Targeted Prostate Biopsy: How, When, and Why? A Systematic Review.

OBJECTIVE: Prostate cancer, the second most diagnosed cancer among men, requires precise diagnostic techniques to ensure effective treatment. This review explores the technological advancements, optimal application conditions, and benefits of targeted prostate biopsies facilitated by multiparametric magnetic resonance imaging (mpMRI). METHODS: A systematic literature review was conducted to compare traditional 12-core systematic biopsies guided by transrectal ultrasound with targeted biopsy techniques using mpMRI. We searched electronic databases including PubMed, Scopus, and Web of Science from January 2015 to December 2024 using keywords such as "targeted prostate biopsy", "fusion prostate biopsy", "cognitive prostate biopsy", "MRI-guided biopsy", and "transrectal ultrasound prostate biopsy". Studies comparing various biopsy methods were included, and data extraction focused on study characteristics, patient demographics, biopsy techniques, diagnostic outcomes, and complications. CONCLUSION: mpMRI-guided targeted biopsies enhance the detection of clinically significant prostate cancer while reducing unnecessary biopsies and the detection of insignificant cancers. These targeted approaches preserve or improve diagnostic accuracy and patient outcomes, minimizing the risks associated with overdiagnosis and overtreatment. By utilizing mpMRI, targeted biopsies allow for precise targeting of suspicious regions within the prostate, providing a cost-effective method that reduces the number of biopsies performed. This review highlights the importance of integrating advanced imaging techniques into prostate cancer diagnosis to improve patient outcomes and quality of life.

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.

Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materials and Methods This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 (P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 (P < .001) for lesions with PI-RADS scores of 4 or greater. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024.

Prediction of Clinically Significant Prostate Cancer by a Specific Collagen-related Transcriptome, Proteome, and Urinome Signature.

BACKGROUND AND OBJECTIVE: While collagen density has been associated with poor outcomes in various cancers, its role in prostate cancer (PCa) remains elusive. Our aim was to analyze collagen-related transcriptomic, proteomic, and urinome alterations in the context of detection of clinically significant PCa (csPCa, International Society of Urological Pathology [ISUP] grade group ≥2). METHODS: Comprehensive analyses for PCa transcriptome (n = 1393), proteome (n = 104), and urinome (n = 923) data sets focused on 55 collagen-related genes. Investigation of the cellular source of collagen-related transcripts via single-cell RNA sequencing was conducted. Statistical evaluations, clustering, and machine learning models were used for data analysis to identify csPCa signatures. KEY FINDINGS AND LIMITATIONS: Differential expression of 30 of 55 collagen-related genes and 34 proteins was confirmed in csPCa in comparison to benign prostate tissue or ISUP 1 cancer. A collagen-high cancer cluster exhibited distinct cellular and molecular characteristics, including fibroblast and endothelial cell infiltration, intense extracellular matrix turnover, and enhanced growth factor and inflammatory signaling. Robust collagen-based machine learning models were established to identify csPCa. The models outcompeted prostate-specific antigen (PSA) and age, showing comparable performance to multiparametric magnetic resonance imaging (mpMRI) in predicting csPCa. Of note, the urinome-based collagen model identified four of five csPCa cases among patients with Prostate Imaging-Reporting and Data System (PI-IRADS) 3 lesions, for which the presence of csPCa is considered equivocal. The retrospective character of the study is a limitation. CONCLUSIONS AND CLINICAL IMPLICATIONS: Collagen-related transcriptome, proteome, and urinome signatures exhibited superior accuracy in detecting csPCa in comparison to PSA and age. The collagen signatures, especially in cases of ambiguous lesions on mpMRI, successfully identified csPCa and could potentially reduce unnecessary biopsies. The urinome-based collagen signature represents a promising liquid biopsy tool that requires prospective evaluation to improve the potential of this collagen-based approach to enhance diagnostic precision in PCa for risk stratification and guiding personalized interventions. PATIENT SUMMARY: In our study, collagen-related alterations in tissue, and urine were able to predict the presence of clinically significant prostate cancer at primary diagnosis.

Part II: Effect of different evaluation methods to the application of a computer-aided prostate MRI detection/diagnosis (CADe/CADx) device on reader performance.

