Comparison of prostate cancer detection rates of various prostate biopsy methods for patients with prostate-specific antigen levels of <10.0 ng/mL in real-world practice

Purpose: Several strategies of prostate biopsy (PBx) have been introduced to improve prostate cancer (PCa) detection rates. However, studies comparing cancer detection rates (CDRs) according to biopsy methods in real-world practice are scarce. This study aimed to investigate CDRs according to the biopsy methods for patients with prostate-specific antigen (PSA) <10.0 ng/mL. Materials and Methods: From 2006 to 2015, patients who underwent PBx were initially selected. All patients were categorized according to the biopsy methods performed (magnetic resonance imaging targeted biopsy [MR-TBx], 12+2 hypoechoic lesion target biopsy, saturation biopsy [sPBx], extended biopsy, and 12-core PBx). The CDR of MR-TBx was compared to that of sPBx and other protocols. Volume per core (VPC) was defined as prostate volume divided by the number of biopsy cores. Patients previously diagnosed with PCa were excluded. Results: Of the 1,598 patients (median PSA, 5.41 ng/mL), 401 (25.1%) were diagnosed with PCa. Among the biopsy methods, MR-TBx has the highest CDR and proportion of Gleason score ≥7 (3+4). Biopsy methods, VPC, age, prostate volume, and PSA were associated with PCa detection. In the sub-analysis for initial biopsy, MR-TBx had no significant difference with sPBx, but had higher CDR than the other biopsy protocols. For repeat biopsy, VPC, rather than the biopsy method, was associated with CDR. Conclusions: This study reaffirmed the efficacy of MR-TBx on CDR in real-world practice. In cases with barriers to performing magnetic resonance imaging, VPC might be useful for adjusting the optimal number of biopsy cores in repeat biopsy.ope

Update on the ICUD-SIU consultation on multi-parametric magnetic resonance imaging in localised prostate cancer

Introduction: Prostate cancer (PCa) imaging is a rapidly evolving field. Dramatic improvements in prostate MRI during the last decade will probably change the accuracy of diagnosis. This chapter reviews recent current evidence about MRI diagnostic performance and impact on PCa management. Materials and methods: The International Consultation on Urological Diseases nominated a committee to review the literature on prostate MRI. A search of the PubMed database was conducted to identify articles focussed on MP-MRI detection and staging protocols, reporting and scoring systems, the role of MP-MRI in diagnosing PCa prior to biopsy, in active surveillance, in focal therapy and in detecting local recurrence after treatment. Results: Differences in opinion were reported in the use of the strength of magnets [1.5 Tesla (T) vs. 3T] and coils. More agreement was found regarding the choice of pulse sequences; diffusion-weighted MRI (DW-MRI), dynamic contrast-enhanced MRI (DCE MRI), and/or MR spectroscopy imaging (MRSI) are recommended in addition to conventional T2-weighted anatomical sequences. In 2015, the Prostate Imaging Reporting and Data System (PI-RADS version 2) was described to standardize image acquisition and interpretation. MP-MRI improves detection of clinically significant PCa (csPCa) in the repeat biopsy setting or before the confirmatory biopsy in patients considering active surveillance. It is useful to guide focal treatment and to detect local recurrences after treatment. Its role in biopsy-naive patients or during the course of active surveillance remains debated. Conclusion: MP-MRI is increasingly used to improve detection of csPCa and for the selection of a suitable therapeutic approach

Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer

Background: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. Methods: Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out-of-bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). Results: As ExoMeth risk score (range, 0-1) increased, the likelihood of high-grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78-2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver-operator characteristic curve (AUC) = 0.89 (95% CI: 0.84-0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87-0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. Conclusion: Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed

Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed

Accuracy of multiparametric magnetic resonance imaging to detect significant prostate cancer and index lesion location

Background: Multiparametric magnetic resonance imaging (mpMRI) of the prostate appears to improve prostate cancer detection, but studies comparing mpMRI to histopathology at the time of radical prostatectomy (RP) are lacking. This retrospective study determined the accuracy of mpMRI predicting Gleason score and index lesion location at the time of RP, the current gold standard for diagnosis. Methods: Between April 2013 and April 2016, a database of all men aged more than 40 years who underwent RP after positive transrectal ultrasound biopsy by an experienced urological surgeon was collated at a single regional centre. This was cross‐referenced with a database of all men who had mpMRIs performed at a single centre and reported according to Prostate Imaging Reporting and Data System (PI‐RADS version 1) during this period to generate a sample size of 64 men. A Spearman\u27s rho test was utilized to calculate correlation. Results: Median age of patients was 64 years, the median prostate‐specific antigen at RP was 6.22 ng/mL. mpMRI was positive (≥PI‐RADS 3) in 85.9% of patients who underwent RP. More than 92% of participants had Gleason ≥7 disease. A positive relationship between mpMRI prostate PI‐RADS score and RP cancer volume was demonstrated. An anatomical location correlation calculated in octants was found to be 89.1% accurate. Conclusion: mpMRI accurately detects prostate cancer location and severity when compared with gold standard histopathology at the time of RP. It thus has an important role in planning for future prostate biopsy and cancer treatment

The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.

To evaluate the performance of computed high b value diffusion-weighted images (DWI) in prostate cancer detection. 97 consecutive patients who had undergone multiparametric MRI of the prostate followed by biopsy were reviewed. Five radiologists independently scored 138 lesions on native high b-value images (b = 1200 s/mm2), apparent diffusion coefficient (ADC) maps, and computed high b-value images (contrast equivalent to b = 2000 s/mm2) to compare their diagnostic accuracy. Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm2) to computed B2000 (c-B2000) in 5 readers. In 4 of 5 readers c-B2000 had significantly increased sensitivity and/or decreased specificity compared to B1200 (McNemar's p &lt; 0.05), at selected thresholds of interpretation. ADC maps were less accurate than B1200 or c-B2000 for 2 of 5 readers (P &lt; 0.05). This study detected no consistent improvement in overall diagnostic accuracy using c-B2000, compared with B1200 images. Readers detected more cancer with c-B2000 images (increased sensitivity) but also more false positive findings (decreased specificity)