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Widespread adoption of mpMRI has led to a decrease in the number of patients requiring prostate biopsies. 68Ga-PSMA-11 PET/CT has demonstrated added benefits in identifying csPCa. Integrating the use of these imaging techniques may hold promise for predicting the presence of csPCa without invasive biopsy. A retrospective analysis of 42 consecutive patients who underwent mpMRI, 68Ga-PSMA-11 PET/CT, prostatic biopsy, and radical prostatectomy (RP) was carried out. A lesion-based model (n = 122) using prostatectomy histopathology as reference standard was used to analyze the accuracy of 68Ga-PSMA-11 PET/CT, mpMRI alone, and both in combination to identify ISUP-grade group ≥ 2 lesions. 68Ga-PSMA-11 PET/CT demonstrated greater specificity and positive predictive value (PPV), with values of 73.3% (vs. 40.0%) and 90.1% (vs. 82.2%), while the mpMRI Prostate Imaging Reporting and Data System (PI-RADS) 4-5 had better sensitivity and negative predictive value (NPV): 90.2% (vs. 78.5%) and 57.1% (vs. 52.4%), respectively. When used in combination, the sensitivity, specificity, PPV, and NPV were 74.2%, 83.3%, 93.2%, and 51.0%, respectively. Subgroup analysis of PI-RADS 3, 4, and 5 lesions was carried out. For PI-RADS 3 lesions, 68Ga-PSMA-11 PET/CT demonstrated a NPV of 77.8%. For PI-RADS 4-5 lesions, 68Ga-PSMA-11 PET/CT achieved PPV values of 82.1% and 100%, respectively, with an NPV of 100% in PI-RADS 5 lesions. A combination of 68Ga-PSMA-11 PET/CT and mpMRI improved the radiological diagnosis of csPCa. This suggests that avoidance of prostate biopsy prior to RP may represent a valid option in a selected subgroup of high-risk patients with a high suspicion of csPCa on mpMRI and 68Ga-PSMA-11 PET/CT.

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Prostate cancer is one of the most challenging malignancies due to its high incidence and prevalence, as it is the most frequently diagnosed non-skin cancer in men. The timely identification of prostate cancer and its metastasis is paramount for ensuring favorable outcomes for patients. Prostate-specific membrane antigen (PSMA) emerges as a promising biomarker for its detection, due to its specificity. This makes it an ideal target for the early identification of a metastatic phenotype. Situated on the membrane of tumor cells, PSMA facilitates the attachment of PSMA-targeting particles, enabling their detection through positron emission tomography (PET) scans with relative ease. Utilizing these imaging agents in conjunction with PET scans enhances the accuracy of prostate cancer tumor detection compared to PET scans alone. The advancement in prostate cancer imaging has paved the way for innovative treatment modalities. Prostate-specific membrane antigen-targeted radionuclide therapies (PSMA-TRT) exploit PSMA imaging agents to target identified prostate cancer malignancies with precise radiation, thereby reducing or eliminating the tumor mass. PSMA-TRT exhibits significant promise in prostate cancer therapy, evident from the notable declines in prostate-specific antigen (PSA) levels post treatment. However, PSMA-TRT carries both beneficial and adverse effects. While it represents a substantial leap forward in tumor cell imaging, PSMA-based antigens, being larger particles than ligands, offer prolonged imaging capabilities. Yet, the long-term effects of PSMA-TRT remain unknown, with the short-term adverse ones including fatigue, nausea, pain flares, and potential radiation exposure to others.

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This study investigates the feasibility of a simple electrochemical detection of Prostate Cancer Antigen 3 (PCA3) fragments extracted from patients' urine, using a thiolated single-strand DNA probe immobilized on a gold surface without using a redox probe. To enhance the PCA3 recognition process, we conducted a comparative analysis of the hybridization location using two thiolated DNA probes: Probe 1 targets the first 40 bases, while Probe 2 targets the fragment from bases 47 to 86. Hybridization with PCA3 followed, using square wave voltammetry. The limit of detection of the designed genosenors were of the order of (2.2 ng/mL), and (1.6 ng/mL) for Probes 1 and 2, respectively, and the subsequent sensitivities were of the order of (0.09 ± 0.01) µA-1 · µg-1 · mL and (0.10 ± 0.01) µA-1 · µg-1 · mL. Specificity tests were then conducted with the sensor functionalized with Probe 2, as it presents better analytical performances. The electrochemical results indicate that the designed sensor can clearly discriminate a complementary target from a non-complementary one. A further modeling of the calibration curves with the Power Law/Hill model indicates that the dissociation constant increases by one order of magnitude, confirming the ability of the designed sensor to perfectly discriminate complementary targets from non-complementary ones.