INTRODUCTION: The construction and results of a multiple-reader multiple-case prostate MRI study are described and reported to illustrate recommendations for how to standardize artificial intelligence (AI) prostate studies per the review constituting Part I1. METHODS: Our previously reported approach was applied to review and report an IRB approved, HIPAA compliant multiple-reader multiple-case clinical study of 150 bi-parametric prostate MRI studies across 9 readers, measuring physician performance both with and without the use of the recently FDA cleared CADe/CADx software ProstatID. RESULTS: Unassisted reader AUC values ranged from 0.418 - 0.759, with AI assisted AUC values ranging from 0.507 - 0.787. This represented a statistically significant AUC improvement of 0.045 (α = 0.05). A free-response ROC (FROC) analysis similarly demonstrated a statistically significant increase in θ from 0.405 to 0.453 (α = 0.05). The standalone performance of ProstatID performed across all prostate tissues demonstrated an AUC of 0.929, while the standalone lesion level performance of ProstatID at all biopsied locations achieved an AUC of 0.710. CONCLUSION: This study applies and illustrates suggested reporting and standardization methods for prostate AI studies that will make it easier to understand, evaluate and compare between AI studies. Providing radiologists with the ProstatID CADe/CADx software significantly increased diagnostic performance as assessed by both ROC and free-response ROC metrics. Such algorithms have the potential to improve radiologist performance in the detection and localization of clinically significant prostate cancer.

Variability in prostate cancer detection among radiologists and urologists using MRI fusion biopsy.

Objectives: The aim of this study is to evaluate the impact of radiologist and urologist variability on detection of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) with magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) fusion prostate biopsies. Patients and methods: The Prospective Loyola University MRI (PLUM) Prostate Biopsy Cohort (January 2015 to December 2020) was used to identify men receiving their first MRI and MRI/TRUS fusion biopsy for suspected PCa. Clinical, MRI and biopsy data were stratified by radiologist and urologist to evaluate variation in Prostate Imaging-Reporting and Data System (PI-RADS) grading, lesion number and cancer detection. Multivariable logistic regression (MVR) models and area under the curve (AUC) comparisons assessed the relative impact of individual radiologists and urologists. Results: A total of 865 patients (469 biopsy-naïve) were included across 5 urologists and 10 radiologists. Radiologists varied with grading 15.4% to 44.8% of patients with MRI lesions as PI-RADS 3. PCa detection varied significantly by radiologist, from 34.5% to 66.7% (p = 0.003) for PCa and 17.2% to 50% (p = 0.001) for csPCa. Urologists' PCa diagnosis rates varied between 29.2% and 55.8% (p = 0.013) and between 24.6% and 39.8% (p = 0.36) for csPCa. After adjustment for case-mix on MVR, a fourfold to fivefold difference in PCa detection was observed between the highest-performing and lowest-performing radiologist (OR 0.22, 95%CI 0.10-0.47, p < 0.001). MVR demonstrated improved AUC for any PCa and csPCa detection when controlling for radiologist variation (p = 0.017 and p = 0.038), but controlling for urologist was not significant (p = 0.22 and p = 0.086). Any PCa detection (OR 1.64, 95%CI 1.06-2.55, p = 0.03) and csPCa detection (OR 1.57, 95%CI 1.00-2.48, p = 0.05) improved over time (2018-2020 vs. 2015-2017). Conclusions: Variability among radiologists in PI-RADS grading is a key area for quality improvement significantly impacting the detection of PCa and csPCa. Variability for performance of MRI-TRUS fusion prostate biopsies exists by urologist but with less impact on overall detection of csPCa.

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture.

Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively.

Current Approach to Complications and Difficulties during Transrectal Ultrasound-Guided Prostate Biopsies.

Prostate cancer is one of the most common male malignancies worldwide. It affects middle-aged men (45-60 years) and is the leading cause of cancer-related mortality in Western countries. The TRUS (trans rectal ultrasound)-guided prostate biopsy has been a standard procedure in prostate cancer detection for more than thirty years, and it is recommended in male patients with an abnormal PSA (prostate-specific antigens) or abnormalities found during digital rectal examinations. During this procedure, urologists might encounter difficulties which may cause subsequent complications. This manuscript aims to present both the complications and the technical difficulties that may occur during TRUS-guided prostate biopsy, along with resolutions and solutions found in the specialized literature. The conclusions of this manuscript will note that the TRUS-guided prostate biopsy remains a solid, cost-efficient, and safe procedure with which to diagnose prostate cancer. The complications are usually self-limiting and do not require additional medical assistance. The difficulties posed by the procedure can be safely overcome if there are no other available alternatives. Open communication with the patients improves both pre- and post-procedure compliance.

