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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. [ABSTRACT FROM AUTHOR]

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 I 1. 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. 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. 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. [ABSTRACT FROM AUTHOR]

The rapid progress in biomedical imaging technology has generated considerable interest in new non-invasive photoacoustic endoscopy imaging techniques. This emerging technology offers significant benefits, including high spectral specificity, strong tissue penetration, and real-time multidimensional high-resolution imaging capabilities, which enhance clinical diagnosis and treatment of prostate cancer. This paper delivers a thorough review of current prostate cancer screening techniques, the core principles of photoacoustic endoscopy imaging, and the latest research on its use in detecting prostate cancer. Additionally, the limitations of this technology in prostate cancer detection are discussed, and future development trends are anticipated. [ABSTRACT FROM AUTHOR]

Abstract 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

The rapid progress in biomedical imaging technology has generated considerable interest in new non-invasive photoacoustic endoscopy imaging techniques. This emerging technology offers significant benefits, including high spectral specificity, strong tissue penetration, and real-time multidimensional high-resolution imaging capabilities, which enhance clinical diagnosis and treatment of prostate cancer. This paper delivers a thorough review of current prostate cancer screening techniques, the core principles of photoacoustic endoscopy imaging, and the latest research on its use in detecting prostate cancer. Additionally, the limitations of this technology in prostate cancer detection are discussed, and future development trends are anticipated.

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. [ABSTRACT FROM AUTHOR]

BackgroundDiffusion 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.PurposeTo evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa.Study typeRetrospective.SubjectsForty-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/sequenceA 3 T, multishell diffusion-weighted and axial T2-weighted sequences.AssessmentHigh-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 testsVoxel-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.ResultsRSI4 -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

Ultrasound imaging is commonly used to guide sampling the prostate tissue in transrectal biopsies, followed by detection of cancer through histopathological analysis and coarse labelling of sampled tissue. Ideally, the procedure should be improved by developing machine learning solutions that can identify the presence of cancer in ultrasound images to guide the biopsy procedure. Training a fully supervised learning model using coarse histopathology labels suffers from weakly annotated data which introduce label noise for each image pixel. To address this challenge, we propose a semi-supervised framework for learning with noisy labels. We leverage a two-component mixture model to cluster the training data into clean and noisy label samples based on their loss values. Then, during the semi-supervised training phase, we utilise the well-known MixMatch algorithm which incorporates consistency regularisation, entropy minimisation, and the Mixup regularisation as well as the cross-entropy loss function for noisy and clean sets, respectively. We evaluate the proposed framework with prostate ultrasound data obtained from 71 subjects, while sampling 264 biopsy cores. We achieve balanced accuracy, sensitivity, and specificity of 78.6%, 80.0%, and 77.1%, respectively. In a detailed comparison study, we demonstrate that our proposed framework outperforms the fully supervised method with state-of-the-art robust loss functions. [ABSTRACT FROM AUTHOR]

The purpose of this work is to study the functional and oncological results of minimally invasive methods in patients with verified prostate cancer.Materials and methods. In our study, 160 patients with identified prostate cancer were presented, treatment was carried out with minimally invasive methods (methods of cryoablation (n = 53), brachytherapy (n = 52) and HIFU therapy (n = 55)). A qualitative assessment of the oncological outcome revealed high levels of prostate-specific antigen (PSA) and the results of repeated transrectal prostate biopsies. The evaluation of functional indicators and quality of life was carried out according to the results of the IIEF-5 (International Index of Erectile Function), IPSS (International Prostate Symptom Score), QoL (Quality of Life), Qmax (maximum urination rate of function).Results. The results of oncological control according to the data of positive repeated biopsies were worse in patients after cryoablation (7.54 %), the best indicators of oncological results were observed in patients after brachytherapy. Looking at the IPSS results, it is possible to detect statistical signs of higher scores in the brachytherapy group when various signs are found in the postoperative period, however, these differences do not reach statistical signs in the late period in patients of group brachytherapy and cryoablation. Patients of the cryoablation group showed higher levels of the IIEF-5 in the postoperative period, in the late period of observation of erectile function in patients of the cryoablation group, the statistical data did not differ from those in patients after brachytherapy. Patients after HIFU therapy showed a decrease in de novo erectile dysfunction over a 3-year follow-up period, above average IIEF5 scores, below IPSS scores, and better QoL results.Conclusion. Long-term oncological results are, in general, revisions, however, the recurrence rate is slightly higher in patients after cryoablation. Prostate cancer recurrence was detected in patients of the ISUP 3 group. In patients after HIFU therapy, the quality of urination is higher than in patients of other groups, which can be associated with the laser enucleation of prostate hyperplasia performed by him. The advantage in patients after HIFU therapy was observed in the detection of IIEF-5, thus HIFU therapy had a better effect on the quality of life of patients with pathological prostate cancer.