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OBJECTIVE: Prostate cancer (PCa) is the second disease threatening men's health, and anti-androgen therapy (AAT) is a primary approach for treating this condition. Increasing evidence suggests that long non-coding RNAs (lncRNAs) play crucial roles in the development of PCa and the process of AAT resistance. The objective of this study is to utilize bioinformatics methods to excavate lncRNAs association with AAT resistance and investigate their biological functions. METHODS: AAT resistance-related risk score model (ARR-RSM) was established by multivariate Cox analysis. Paired clinical tissue samples of 36 PCa patients and 42 blood samples from patients with PSA over 4 ng/ml were collected to verify the ARR-RSM. In vitro, RT-qPCR, CCK-8 and clone formation assays were displayed to verify the expression and function of AL354989.1 and AC007405.2. RESULTS: Pearson correlation analysis identified 996 lncRNAs were associated with AAT resistance (ARR-LncRs). ARR-RSM was established using multivariate Cox regression analysis, and PCa patients were divided into high-risk and low-risk groups. High-risk patients showed increased expression of AL354989.1 and AC007405.2 had poorer prognoses. The high-risk score correlated with advanced T-stage and N-stage. The AUC of ARR-RSM outperformed tPSA in diagnosing PCa. Silencing of AC007405.2 and AL354989.1 inhibited PCa cells proliferation and AAT resistance. CONCLUSIONS: In this study, we have discovered the clinical significance of AC007405.2 and AL354989.1 in predicting the prognosis and diagnosing PCa patients. Furthermore, we have confirmed their correlation with various clinical features. These findings provide potential targets for PCa treatment and a novel diagnostic and predictive indicator for precise PCa diagnosis.

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OBJECTIVES: To investigate the effect of the COVID-19 pandemic on prostate cancer incidence, prevalence, and mortality in England. PATIENTS AND METHODS: With the approval of NHS England and using the OpenSAFELY-TPP dataset of 24 million patients, we undertook a cohort study of men diagnosed with prostate cancer. We visualised monthly rates in prostate cancer incidence, prevalence, and mortality per 100 000 adult men from January 2015 to July 2023. To assess the effect of the pandemic, we used generalised linear models and the pre-pandemic data to predict the expected rates from March 2020 as if the pandemic had not occurred. The 95% confidence intervals (CIs) of the predicted values were used to estimate the significance of the difference between the predicted and observed rates. RESULTS: In 2020, there was a drop in recorded incidence by 4772 (31%) cases (15 550 vs 20 322; 95% CI 19 241-21 403). In 2021, the incidence started to recover, and the drop was 3148 cases (18%, 17 950 vs 21 098; 95% CI 19 740-22 456). By 2022, the incidence returned to the levels that would be expected. During the pandemic, the age at diagnosis shifted towards older men. In 2020, the average age was 71.6 (95% CI 71.5-71.8) years, in 2021 it was 71.8 (95% CI 71.7-72.0) years as compared to 71.3 (95% CI 71.1-71.4) years in 2019. CONCLUSIONS: Given that our dataset represents 40% of the population, we estimate that proportionally the pandemic led to 20 000 missed prostate cancer diagnoses in England alone. The increase in incidence recorded in 2023 was not enough to account for the missed cases. The prevalence of prostate cancer remained lower throughout the pandemic than expected. As the recovery efforts continue, healthcare should focus on finding the men who were affected. The research should focus on investigating the potential harms to men diagnosed at older age.