A Diagnostic Accuracy Study of Targeted and Systematic Biopsies to Detect Clinically Significant Prostate Cancer, including a Model for the Partial Omission of Systematic Biopsies.

The primary objective of this study was to analyse the current accuracy of targeted and systematic prostate biopsies in detecting csPCa. A secondary objective was to determine whether there are factors predicting the finding of csPCa in targeted biopsies and, if so, to explore the utility of a predictive model for csPCa detection only in targeted biopsies. We analysed 2122 men with suspected PCa, serum PSA > 3 ng/mL, and/or a suspicious digital rectal examination (DRE), who underwent targeted and systematic biopsies between 2021 and 2022. CsPCa (grade group 2 or higher) was detected in 1026 men (48.4%). Discrepancies in csPCa detection in targeted and systematic biopsies were observed in 49.6%, with 13.9% of csPCa cases being detected only in systematic biopsies and 35.7% only in targeted biopsies. A predictive model for csPCa detection only in targeted biopsies was developed from the independent predictors age (years), prostate volume (mL), PI-RADS score (3 to 5), mpMRI Tesla (1.5 vs. 3.0), TRUS-MRI fusion image technique (cognitive vs. software), and prostate biopsy route (transrectal vs. transperineal). The csPCa discrimination ability of targeted biopsies showed an AUC of 0.741 (95% CI 0.721-0.762). The avoidance rate of systematic prostate biopsies went from 0.5% without missing csPCa to 18.3% missing 4.6% of csPCa cases. We conclude that the csPCa diagnostic accuracy of targeted biopsies is higher than that of systematic biopsies. However, a significant rate of csPCa remains detected only in systematic biopsies. A predictive model for the partial omission of systematic biopsies was developed.

Urinary Zinc Loss Identifies Prostate Cancer Patients.

Prostate Cancer (PCa) is one of the most common malignancies in men worldwide, with 1.4 million diagnoses and 310,000 deaths in 2020. Currently, there is an intense debate regarding the serum prostatic specific antigen (PSA) test as a diagnostic tool in PCa due to the lack of specificity and high prevalence of over-diagnosis and over-treatments. One of the most consistent characteristics of PCa is the marked decrease in zinc; hence the lost ability to accumulate and secrete zinc represents a potential parameter for early detection of the disease. We quantified zinc levels in urine samples collected after a standardized prostatic massage from 633 male subjects that received an indication for prostate biopsy from 2015 and 2019 at AOU Città della Salute e della Scienza di Torino Hospital. We observed that the mean zinc levels were lower in the urine of cancer patients than in healthy subjects, with a decreasing trend in correlation with the progression of the disease. The combination of zinc with standard parameters, such as PSA, age, digital rectal exploration results, and magnetic resonance findings, displayed high diagnostic performance. These results suggest that urinary zinc may represent an early and non-invasive diagnostic biomarker for prostate cancer.

Combining targeted and systematic prostate biopsy improves prostate cancer detection and correlation with the whole mount histopathology in biopsy naïve and previous negative biopsy patients.

Objective: Guidelines for previous negative biopsy (PNB) cohorts with a suspicion of prostate cancer (PCa) after positive multiparametric (mp) magnetic-resonance-imaging (MRI) often favour the fusion-guided targeted prostate-biopsy (TB) only approach for Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions. However, recommendations lack direct biopsy performance comparison within biopsy naïve (BN) vs. PNB patients and its prognostication of the whole mount pathology report (WMPR), respectively. We suppose, that the combination of TB and concomitant TRUS-systematic biopsy (SB) improves the PCa detection rate of PI-RADS 2, 3, 4 or 5 lesions and the International Society of Urological Pathology (ISUP)-grade predictability of the WMPR in BN- and PNB patients. Methods: Patients with suspicious mpMRI, elevated prostate-specific-antigen and/or abnormal digital rectal examination were included. All PI-RADS reports were intramurally reviewed for biopsy planning. We compared the PI-RADS score substratified TB, SB or combined approach (TB/SB) associated BN- and PNB-PCa detection rate. Furthermore, we assessed the ISUP-grade variability between biopsy cores and the WMPR. Results: According to BN (n = 499) vs. PNB (n = 314) patients, clinically significant (cs) PCa was detected more frequently by the TB/SB approach (62 vs. 43%) than with the TB (54 vs. 34%) or SB (57 vs. 34%) (all p < 0.0001) alone. Furthermore, we observed that the TB/SB strategy detects a significantly higher number of csPCa within PI-RADS 3, 4 or 5 reports, both in BN and PNB men. In contrast, applied biopsy techniques were equally effective to detect csPCa within PI-RADS 2 lesions. In case of csPCa diagnosis the TB approach was more often false-negative in PNB patients (BN 11% vs. PNB 19%; p = 0.02). The TB/SB technique showed in general significantly less upgrading, whereas a higher agreement was only observed for the total and BN patient cohort. Conclusion: Despite csPCa is more frequently found in BN patients, the TB/SB method always detected a significantly higher number of csPCa within PI-RADS 3, 4 or 5 reports of our BN and PNB group. The TB/SB strategy predicts the ISUP-grade best in the total and BN cohort and in general shows the lowest upgrading rates, emphasizing its value not only in BN but also PNB patients.