Simple Summary: Prostate cancer is known to lose the capability to absorb and secrete zinc compared to normal prostate tissue, suggesting that the evaluation of zinc in prostate secretion can be a tool to identify the risk of developing cancer. In our study, we observed that the average amount of zinc detectable in urine after a prostatic massage is lower in patients with prostate cancer than in healthy subjects. Moreover, there is an inverse correlation between the concentration of urinary zinc and the tumor stage. This evidence suggests that the evaluation of urinary zinc may be a parameter for better diagnosis and prognosis of prostate cancer. 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. [ABSTRACT FROM AUTHOR]

ObjectiveGuidelines 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.MethodsPatients 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.ResultsAccording 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

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 [ABSTRACT FROM AUTHOR]

One of the dreadful diseases in the medical industry is prostate cancer and it is growing at a higher rate among men. Hence, it is a necessity to detect cancer in an early stage due to the alarming increase in the reports. Various techniques are introduced for effective prostate cancer detection using histopathology images. Accordingly, an automatic method is proposed for segmenting and classifying prostate cancer. This paper presents the prostate cancer detection method using histopathology images by proposing the fuzzy-based salp swarm algorithm-based rider neural network (SSA-RideNN) classifier. At first, the input image is fed to the pre-processing step and then the segmentation is performed using Color Space transformation and thresholding. Once the segmentation is performed, the feature extraction is done by extracting multiple kernel scale invariant feature transform features along with the texture features that are extracted based on local optimal oriented pattern descriptor to improve the classification accuracy. Finally, the prostate cancer detection is done based on the proposed fuzzy-based SSA-RideNN, which is developed by integrating fuzzy approach with SSA-RideNN. The performance of the proposed fuzzy-based SSA-RideNN is analyzed using sensitivity, specificity, and accuracy. The proposed fuzzy-based SSA-RideNN produces the maximum accuracy of 0.9190, a maximum sensitivity of 0.9084, and maximum specificity of 0.9, indicating its superiority. [ABSTRACT FROM AUTHOR]

Prostate cancer is a malignant tumor that occurs in the male prostate. Prostate cancer lesions have the characteristics of small size and blurry outline, which is a challenge to design a robust prostate cancer detection method. At present, clinical diagnosis of prostate cancer is mainly based on magnetic resonance (MR) imaging. However, it is difficult to obtain prostate cancer data, and the data with true values is also very limited, which further increases the difficulty of prostate cancer detection methods based on MR images. To solve these problems, this paper designs a new method of prostate cancer detection based on MR images, which is recorded as ProCDet. The method consists of three modules: registration of prostate MR images, segmentation of prostate, and segmentation of prostate cancer lesions. First, the registration between different sequences of MR images is performed to find the spatial relationship between the different sequences. Then, the designed prostate segmentation network based on the attention mechanism is used to segment the prostate to remove the interference of background information. Finally, a 3D prostate cancer lesion segmentation network based on Focal Tversky Loss is applied to determine the specific location of prostate cancer. Moreover, in order to take full advantage of unlabeled prostate data, this paper designs a self-supervised learning method to improve the accuracy of prostate cancer detection. The proposed ProCDet has been experimentally verified on the ProstateX dataset. When the average number of false-positive lesions per patient is 0.6275, the true-positive rate is 91.82%. Experimental results show that the ProCDet can obtain competitive detection performance.

