Search Results (11)

Background: 5-alpha reductase inhibitors (5-ARIs) change hormonal pathways and reduce prostate size. We evaluated the effects of 5-ARIs on prostatic multiparametric magnetic resonance imaging (mpMRI) suspicious findings and in the identification of prostate cancer using targeted biopsies. Methods: We conducted a retrospective study including 600 consecutive patients who, between 2017 and 2021, underwent combined transperineal fusion biopsies. Primary outcomes were Prostate Imaging Reporting and Data System version 2 (PIRADS v2) scores and the identification of clinically significant prostate cancer from suspicious lesions (targeted CSPC). Outcomes were compared between patients treated with 5-ARIs for a minimum of 6 months and the other patients. Results: Patients treated with 5-ARIs were older (p < 0.001) with higher rates of previous prostate biopsies (p = 0.004). PIRADS scores were 3, 4, and 5 in 15 (29%), 28 (54%), and 9 (17%) patients among the 5-ARI group and 130 (24%), 308 (56%), and 110 (20%) patients among the others, and the scores were not different between the groups (p = 0.69). The targeted CSPC identification rate among 5-ARI patients was 31%, not different compared to the non-5-ARI group (p = 1). Rates of targeted CSPC for each PIRADS score were not affected by 5-ARI treatment. The 5-ARI was not associated with neither PIRADS ≥ 4 score nor targeted CSPC on logistic regression analyses (OR = 0.76, 95% CI 0.4–1.4 and OR = 1.02, 95% CI 0.5–1.9, respectively). Conclusions: 5-ARI treatment is not associated with PIRADS score alterations or targeted biopsy results. Patients treated by 5-ARIs with suspicious lesions should not be addressed differently during the mpMRI-related diagnostic process. Full article

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann–Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70–0.84). The diagnostic performance for TZ lesions (AUC: 0.83–1.00) was superior to PZ lesions (AUC: 0.74–0.85). In the bivariate models, performance in the PZ improved with AUCs >0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions. Full article

Background: Multiparametric magnetic resonance is an established imaging utilized in the diagnostic pathway of prostate cancer. The aim of this study is to evaluate the accuracy and reliability of multiparametric magnetic resonance imaging (mpMRI) in the detection of clinically significant prostate cancer, defined as Gleason Score ≥ 4 + 3 or a maximum cancer core length 6 mm or longer, in patients with a previous negative biopsy. Methods: The study was conducted as a retrospective observational study at the University of Naples “Federico II”, Italy. Overall, 389 patients who underwent systematic and target prostate biopsy between January 2019 and July 2020 were involved and were divided into two groups: Group A, which included biopsy-naïve patients; Group B, which included re-biopsy patients. All mpMRI images were obtained using three Tesla instruments and were interpreted according to PIRADS (Prostate Imaging Reporting and Data System) version 2.0. Results: 327 patients were biopsy-naïve, while 62 belonged to the re-biopsy group. Both groups were comparable in terms of age, total PSA (prostate-specific antigen), and number of cores obtained at the biopsy. 2.2%, 8.8%, 36.1%, and 83.4% of, respectively, PIRADS 2, 3, 4, and 5 biopsy-naïve patients reported a clinically significant prostate cancer compared to 0%, 14.3%, 39%, and 66.6% of re-biopsy patients (p < 0.0001–p = 0.040). No difference was reported in terms of post-biopsy complications. Conclusions: mpMRI confirms its role as a reliable diagnostic tool prior to performing prostate biopsy in patients who underwent a previous negative biopsy, reporting a comparable detection rate of clinically significant prostate cancer. Full article

Objectives. The objective of this study was to evaluate the potential use of Prostate Imaging – Reporting and Data System version 2 (PI-RADS) in combination with Glutathione S-transferase P1 (GST-P1) expression for an improved diagnosis of prostate cancer, in patients with inconclusive values of prostate-specific antigen (PSA). Materials and Methods. The study was conducted on 80 patients for whom PSA values were evaluated and were found to be inconclusive (4–10 ng/ml). These patients underwent imagistic evaluation (PI-RADS), followed by transurethral prostate biopsy, with the evaluation of GST-P1 expression and histopathological examination (for diagnosis confirmation). Results. By combining the results of PI-RADS and GST-P1 the capacity of the tests to correctly identify healthy subjects, with an area under curve of 0.832 (95% CI 0.732–0.907), with a sensitivity of 73.25% and a specificity of 77.78%. Conclusions. PI-RADS lesions and GST-P1 methylation testing when PSA levels are in a “grey zone”, provide a better specificity and sensitivity by comparison through single testing. Testing patients with inconclusive PSA levels allows for a more accurate diagnosis and less over-diagnosis by non-invasive procedures, such as repeated biopsies. Full article