Value of T2 Mapping MRI for Prostate Cancer Detection and Classification.

BACKGROUND: Currently, multi-parametric prostate MRI (mpMRI) consists of a qualitative T2 , diffusion weighted, and dynamic contrast enhanced imaging. Quantification of T2 imaging might further standardize PCa detection and support artificial intelligence solutions. PURPOSE: To evaluate the value of T2 mapping to detect prostate cancer (PCa) and to differentiate PCa aggressiveness. STUDY TYPE: Retrospective single center cohort study. POPULATION: Forty-four consecutive patients (mean age 67 years; median PSA 7.9 ng/mL) with mpMRI and verified PCa by subsequent targeted plus systematic MR/ultrasound (US)-fusion biopsy from February 2019 to December 2019. FIELD STRENGTH/SEQUENCE: Standardized mpMRI at 3 T with an additionally acquired T2 mapping sequence. ASSESSMENT: Primary endpoint was the analysis of quantitative T2 values and contrast differences/ratios (CD/CR) between PCa and benign tissue. Secondary objectives were the correlation between T2 values, ISUP grade, apparent diffusion coefficient (ADC) value, and PI-RADS, and the evaluation of thresholds for differentiating PCa and clinically significant PCa (csPCa). STATISTICAL TESTS: Mann-Whitney test, Spearman's rank (rs ) correlation, receiver operating curves, Youden's index (J), and AUC were performed. Statistical significance was defined as P < 0.05. RESULTS: Median quantitative T2 values were significantly lower for PCa in PZ (85 msec) and PCa in TZ (75 msec) compared to benign PZ (141 msec) or TZ (97 msec) (P < 0.001). CD/CR between PCa and benign PZ (51.2/1.77), respectively TZ (19.8/1.29), differed significantly (P < 0.001). The best T2 -mapping threshold for PCa/csPCa detection was for TZ 81/86 msec (J = 0.929/1.0), and for PZ 110 msec (J = 0.834/0.905). Quantitative T2 values of PCa did not correlate significantly with the ISUP grade (rs  = 0.186; P = 0.226), ADC value (rs  = 0.138; P = 0.372), or PI-RADS (rs  = 0.132; P = 0.392). DATA CONCLUSION: Quantitative T2 values could differentiate PCa in TZ and PZ and might support standardization of mpMRI of the prostate. Different thresholds seem to apply for PZ and TZ lesions. However, in the present study quantitative T2 values were not able to indicate PCa aggressiveness. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

The Role and Significance of Bioumoral Markers in Prostate Cancer.

The prostate is one of the most clinically accessible internal organs of the genitourinary tract in men. For decades, the only method of screening for prostate cancer (PCa) has been digital rectal examination of 1990s significantly increased the incidence and prevalence of PCa and consequently the morbidity and mortality associated with this disease. In addition, the different types of oncology treatment methods have been linked to specific complications and side effects, which would affect the patient's quality of life. In the first two decades of the 21st century, over-detection and over-treatment of PCa patients has generated enormous costs for health systems, especially in Europe and the United States. The Prostate Specific Antigen (PSA) is still the most common and accessible screening blood test for PCa, but with low sensibility and specificity at lower values (<10 ng/mL). Therefore, in order to avoid unnecessary biopsies, several screening tests (blood, urine, or genetic) have been developed. This review analyzes the most used bioumoral markers for PCa screening and also those that could predict the evolution of metastases of patients diagnosed with PCa.