Objective: To determine the diagnostic performance of multiparametric magnetic resonance imaging (mp-MRI) for prostate cancer (PCa) detection compared with MRI/ultrasound fusion-guided prostate biopsy. Materials and Methods: Pre-biopsy prostate mp-MRI of 100 consecutive men were retrospectively compared with prostate biopsy obtained by MRI/ultrasound fusion guidance between June 2017 and July 2018. Two experienced radiologists assigned PI-RADS score and localization the suspicious lesions by consensus. Tumor detection rates were calculated for each PI-RADS scores, Gleason scores, and tumor location. Results: Of the 151 target lesions on mp-MRI from 100 patients, 21% (31/151) were pathologically determined as clinically significant PCa. The detection rates on targeted biopsy for PI-RADS scores of 3, 4, and 5 were 10%, 29%, and 54%, respectively. No cancer was detected in PI-RADS score of 2 lesions. Higher level of suspicion PI-RADS scores indicated higher percentages of high Gleason scores. The percentages of PCa for Gleason scores of 7, 8, and 9 were 17%, 11%, and 0% for PI-RADS 3, 50%, 44%, and 25%, for PI-RADS 4, and 33%, 44%, and 75% for PI-RADS 5. High rates at 82% (23/28) of false-positive mp-MRI findings were found for small of less than 0.5 mL peripheral zone targets and 91%(10/11) of targets in the anterior location of the transition zone. Conclusion: PI-RADS v2 showed satisfactory performance for diagnosing clinically significant PCa and provided predictive information on tumor grade. High negative predictive values for PI-RADS v2 could be used in clinical management workflow to confidently avoid prostate biopsies. [ABSTRACT FROM AUTHOR]

Background: Early detection of prostate cancer has significant benefits for its treatment and can increase the survival chance in patients. In recent years, new methods such as shear wave elastography and vibro-elastography, as well as artificial tactile sensing, have been used to detect a mass in the prostate tissue in-vivo and ex-vivo. This paper aims to investigate the difference between normal and malignant prostate tissue reaction to simple and vibro-neural stimulation for prostate tissue mass detection in order to determine neural stimulation intensity, velocity, and frequency to obtain the best result in detecting the type and location of the tumor. Methods: This study has utilized neural stimulation devices in normal and cancerous tissues. The stimulation velocity, probe location, and the frequency of neural stimulation considered as the independent variables. Results: The results show that for superficial masses, although dependent on the probe, the accuracy of detection at the low speed of 5mm/s is 50% higher than other conditions. On the other hand, in deep masses, with increasing mass depth, the accuracy of detection at the medium speed of 8mm/s is 30% higher than the low speed. Finally, the results showed that with increased stimulation frequency, the possibility of tumor detection, and its accuracy increases by 35%. Conclusion: By improving the accuracy of the neural stimulation device, it can apply to detect hard materials such as tumors and malignant tissues.

Background: Transperineal (TP) biopsy is increasingly used as an alternative to standard transrectal (TR) biopsy for prostate cancer detection to reduce infection risks. However, evidence on comparative diagnostic accuracy remains inconclusive. The aim of this study was to perform an updated systematic review and meta-analysis of studies comparing prostate cancer detection rates between TP and TR ultrasound biopsies., Methods: PubMed, EMBASE, Web of Science and other databases were searched for relevant studies up to December 2023. Randomised trials and observational studies comparing TP and TR biopsies were included. Pooled risk ratios (RRs) with 95% confidence intervals (CIs) were calculated using random effects models. Heterogeneity was assessed, and subgroup analyses were conducted., Results: Nine studies comprising four randomised controlled trials (RCTs) and five observational studies were analysed, including 2763 patients (1376 TP, 1387 TR). No significant difference was found in overall cancer detection rates between TP and TR biopsies (RR = 0.9762, 95% CI = 0.8225-1.1586 for random effects model). However, subgroup analysis found that the RCTs showed no difference (RR = 0.9681, 95% CI = 0.8491-1.1038), whereas the observational studies varied (RR = 0.9416, 95% CI = 0.8073-1.0983). Significant heterogeneity was present across studies (I 2 = 64.3%, p = 0.0156). Details on the prostate specific antigen (PSA) levels in the included studies were provided, and no significant differences were found between TP and TR biopsies regardless of whether a PSA threshold of >10 ng/mL or <10 ng/mL was used., Conclusions: In summary, this updated meta-analysis found no significant difference between TP and TR biopsies in overall prostate cancer detection rates. The subgroup analysis highlighted that results from RCTs specifically indicated equivalence in diagnostic accuracy. TP biopsy may be considered an appropriate alternative to TR biopsy for patients requiring prostate biopsy., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s).)

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.