MRI can identify suspicious lesions, providing the semi-quantitative risk of csPCa through the Prostate Imaging-Report and Data System (PI-RADS). Predictive models of clinical variables that individualise the risk of csPCa have been developed by adding PI-RADS score (MRI-PMs). Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness. A systematic review was performed after a literature search performed by two independent investigators in PubMed, Cochrane, and Web of Science databases, with the Medical Subjects Headings (MESH): predictive model, nomogram, risk model, magnetic resonance imaging, PI-RADS, prostate cancer, and prostate biopsy. This review was made following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria and studied eligibility based on the Participants, Intervention, Comparator, and Outcomes (PICO) strategy. Among 723 initial identified registers, 18 studies were finally selected. Warp analysis of selected studies was performed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical predictors in addition to the PI-RADS score in developed MRI-PMs were age, PCa family history, digital rectal examination, biopsy status (initial vs. repeat), ethnicity, serum PSA, prostate volume measured by MRI, or calculated PSA density. All MRI-PMs improved the prediction of csPCa made by clinical predictors or imaging alone and achieved most areas under the curve between 0.78 and 0.92. Among 18 developed MRI-PMs, 7 had any external validation, and two RCs were available. The updated PI-RADS version 2 was exclusively used in 11 MRI-PMs. The performance of MRI-PMs according to PI-RADS was only analysed in a single study. We conclude that MRI-PMs improve the selection of candidates for prostate biopsy beyond the PI-RADS category. However, few developed MRI-PMs meet the appropriate requirements in routine clinical practice. Full article

The impact of pelvic inflammation on prostate cancer (PCa) biology and aggressive phenotype has never been studied. Our study objective was to evaluate the role of pelvic inflammation on PCa aggressiveness and its association with clinical outcomes in patients following radical prostatectomy (RP). This study has been conducted on a retrospective single-institutional consecutive cohort of 2278 patients who underwent robot-assisted laparoscopic prostatectomy (RALP) between 01/2013 and 10/2019. Data from 2085 patients were analyzed to study the association between pelvic inflammation and adverse pathology (AP), defined as Gleason Grade Group (GGG) > 2 and ≥ pT3 stage, at resection. In a subset of 1997 patients, the association between pelvic inflammation and biochemical recurrence (BCR) was studied. Alteration in tumor transcriptome and inflammatory markers in patients with and without pelvic inflammation were studied using microarray analysis, immunohistochemistry, and culture supernatants derived from inflamed sites used in functional assays. Changes in blood inflammatory markers in the study cohort were analyzed by O-link. In univariate analyses, pelvic inflammation emerged as a significant predictor of AP. Multivariate cox proportional-hazards regression analyses showed that high pelvic inflammation with pT3 stage and positive surgical margins significantly affected the time to BCR (p ≤ 0.05). PCa patients with high inflammation had elevated levels of pro-inflammatory cytokines in their tissues and in blood. Genes involved in epithelial-to-mesenchymal transition (EMT) and DNA damage response were upregulated in patients with pelvic inflammation. Attenuation of STAT and IL-6 signaling decreased tumor driving properties of conditioned medium from inflamed sites. Pelvic inflammation exacerbates the progression of prostate cancer and drives an aggressive phenotype. Full article

The aim of this study was to assess the predictive value of pre-biopsy blood-based markers in patients undergoing a fusion biopsy for suspicious prostate magnetic resonance imaging (MRI). We identified 365 consecutive patients who underwent MRI-targeted and systematic prostate biopsy for an MRI scored Prostate Imaging–Reporting and Data System Version (PI-RADS) ≥ 3. We evaluated the neutrophil/lymphocyte ratio (NLR), derived neutrophil/lymphocyte ratio (dNLR), platelet/lymphocyte ratio (PLR), systemic immune inflammation index (SII), lymphocyte/monocyte ratio (LMR,) de Ritis ratio, modified Glasgow Prognostic Score (mGPS), and prognostic nutrition index (PNI). Uni- and multivariable logistic models were used to analyze the association of the biomarkers with biopsy findings. The clinical benefits of biomarkers implemented in clinical decision-making were assessed using decision curve analysis (DCA). In total, 69% and 58% of patients were diagnosed with any prostate cancer and Gleason Grade (GG) ≥ 2, respectively. On multivariable analysis, only high dNLR (odds ratio (OR) 2.61, 95% confidence interval (CI) 1.23–5.56, p = 0.02) and low PNI (OR 0.48, 95% CI 0.26–0.88, p = 0.02) remained independent predictors for GG ≥ 2. The logistic regression models with biomarkers reached AUCs of 0.824–0.849 for GG ≥ 2. The addition of dNLR and PNI did not enhance the net benefit of a standard clinical model. Finally, we created the nomogram that may help guide biopsy avoidance in patients with suspicious MRI. In patients with PI-RADS ≥ 3 lesions undergoing MRI-targeted and systematic biopsy, a high dNLR and low PNI were associated with unfavorable biopsy outcomes. Pre-biopsy blood-based biomarkers did not, however, significantly improve the discriminatory power and failed to add a clinical benefit beyond standard clinical factors. Full article