The accuracy of prostate cancer diagnosis in biopsy-naive patients using combined magnetic resonance imaging and transrectal ultrasound fusion-targeted prostate biopsy.

BACKGROUND: This study aimed to estimate whether multiparametric magnetic resonance imaging (mpMRI)-transrectal ultrasound (TRUS) fusion biopsy (FUS-TB) increases the detection rates of clinically significant prostate cancer (csPCa) compared with TRUS-guided systematic biopsy (TRUS-GB). METHODS: This retrospective study focused on patients who underwent mpMRI before prostate biopsy (PB) with Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) scores ≥3 and prostate-specific antigen (PSA) level between 2.5 and 20 ng/mL. Before FUS-TB, the biopsy needle position was checked virtually using three-dimensional mapping. After confirming the position of the target within the prostate, biopsy needle was inserted and PB was performed. Suspicious lesions were generally targeted with 2 to 4 cores. Subsequently, 10-12 cores were biopsied for TRUS-GB. The primary endpoint was the PCa detection rate (PCDR) for patients with PCa who underwent combined FUS-TB and TRUS-GB. RESULTS: According to PI-RADS v2, 76.7% of the patients with PI-RADS v2 score ≥3 were diagnosed with PCa. The PCDRs in patients with PI-RADS v2 score of 4 or 5 were significantly higher than those in patients with PI-RADS v2 score of 3 (3 vs. 4, P<0.001; 3 vs. 5, P<0.001; 4 vs. 5, P=0.073). According to PCDR, the detection rates of PCa and csPCa in the FUS-TB were significantly higher than that in the TRUS-GB. CONCLUSIONS: Following detection of suspicious tumor lesions on mpMRI, FUS-TB use detects a higher number of PCa cases compared with TRUS-GB.

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.

Low Levels of Urinary PSA Better Identify Prostate Cancer Patients.

Serum prostatic specific antigen (PSA) has proven to have limited accuracy in early diagnosis and in making clinical decisions about different therapies for prostate cancer (PCa). This is partially due to the fact that an increase in PSA in the blood is due to the compromised architecture of the prostate, which is only observed in advanced cancer. On the contrary, PSA observed in the urine (uPSA) reflects the quantity produced by the prostate, and therefore can give more information about the presence of disease. We enrolled 574 men scheduled for prostate biopsy at the urology clinic, and levels of uPSA were evaluated. uPSA levels resulted lower among subjects with PCa when compared to patients with negative biopsies. An indirect correlation was observed between uPSA amount and the stage of disease. Loss of expression of PSA appears as a characteristic of prostate cancer development and its evaluation in urine represents an interesting approach for the early detection of the disease and the stratification of patients.

Prostate-specific antigen density during dutasteride treatment for 1 year predicts the presence of prostate cancer in benign prostatic hyperplasia after the first negative biopsy (PREDICT study).

OBJECTIVES: To prospectively evaluate the detection rate of prostate cancer, and to identify the risk factors of prostate cancer detection after a 1-year administration of dutasteride and first negative prostate biopsy. METHODS: Patients with benign prostatic hyperplasia who presented high prostate-specific antigen levels after the first negative prostate biopsy were administered 0.5 mg dutasteride daily for 1 year. They underwent a repeat prostate biopsy after 1 year. The primary end-point was the detection rate of prostate cancer. The secondary end-point was the ability of prostate-specific antigen kinetics to predict prostate cancer detection. Prostate-specific antigen was measured before the initial prostate biopsy and at 6, 9 and 12 months after starting dutasteride. Patients were classified into a prostate cancer and a non-prostate cancer group. RESULTS: Prostate cancer was detected in 15 of 149 participants (10.1%). The total prostate-specific antigen change between the prostate cancer and non-prostate cancer group at 1 year was significantly different (P = 0.002). Although prostate-specific antigen levels at baseline did not significantly differ between study groups (P = 0.102), prostate-specific antigen levels at 6, 9 and 12 months were significantly different (P = 0.002, P = 0.001 and P < 0.001, respectively). The mean reduction rate of prostate-specific antigen density between the prostate cancer and non-prostate cancer group at 1 year was significantly different (-4.25 ± 76.5% vs -38.0 ± 28.7%, P = 0.001). Using a multivariate analysis, a >10% increase of prostate-specific antigen density at 1 year post-dutasteride treatment was the only predictive risk factor for prostate cancer after the first negative prostate biopsy (odds ratio 11.238, 95% confidence interval 3.112-40.577, P < 0.001). CONCLUSION: In the present study cohort, >10% increase in prostate-specific antigen density represented the only significant predictive risk factor for prostate cancer diagnosis in patients with elevated prostate-specific antigen after the first negative prostate biopsy.