Hui Wang,1–3 Sheng Tai,1,2 Li Zhang,1,3 Jun Zhou,1–3 Chaozhao Liang1–31Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China; 2Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei, People’s Republic of China; 3The Institute of Urology, Anhui Medical University, Hefei, People’s Republic of ChinaPurposes: To develop a new predictor and update nomogram based on prostate imaging reporting and data system version 2 (PI-RADS V2) in predicting intermediate- and high-risk prostate cancer (IH-Pca) and reducing the overtreatment for low-risk Pca (L-Pca).Methods: All men that underwent trans-rectal ultrasound-guided 12+X-core prostate biopsy between January 2015 and June 2018 were collected and analyzed. The significant risks (SRs) of Pca were selected by univariate and multivariate analysis. All SRs were divided into four groups (0 to 3 points) based on the probability of PI-RADS. Each patient can obtain a total score (TS). The updated nomogram was established by R package version 3.0. The area under the curve (AUC), net reclassification index (NRI), calibration curves and decision curves were used to evaluate the diagnostic performance.Results: There were 1,078 patients, including 640 (59%) men with normal or L-Pca (N-LPca) and 438 (41%) men with IH-Pca. The scores of TS for IH-Pca and N-LPca were 16.13±3.11 and 10.52±3.32, respectively (P

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Among American men, prostate cancer is the cause of the second-highest death by any cancer. It is also the most common cancer in men worldwide, and the annual numbers are quite alarming. The most prognostic marker for prostate cancer is the Gleason grading system on histopathology images. Pathologists determine the Gleason grade on stained tissue specimens of Hematoxylin and Eosin (H&E) based on tumor structural growth patterns from whole slide images. Recent advances in Computer-Aided Detection (CAD) using deep learning have brought the immense scope of automatic detection and recognition at very high accuracy in prostate cancer like other medical diagnoses and prognoses. Automated deep learning systems have delivered promising results from histopathological images to accurate grading of prostate cancer. Many studies have shown that deep learning strategies can achieve better outcomes than simpler systems that make use of pathology samples. This article aims to provide an insight into the gradual evolution of deep learning in detecting prostate cancer and Gleason grading. This article also evaluates a comprehensive, synthesized overview of the current state and existing methodological approaches as well as unique insights in prostate cancer detection using deep learning. We have also described research findings, current limitations, and future avenues for research. We have tried to make this paper applicable to deep learning communities and hope it will encourage new collaborations to create dedicated applications and improvements for prostate cancer detection and Gleason grading.

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. [ABSTRACT FROM AUTHOR]

We analyzed the kinetic and kinematic variables of artificial tactile and artificial vibrotactile sensing test for mass detection in silicon phantom to determine tactile intensity and speed to obtain the best result in detecting the type and location of the mass. This study has utilized Artificial Tactile Sensing Instrument for Mass Detection (ATSIMD) in cylindrical silicone phantoms. The masses embedded in these samples were inserted in axial and environmental, deep and surface positions. The loading velocity, probe location, and the frequency of the applied force were considered as the independent variables in this study. It was found that for superficial mases the accuracy of detection at low speed 5 mm/sec, although dependent on the probe, but was 50% higher than under other conditions. For deep masses, with increasing mass depth, the accuracy of detection at medium speed of 8 mm/sec was 30% higher than at low speed. Mass detection by ATSIMD used in this study showed maximum efficiency at medium loading velocity. At low and high loading velocities, the dependence of mass detection on the probe location is related to the interaction of the testing method, tissue, and viscoelastic properties of the tissue. [ABSTRACT FROM AUTHOR]

Quite often, the available pre-biopsy data for early prostate cancer detection are imbalanced. When the least squares support vector machines (LS-SVMs) are applied to such scenarios, it becomes naturally desirable for us to introduce the well-known AUC performance index into the LS-SVMs framework to avoid bias towards majority classes. However, this may result in high computational complexity for the minimal leave-one-out error. In this paper, by introducing the parameter λ , a generalized Area under the ROC curve (AUC) performance index R AUCLS is developed to theoretically guarantee that R AUCLS linearly depends on the classical AUC performance index R AUC . Based on both R AUCLS and the classical LS-SVM, a new AUC-based least squares support vector machine called AUC-LS-SVMs is proposed for directly and effectively classifying imbalanced prostate cancer data. The distinctive advantage of the proposed classifier AUC-LS-SVMs exists in that it can achieve the minimal leave-one-out error by quickly optimizing the parameter λ in R AUCLS using the proposed fast leave-one-out cross validation (LOOCV) strategy. The proposed classifier is first evaluated using generic public datasets. Further experiments are then conducted on a real-world prostate cancer dataset to demonstrate the efficacy of our proposed classifier for early prostate cancer detection. [ABSTRACT FROM AUTHOR]