This study aimed to determine the predictive value of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) based on biparametric magnetic resonance imaging (bpMRI) with combined target biopsy (TBx) and systematic biopsy (SBx) in patients with suspicion of having clinically significant prostate cancer (csPCa). In this retrospective study, we reviewed the clinical and pathological records of 184 consecutive patients who underwent bpMRI before prostate biopsy. We focused on patients with PI-RADS v2 scores ≥ 3. MRI was performed using a 3-Tesla clinical scanner with a 32-channel phased-array receiver coil. PI-RADS v2 was used to describe bpMRI findings based on T2-weighted imaging and diffusion-weighted imaging scores. The primary endpoint was the diagnostic accuracy rate of PI-RADS v2 based on bpMRI for patients with prostate cancer (PCa) who underwent combined TBx and SBx. A total of 104 patients were enrolled in this study. Combined TBx and SBx was significantly superior to either method alone for PCa detection in patients with suspicious lesions according to PI-RADS v2. TBx and SBx detected concordant csPCa in only 24.1% of the patients. In addition, the rate of increase in the Gleason score was similar between SBx (41.5%) and TBx (34.1%). The diagnostic accuracy of bpMRI is comparable to that of standard multiparametric MRI for the detection of csPCa. Moreover, combined TBx and SBx may be optimal for the accurate determination of csPCa diagnosis, the International Society of Urological Pathology grade, and risk classification. Full article

Radiomics applied in MRI has shown promising results in classifying prostate cancer lesions. However, many papers describe single-center studies without external validation. The issues of using radiomics models on unseen data have not yet been sufficiently addressed. The aim of this study is to evaluate the generalizability of radiomics models for prostate cancer classification and to compare the performance of these models to the performance of radiologists. Multiparametric MRI, photographs and histology of radical prostatectomy specimens, and pathology reports of 107 patients were obtained from three healthcare centers in the Netherlands. By spatially correlating the MRI with histology, 204 lesions were identified. For each lesion, radiomics features were extracted from the MRI data. Radiomics models for discriminating high-grade (Gleason score ≥ 7) versus low-grade lesions were automatically generated using open-source machine learning software. The performance was tested both in a single-center setting through cross-validation and in a multi-center setting using the two unseen datasets as external validation. For comparison with clinical practice, a multi-center classifier was tested and compared with the Prostate Imaging Reporting and Data System version 2 (PIRADS v2) scoring performed by two expert radiologists. The three single-center models obtained a mean AUC of 0.75, which decreased to 0.54 when the model was applied to the external data, the radiologists obtained a mean AUC of 0.46. In the multi-center setting, the radiomics model obtained a mean AUC of 0.75 while the radiologists obtained a mean AUC of 0.47 on the same subset. While radiomics models have a decent performance when tested on data from the same center(s), they may show a significant drop in performance when applied to external data. On a multi-center dataset our radiomics model outperformed the radiologists, and thus, may represent a more accurate alternative for malignancy prediction. Full article

Background and objectives: Overdiagnosis, overtreatment, and the need for repeated procedures caused by transrectal ultrasound guided prostate biopsies and their related complications places a heavy burden on healthcare systems. This was a prospective cohort validating study to access the clinical accuracy of systematic and MRI-cognitive targeted transperineal prostate biopsies in detecting clinically significant prostate cancer after a previous negative biopsy and persistent suspicion of malignancy. The primary goal was to assess the ability of multiparametric magnetic resonance imaging (mpMRI) to detect clinically significant prostate cancer with an additional goal to assess the diagnostic value of systematic and MRI-cognitive transperineal biopsies. Materials and Methods: In total, 200 patients were enrolled who had rising serum prostate specific antigen (PSA) levels for at least 4 months after a previous negative transrectal ultrasound (TRUS) biopsy. All eligible men underwent 1.5T prostate mpMRI, reported using the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), followed by a 20-region transperineal prostate systematic biopsy and additional targeted biopsies. Results: Systematic 20-core transperineal prostate biopsies (TPBs) were performed for 38 (19%) patients. Systemic 20-core TPB with additional cognitive targeted biopsies were performed for 162 (81%) patients. Clinically significant prostate cancer (csPC) was detected for 31 (15.5%) patients, of which 20 (64.5%) cases of csPC were detected by systematic biopsy, eight (25.8%) cases were detected by targeted biopsy, and three (9.7%) both by systematic and targeted biopsies. Conclusions: Cognitive mpMRI guided transperineal target biopsies increase the detection rate of clinically significant prostate cancer after a previously negative biopsy. However, in a repeat prostate biopsy setting, we recommend applying a cognitive targeted biopsy with the addition of a systematic biopsy. Full article

Prostate cancer identification and assessment of clinical significance continues to be a challenge. Routine multiparametric magnetic resonance imaging has shown to be useful in assessing disease progression. Although dynamic contrast-enhanced imaging (DCE) has the ability to characterize perfusion across time and has shown enormous utility, radiological assessment (Prostate Imaging-Reporting and Data System or PIRADS version 2) has limited its use owing to lack of consistency and nonquantitative nature. In our work, we propose a systematic methodology to quantify perfusion dynamics for the DCE imaging. Using these metrics, 7 different subregions or perfusion habitats of the targeted lesions are localized and related to clinical significance. We found that quantitative features describing the habitat based on the late area under the DCE time-activity curve was a good predictor of clinical significance disease. The best predictive feature in the habitat had an AUC of 0.82, CI [0.81–0.83]. Full article