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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 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

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

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.

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.

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

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]

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.

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]

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

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.

Background: To explore the diagnostic benefit of an additional image fusion of the sagittal plane in addition to the standard axial image fusion, using a sensor-based MRI/US fusion platform.Methods: During July 2013 and September 2015, 251 patients with at least one suspicious lesion on mpMRI (rated by PI-RADS) were included into the analysis. All patients underwent MRI/US targeted biopsy (TB) in combination with a 10 core systematic prostate biopsy (SB). All biopsies were performed on a sensor-based fusion system. Group A included 162 men who received TB by an axial MRI/US image fusion. Group B comprised 89 men in whom the TB was performed with an additional sagittal image fusion.Results: The median age in group A was 67 years (IQR 61-72) and in group B 68 years (IQR 60-71). The median PSA level in group A was 8.10 ng/ml (IQR 6.05-14) and in group B 8.59 ng/ml (IQR 5.65-12.32). In group A the proportion of patients with a suspicious digital rectal examination (DRE) (14 vs. 29%, p = 0.007) and the proportion of primary biopsies (33 vs 46%, p = 0.046) were significantly lower. The rate of PI-RADS 3 lesions were overrepresented in group A compared to group B (19 vs. 9%; p = 0.044). Classified according to PI-RADS 3, 4 and 5, the detection rates of TB were 42, 48, 75% in group A and 25, 74, 90% in group B. The rate of PCa with a Gleason score ≥7 missed by TB was 33% (18 cases) in group A and 9% (5 cases) in group B; p-value 0.072. An explorative multivariate binary logistic regression analysis revealed that PI-RADS, a suspicious DRE and performing an additional sagittal image fusion were significant predictors for PCa detection in TB. 9 PCa were only detected by TB with sagittal fusion (sTB) and sTB identified 10 additional clinically significant PCa (Gleason ≥7).Conclusion: Performing an additional sagittal image fusion besides the standard axial fusion appears to improve the accuracy of the sensor-based MRI/US fusion platform. [ABSTRACT FROM AUTHOR]

Prostate specific antigen (PSA) remains the most used biomarker in the management of early prostate cancer (PCa), in spite of the problems related to false positive results and overdiagnosis. New biomarkers have been proposed in recent years with the aim of increasing specificity and distinguishing aggressive from non-aggressive PCa. The emerging role of the prostate health index and the 4Kscore is reviewed in this article. Both are blood-based tests related to the aggressiveness of the tumor, which provide the risk of suffering PCa and avoiding negative biopsies. Furthermore, the use of urine has emerged as a non-invasive way to identify new biomarkers in recent years, including the PCA3 and TMPRSS2:ERG fusion gene. Available results about the PCA3 score showed its usefulness to decide the repetition of biopsy in patients with a previous negative result, although its relationship with the aggressiveness of the tumor is controversial. More recently, aberrant microRNA expression in PCa has been reported by different authors. Preliminary results suggest the utility of circulating and urinary microRNAs in the detection and prognosis of PCa. Although several of these new biomarkers have been recommended by different guidelines, large prospective and comparative studies are necessary to establish their value in PCa detection and prognosis. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative radiomics feature model for performing prostate cancer detection using multiparametric MRI (mpMRI). It incorporates a novel tumor candidate identification algorithm to efficiently and thoroughly identify the regions of concern and constructs a comprehensive radiomics feature model to detect tumorous regions. In contrast to conventional automated classification schemes, this radiomics-based feature model aims to ground its decisions in a way that can be interpreted and understood by the diagnostician. This is done by grouping features into high-level feature categories which are already used by radiologists to diagnose prostate cancer: Morphology, Asymmetry, Physiology, and Size (MAPS), using biomarkers inspired by the PI-RADS guidelines for performing structured reporting on prostate MRI. Clinical mpMRI data were collected from 13 men with histology-confirmed prostate cancer and labeled by an experienced radiologist. These annotated data were used to train classifiers using the proposed radiomics-driven feature model in order to evaluate the classification performance. The preliminary experimental results indicated that the proposed model outperformed each of its constituent feature groups as well as a comparable conventional mpMRI feature model. A further validation of the proposed algorithm will be conducted using a larger dataset as future work. [ABSTRACT FROM AUTHOR]

Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images from 40 patient studies. Nuclear shape based, architectural and textural features extracted from these segmentations were extracted and found to able to discriminate different Gleason grade patterns with a classification accuracy of 86% via a quadratic discriminant analysis (QDA) classifier. On average the AdACM model provided 60% savings in computational times compared to a non-optimized hybrid active contour model involving a shape prior. [ABSTRACT FROM AUTHOR]