Purpose: The detection of clinically significant prostate cancer (PCa) is shown to greatly benefit from MRI–ultrasound fusion biopsy, which involves overlaying pre-biopsy MRI volumes (or targets) with real-time ultrasound images. In previous literature, machine learning models trained on either MRI or ultrasound data have been proposed to improve biopsy guidance and PCa detection. However, quantitative fusion of information from MRI and ultrasound has not been explored in depth in a large study. This paper investigates information fusion approaches between MRI and ultrasound to improve targeting of PCa foci in biopsies. Methods: We build models of fully convolutional networks (FCN) using data from a newly proposed ultrasound modality, temporal enhanced ultrasound (TeUS), and apparent diffusion coefficient (ADC) from 107 patients with 145 biopsy cores. The architecture of our models is based on U-Net and U-Net with attention gates. Models are built using joint training through intermediate and late fusion of the data. We also build models with data from each modality, separately, to use as baseline. The performance is evaluated based on the area under the curve (AUC) for predicting clinically significant PCa. Results: Using our proposed deep learning framework and intermediate fusion, integration of TeUS and ADC outperforms the individual modalities for cancer detection. We achieve an AUC of 0.76 for detection of all PCa foci, and 0.89 for PCa with larger foci. Results indicate a shared representation between multiple modalities outperforms the average unimodal predictions. Conclusion: We demonstrate the significant potential of multimodality integration of information from MRI and TeUS to improve PCa detection, which is essential for accurate targeting of cancer foci during biopsy. By using FCNs as the architecture of choice, we are able to predict the presence of clinically significant PCa in entire imaging planes immediately, without the need for region-based analysis. This reduces the overall computational time and enables future intra-operative deployment of this technology. [ABSTRACT FROM AUTHOR]

Objectives: To review the utilization of prostate-specific antigen (PSA) testing in Winnipeg, a major Canadian city, and to compare PSA testing rates between Winnipeg and Calgary, another major Canadian city of comparable size.Methods: PSA testing results were reviewed by year and age group. We focused our studies in years 2011 and 2016, for which census demographic data are available.Results: In Winnipeg, the PSA testing rates (patients with one or two PSA tests divided by the male population) showed a declining trend over years from 2008 to 2017. For almost all age groups, PSA testing rates in 2016 decreased in comparison to those in 2011. For age older than 40 years, the relative percentage decreases were 14% to 20%.In 2011, Winnipeg PSA testing rates were consistently higher than those in Calgary for all age groups. For age older than 40 years, the relative percentage differences were 36% to 50%.In addition, 41% and 40% of patients in Winnipeg who underwent PSA testing were younger than 50 years or older than 69 years in 2011 and 2016, respectively.Conclusions: PSA testing utilization may be falling short of optimal rates. There is a need to reinforce the optimal use of clinical recommendations. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the practicability of combining prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) for the detection of clinically significant prostate cancer (csPC) in an Asian population. Patients and methods: We prospectively enrolled patients who underwent prostate biopsy due to elevated serum prostate-specific antigen (PSA > 4 ng/mL) and/or abnormal digital rectal examination in a tertiary referral center. Before prostate biopsy, the serum samples were tested for PSA, free PSA, and p2PSA to calculate PHI. Besides, mpMRI was performed using a 3-T scanner and reported in the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2). The diagnostic performance of PHI, mpMRI, and combination of both was assessed. Result: Among 102 subjects, 39 (38.2%) were diagnosed with PC, including 24 (23.5%) with csPC (Gleason ≥ 7). By the threshold of PI-RADS ≥ 3, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to predict csPC were 100%, 44.9%, 35.8%, and 100%, respectively. By the threshold of PHI ≥ 30, the sensitivity, specificity, PPV, and NPV to predict csPC were 91.7%, 43.6%, 33.3%, and 94.4%, respectively. The area under the receiver operator characteristic curve of combining PHI and mpMRI was greater than that of PHI alone (0.873 vs. 0.735, p = 0.002) and mpMRI alone (0.873 vs. 0.830, p = 0.035). If biopsy was restricted to patients with PI-RADS 5 as well as PI-RADS 3 or 4 and PHI ≥ 30, 50% of biopsy could be avoided with one csPC patient being missed. Conclusion: The combination of PHI and mpMRI had higher accuracy for detection of csPC compared with PHI or mpMRI alone in an Asian population. [ABSTRACT FROM AUTHOR]