MRI-targeted prostate biopsy: the next step forward!

AIM: For decades, the gold standard technique for diagnosing prostate cancer was the 10 to 12 core systematic transrectal or transperineal biopsy, under ultrasound guidance. Over the past years, an increased rate of false negative results and detection of clinically insignificant prostate cancer has been noted, resulting into overdiagnosis and overtreatment. The purpose of the current study was to evaluate the changes in diagnosis and management of prostate cancer brought by MRI-targeted prostate biopsy. METHODS: A critical review of literature was carried out using the Medline database through a PubMed search, 37 studies meeting the inclusion criteria: prospective studies published in the past 8 years with at least 100 patients per study, which used multiparametric magnetic resonance imaging as guidance for targeted biopsies. RESULTS: In-Bore MRI targeted biopsy and Fusion targeted biopsy outperform standard systematic biopsy both in terms of overall and clinically significant prostate cancer detection, and ensure a lower detection rate of insignificant prostate cancer, with fewer cores needed. In-Bore MRI targeted biopsy performs better than Fusion biopsy especially in cases of apical lesions. CONCLUSION: Targeted biopsy is an emerging and developing technique which offers the needed improvements in diagnosing clinically significant prostate cancer and lowers the incidence of insignificant ones, providing a more accurate selection of the patients for active surveillance and focal therapies.

Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering.

The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.

BACKGROUND: Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity. PURPOSE: To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa. STUDY TYPE: Retrospective. SUBJECTS: Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa. FIELD STRENGTH/SEQUENCE: A 3 T, multishell diffusion-weighted and axial T2-weighted sequences. ASSESSMENT: High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated. STATISTICAL TESTS: Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05. RESULTS: RSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05). DATA CONCLUSION: RSI4 -C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Transperineal freehand multiparametric MRI fusion targeted biopsies under local anaesthesia for prostate cancer diagnosis: a multicentre prospective study of 1014 cases.

OBJECTIVE: To assess the outcomes of multiparametric magnetic resonance imaging (mpMRI) transperineal targeted fusion biopsy (TPFBx) under local anaesthesia. PATIENTS AND METHODS: We prospectively screened 1327 patients with a positive mpMRI undergoing TPFBx (targeted cores and systematic cores) under local anaesthesia, at two tertiary referral institutions, between September 2016 and May 2019, for inclusion in the present study. Primary outcomes were detection of clinically significant prostate cancer (csPCa) defined as (1) International Society of Urological Pathologists (ISUP) grade >1 or ISUP grade 1 with >50% involvement of prostate cancer (PCa) in a single core or in >2 cores (D1) and (2) ISUP grade >1 PCa (D2). Secondary outcomes were: assessment of peri-procedural pain (numerical rating scale [NRS]) and procedure timings; erectile (International Index of Erectile Function) and urinary (International Prostate Symptom Score) function changes; and complications. We also investigated the value of systematic sampling and concordance with radical prostatectomy (RP). RESULTS: A total of 1014 patients were included, of whom csPCa was diagnosed in 39.4% (n = 400). The procedure was tolerable (NRS pain score 3.1 ± 2.3), with no impact on erectile (P = 0.45) or urinary (P = 0.58) function, and a low rate of complications (Clavien-Dindo grades 1 or 2, n = 8; grade >2, n = 0). No post-biopsy sepsis was recorded. Twenty-two men (95% confidence interval [CI] 17-29) needed to undergo additional systematic biopsy to diagnose one csPCa missed by targeted biopsies (D1). ISUP grade concordance of biopsies with RP was as follows: k = 0.40 (95% CI 0.31-0.49) for targeted cores alone and k = 0.65 (95% CI 0.57-0.72; P < 0.05) overall. CONCLUSIONS: The use of TPFBx under local anaesthesia yielded good csPCa detection and was feasible, quick, well tolerated and safe. Infectious risk was negligible. Addition of systematic to targeted cores may not be needed in all men, although it improves csPCa detection and concordance with RP.