Developing an artificial intelligence-based prostate cancer detection and diagnosis system that can automatically determine important regions and accurately classify the determined regions on an input biopsy image. The Yolo general-purpose object detection algorithm was utilized to detect important regions (for the localization task) and to grade the detected regions (for the classification task). The algorithm was re-trained with our prostate cancer dataset. The dataset was created by annotating 500 real prostate tissue biopsy images. The dataset was split into train/test parts as 450/50 real prostate tissue images, respectively, before the data augmentation process. Next, the training set consisting of 450 labeled biopsy images was pre-processed with the data augmentation method. This way, the number of biopsy images in the dataset was increased from 450 to 1776. Then, the algorithm was trained with the dataset and the automatic prostate cancer detection and diagnosis tool was developed. The developed tool was tested with two test sets. The first test set contains 50 images that are similar to the train set. Hence, 97% detection and classification accuracy has been achieved. The second test set contains 137 completely different real prostate tissue biopsy images, thus, 89% detection accuracy has been achieved. In this study, an automatic prostate cancer detection and diagnosis tool was developed. The test results show that high-accuracy (high-performance) prostate cancer diagnosis tools can be developed using AI (computer vision) methods such as object detection algorithms. These systems can decrease the inter-observer variability among pathologists, and help prevent the time delay in the diagnosis phase. • Automated prostate cancer detection and diagnosis system was developed. • The system was developed by applying the Gleason grading on prostate tissue images. • A new PCa dataset was created by a pathologist and reviewed by two other pathologists. • The fine-tuned Yolo object detection algorithm was re-trained via the dataset. • Grading outcomes of the Yolo algorithm was fused with ISUP to make the final decision. [ABSTRACT FROM AUTHOR]

Abstract: We present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains. [Copyright &y& Elsevier]

Controversial data on sarcosine as a promising biomarker for prostate cancer (PCa) detection are present. The objective was to clarify these discrepancies and reevaluate the potential value of sarcosine in PCa. Sarcosine algorithms (supernatant and sediment sarcosine/creatinine, supernatant and sediment log2 (sarcosine/alanine)) in urine samples from 71 untreated patients with PCa, 39 patients with no evidence of malignancy (NEM) and 20 healthy women and men were quantified by liquid chromatography/tandem mass spectrometry. Although any sarcosine algorithms were significantly higher in PCa patients than in NEM patients (all P<0.05), comparable sarcosine values were measured in healthy women and men. Additionally, neither biopsy Gleason score nor clinical T-stage were correlated with sarcosine algorithms (all P>0.05), and receiver operating characteristic curve analysis indicated that the diagnostic power of any of sarcosine algorithms was nonsignificantly higher than that of serum and urine PSA, but nonsignificantly lower than prostate cancer antigen 3 (PCA3) and the percent-free PSA (%fPSA). Improved diagnostic performances were observed when any of sarcosine algorithms was combined with PCA3 or %fPSA. In conclusion, the predictive power of sarcosine in PCa is modest compared with PCA3 and %fPSA. Sarcosine, which awaits more validation before it reaches the clinic, could be included into the list of candidate PCa biomarkers. [ABSTRACT FROM AUTHOR]

We reviewed the status of prostate cancer diagnosis in Western Australia (WA) with the aim of improving decision-making about PSA testing and prostate biopsy. Our patient cohort was 5145 men undergoing an initial biopsy for prostate cancer diagnosis in WA between 1998 and 2004. Transrectal ultrasound-guided biopsies were performed by one of 18 clinicians whereas all pathology was assessed by one urological pathologist. Cancer detection rates were 59% for initial biopsies and 32% for repeat biopsies. High-grade cancer (Gleason sum 7) accounted for 69 and 38% of tumours diagnosed on initial and repeat biopsy, respectively. The rates of cancer diagnosis and detection of high-grade tumours were both 1.6-fold higher in WA patients compared with those obtained at baseline screening of the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial of US men (P<0.001). These higher than expected rates of cancer detection and high histological grade indicate that urological practice in WA between 1998 and 2004 was significantly more conservative than US practice over this time period, probably leading to underdiagnosis of prostate cancer. Our findings may be relevant to other countries where urological practice differs from that in the United States. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer (PCa) represents a very important health problem worldwide. Used as the main screening method for almost four decades, PSA (Prostate Specific Antigen) has proven its limitations. In this review, the authors try to make an evaluation of the biomarkers commercially available and used to improve the PCa detection in patients with elevated PSA. The authors also present the current PCa screening and diagnosis protocols in Romania. 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. [ABSTRACT FROM AUTHOR]