Featured Application: Integration of Conditional Random Fields into Convolutional Neural Networks as a hybrid end-to-end approach for prostate cancer detection on non-contrast-enhanced Magnetic Resonance Imaging. Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN. [ABSTRACT FROM AUTHOR]

Abstract Prostate cancer is a highly incident malignant cancer among men. Early detection of prostate cancer is necessary for deciding whether a patient should receive costly and invasive biopsy with possible serious complications. However, existing cancer diagnosis methods based on data mining only focus on diagnostic accuracy, while neglecting the interpretability of the diagnosis model that is necessary for helping doctors make clinical decisions. To take both accuracy and interpretability into consideration, we propose a stacking-based ensemble learning method that simultaneously constructs the diagnostic model and extracts interpretable diagnostic rules. For this purpose, a multi-objective optimization algorithm is devised to maximize the classification accuracy and minimize the ensemble complexity for model selection. As for model combination, a random forest classifier-based stacking technique is explored for the integration of base learners, i.e., decision trees. Empirical results on real-world data from the General Hospital of PLA demonstrate that the classification performance of the proposed method outperforms that of several state-of-the-art methods in terms of the classification accuracy, sensitivity and specificity. Moreover, the results reveal that several diagnostic rules extracted from the constructed ensemble learning model are accurate and interpretable. Highlights • We propose a stacking-based interpretable selective ensemble learning method. • We select ensemble models with accuracy and complexity under consideration. • We combine selected effective models by random forest-based stacking. • The proposed method is more accurate and interpretable in prostate cancer detection. • We extract a few of effective diagnostic rules for clinical decision support. [ABSTRACT FROM AUTHOR]

Rima Tinawi-Aljundi,1 Shannon T Knuth,2 Michael Gildea,2 Joshua Khal,2 Jason Hafron,1 Kenneth Kernen,1 Robert Di Loreto,1 Joan Aurich-Costa2 1Pathology and Research Department, Michigan Institute of Urology, St Clair Shores, MI, USA; 2Research and Development, Cellay, Inc., Cambridge, MA, USA Purpose: The ability to test for and detect prostate cancer with minimal invasiveness has the potential to reduce unnecessary prostate biopsies. This study was conducted as part of a clinical investigation for the development of an OligoFISH® probe panel for more accurate detection of prostate cancer.Materials and methods: One hundred eligible male patients undergoing transrectal ultrasound biopsies were enrolled in the study. After undergoing digital rectal examination with pressure, voided urine was collected in sufficient volume to prepare at least two slides using ThinPrep. Probe panels were tested on the slides, and 500 cells were scored when possible. From the 100 patients recruited, 85 had more than 300 cells scored and were included in the clinical performance calculations.Results: Chromosomes Y, 7, 10, 20, 6, 8, 16, and 18 were polysomic in most prostate carcinoma cases. Of these eight chromosomes, chromosomes 7, 16, 18, and 20 were identified as having the highest clinical performance as a fluorescence in situ hybridization test and used to manufacture the fluorescence in situ hybridization probe panels. The OligoFISH® probes performed with 100% analytical specificity. When the OligoFISH® probes were compared with the biopsy results for each individual, the test results highly correlated with positive and negative prostate biopsy pathology findings, supporting their high specificity and accuracy. Probes for chromosomes 7, 16, 18, and 20 showed in the receiver operator characteristics analysis an area under the curve of 0.83, with an accuracy of 81% in predicting the biopsy result.Conclusion: This investigation demonstrates the ease of use with high specificity, high predictive value, and accuracy in identifying prostate cancer in voided urine after digital rectal examination with pressure. The test is likely to have positive impact on clinical practice and advance approaches to the detection of prostate cancer. Further evaluation is warranted. Keywords: prostate cancer detection, OligoFISH®, oncology, PSA screening