIRADS score and PHI. Subsequent sub-analyses included patients undergoing first and repeat biopsy.

RESULTS

The AUC for predicting the presence of csPCa in all patients was 59.5 for total PSA, 69.7 for PHI, 64.9 for PSAD and 62.5 for PIRADS. In biopsy naive patients it was 61.6 for total PSA, 68.9 for PHI, 64.6 for PSAD and 63.1 for PIRADS. In patients with previous negative biopsy the AUC for total PSA, PHI, PSAD and PIRADS was 55.4, 71.2, 64.4 and 69.3, respectively. Adding of PHI to PIRADS increased significantly (p = 0.007) the accuracy for prediction of csPCa.

CONCLUSION

Prostate Health Index could serve as a tool in predicting csPCa. When compared to the mpMRI, it shows comparable results. The PHI cannot, however, help us guide prostate biopsies in any way, and its main use may, therefore, be in pre-MRI or pre-biopsy triage.

Comparison of 68Ga-PSMA-617 PET/CT with mpMRI for the detection of PCa in patients with a PSA level of 4-20 ng/ml before the initial biopsy

The study was aimed at assessing the diagnostic performance of ⁶⁸Ga-PSMA-617 PET/CT in the detection of prostate cancer (PCa) in patients with a prostate-specific antigen (PSA) level of 4–20 ng/ml and to compare its efficacy with that of multiparametric MRI (mpMRI). We analyzed the data of 67 consecutive patients with PSA levels of 4–20 ng/ml who almost simultaneously underwent ⁶⁸Ga-PSMA-617 PET/CT and mpMRI. ⁶⁸Ga-PSMA-617 PET/CT and mpMRI diagnostic performances were compared via receiver operating characteristic (ROC) curve analysis. Of the 67 suspected PCa cases, 33 had pathologically confirmed PCa. ⁶⁸Ga-PSMA-617 PET/CT showed a patient-based sensitivity, specificity, and positive and negative predictive values (PPVs and NPVs) of 87.88%, 88.24%, 87.88%, and 88.24%, respectively. The corresponding values for mpMRI were 84.85%, 52.94%, 63.64%, and 78.26%. The area under the curve values for ⁶⁸Ga-PSMA-617 PET/CT and mpMRI were 0.881 and 0.689, respectively. ⁶⁸Ga-PSMA-617 PET/CT showed a better diagnostic performance than mpMRI in the detection of PCa in patients with PSA levels of 4–20 ng/ml.

Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer

Objectives: To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa).

Methods: This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group (n = 54) and an internal validation group (n = 22) from one center along with an external independent validation group (n = 83) from another center. A total of 1,188 radiomic features were extracted from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was performed to develop the model, incorporating the radiomic signature, ADC value, and independent clinical risk factors. This was presented using a radiomic nomogram. The receiver operating characteristic (ROC) curve was utilized to assess the predictive efficacy of the radiomic nomogram in both the training and validation groups. The decision curve analysis was used to evaluate which model achieved the most net benefit.

Results: The radiomic signature, which was made up of 10 selected features, was significantly associated with csPCa (P < 0.001 for both training and internal validation groups). The area under the curve (AUC) values of discriminating csPCa for the radiomics signature were 0.95 (training group), 0.86 (internal validation group), and 0.81 (external validation group). Multivariate logistic analysis identified the radiomic signature and ADC value as independent parameters of predicting csPCa. Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram.

Conclusions: The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.

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/).

What Type of Prostate Cancer Is Systematically Overlooked by Multiparametric Magnetic Resonance Imaging? An Analysis from the PROMIS Cohort

BACKGROUND

All risk stratification strategies in cancer overlook a spectrum of disease. The Prostate MR Imaging Study (PROMIS) provides a unique opportunity to explore cancers that are overlooked by multiparametric magnetic resonance imaging (mpMRI).

OBJECTIVE

To summarise attributes of cancers that are systematically overlooked by mpMRI.

DESIGN, SETTING, AND PARTICIPANTS

PROMIS tested performance of mpMRI and transrectal ultrasonography (TRUS)-guided biopsy, using 5 mm template mapping (TPM) biopsy as the reference standard.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Outcomes were overall and maximum Gleason scores, maximum cancer core length (MCCL), and prostate-specific antigen density (PSAD). Cancer attributes were compared between cancers that were overlooked and those that were detected.

RESULTS AND LIMITATIONS

Of men with cancer, 7% (17/230; 95% confidence interval [CI] 4.4-12%) had significant disease overlooked by mpMRI according to definition 1 (Gleason ≥ 4 + 3 of any length or MCCL ≥ 6 mm of any grade) and 13% (44/331; 95% CI 9.8-17%) according to definition 2 (Gleason ≥ 3 + 4 of any length or MCCL ≥ 4 mm). In comparison, TRUS-guided biopsy overlooked 52% (119/230; 95% CI 45-58%) of significant disease by definition 1 and 40% (132/331; 95% CI 35-45%) by definition 2. Prostate cancers undetected by mpMRI had significantly lower overall and maximum Gleason scores (p = 0.0007; p < 0.0001) and shorter MCCL (median difference: 3 mm [5 vs 8 mm], p < 0.0001; 95% CI 1-3) than cancers that were detected. No tumours with overall Gleason score > 3 + 4 (Gleason Grade Groups 3-5; 95% CI 0-6.4%) or maximum Gleason score > 4 + 3 (Gleason Grade Groups 4-5; 95% CI 0-8.0%) on TPM biopsy were undetected by mpMRI. Application of a PSAD threshold of 0.15 reduced the proportion of men with undetected cancer to 5% (12/230; 95% CI 2.7-8.9%) for definition 1 and 9% (30/331; 95% CI 6.2-13%) for definition 2. Application of a PSAD threshold of 0.10 reduced the proportion of men with undetected disease to 3% (6/230; 95% CI 1.0-5.6%) for definition 1 cancer and to 3% (11/331; 95% CI 1.7-5.9%) for definition 2 cancer. Limitations were post hoc analysis and uncertain significance of undetected lesions.

CONCLUSIONS

Overall, a small proportion of cancers are overlooked by mpMRI, with estimates ranging from 4.4% (lower boundary of 95% CI for definition 1) to 17% (upper boundary of 95% CI for definition 2). Prostate cancers undetected by mpMRI are of lower grade and shorter length than cancers that are detected.

PATIENT SUMMARY

Prostate cancers that are undetected by magnetic resonance imaging (MRI) are smaller and less aggressive than those that are detected, and none of the most aggressive cancers are overlooked by MRI.

Magnetic resonance as imaging diagnostic tool in prostate cancer: New evidences-The EAU Section of Uro-Technology position

Prostate cancer (PCa) is the second leading cause of cancer-related mortality and the most frequently diagnosed male malignant disease among men. The manifestation of PCa ranges from indolent to highly aggressive disease and due to this high variation in PCa progression, the diagnosis and subsequent treatment planning can be challenging. The current diagnostic approach with PSA testing and digital rectal examination followed by transrectal ultrasound biopsies lack in both sensitivity and specificity in PCa detection and offers limited information about the aggressiveness and stage of the cancer. Scientific work supports the rapidly growing use of multiparametric magnetic resonance imaging as the most sensitive and specific imaging tool for detection, lesion characterization and staging of PCa. Therefore, we carried out an updated review of magnetic resonance imaging in the diagnostic PCa reviewing the latest papers published in PubMed.

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

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