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We designed a highly sensitive fluorescent sensor for the early detection of sarcosine, a potential biomarker for prostate cancer. This sensor was based on surface-cobalt-doped fluorescent carbon quantum dots (Co-CD) using a FRET-based photoluminescent sensing platform. Blue luminescent carbon quantum dots (CQD) were synthesised through a hydrothermal approach, utilizing Delonix regia tree pod shells. Cobalt was employed to functionalize the CQD, enhancing the quantum-entrapped effects and minimizing surface flaws. To optimize Co-CD preparation, we employed a Box-Behnken design (BBD), and response surface methodology (RSM) based on single-factor experiments. The Co-CD was then used as a fluorescent probe for selective Cu2+ detection, with Cu2+ quenching Co-CD fluorescence through an energy transfer process, referred to as 'turn-off'. When sarcosine was introduced, the fluorescence intensity of Co-CD was restored, creating a 'turn-on' response. The sensor exhibited a Cu2+ detection limit (LOD) of 2.4 µM with a linear range of 0 µM to 10 µM. The sarcosine detection in phosphate buffer saline (PBS, pH 7.4) resulted in an LOD of 1.54 µM and a linear range of 0 to 10 µM. Importantly, the sensor demonstrated its suitability for clinical analysis by detecting sarcosine in human urine. In summary, our rapid and highly sensitive sensor offers a novel approach for the detection of sarcosine in real samples, facilitating early prostate cancer diagnosis.Communicated by Ramaswamy H. Sarma.

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OBJECTIVES: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). PATIENTS AND METHODS: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%-20%. RESULTS: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63-72) years, 9 (7-15) ng/mL, and 0.20 (0.13-0.32) ng/mL2 , respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD. CONCLUSIONS: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.

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BACKGROUND: The prospective randomized PRECISE trial demonstrated that magnetic resonance imaging (MRI) with only targeted biopsy (TBx) was noninferior to systematic transrectal ultrasound biopsy (SBx) in the detection of International Society of Urological Pathology grade group (GG) ≥2 prostate cancer (PC). An unanswered question is the outcome for patients who avoided a biopsy because of negative MRI findings. OBJECTIVE: To explore the rate of PC diagnosis based on 2-yr MRI for PRECISE participants who had no biopsy and for patients who had a negative result or GG 1 on TBx in comparison to those with a negative result or GG 1 on SBx. DESIGN, SETTING, AND PARTICIPANTS: The PRECISE prospective trial was conducted at five Canadian academic centers. The present analysis was for trial participants who were not diagnosed with clinically significant PC (csPC) at baseline. Of 453 randomized patients, 146 were diagnosed with GG ≥2 at baseline and were excluded. Eligible patients for this study included 83 men from the MRI arm who had negative MRI findings and no biopsy, 120 from the overall cohort who had a negative SBx or TBx, and 72 from the overall cohort who were diagnosed with GG 1 disease. INTERVENTION: MRI at 2 yr in all men in the MRI and SBx arms and TBx for lesions with a Prostate Imaging-Reporting and Data System score of ≥3 or on the basis of clinical suspicion. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was the proportion of men diagnosed with GG ≥2 cancer. Secondary outcomes included the MRI outcome and the proportion of men diagnosed with GG 1 PC. RESULTS AND LIMITATIONS: Evaluable 2-yr MRI scans were available for 75 (56%) eligible patients in the MRI arm and 69 (49%) in the SBx arm. Of these patients, 55 (73%) in the MRI arm and 51 (67%) SBx arm had negative 2-yr MRI. Of the 76 patients in the SBx arm with 2-yr MRI, 16 (21%) had a biopsy, for which the result was negative in eight (10%), GG1 in two (2.6%), and GG ≥2 in six (7.9%) cases. Of the 75 men in the MRI arm with 2-yr MRI, eight (11%) were biopsied, for which the result was negative in four cases (5%) and GG ≥2 in the other four (5%). At 2 yr, including baseline biopsy results, 116/221 (52.5%) in the MRI arm and 113/204 (55%) in the SBx arm were free of GG ≥2 disease, treatment, death from any cause, or progression (OR 1.08; p = 0.66). CONCLUSIONS: After 2-yr follow-up including MRI for patients in both arms of PRECISE, there was no difference in the rate of csPC diagnosis between the MRI and SBx groups, even though 38% of men in the MRI group avoided an initial biopsy. PATIENT SUMMARY: The PRECISE trial compared systematic biopsy of the prostate to a strategy of magnetic resonance imaging (MRI) with targeted biopsy of any lesions suspicious for cancer on the scan. After 2 years of follow-up that included 2-year MRI with or without biopsy in both groups, there was no difference in the rate of diagnosis of significant cancer, even though 38% of men in the initial MRI arm avoided an initial biopsy, and 30% avoided biopsy altogether. The PRECISE trial is registered on ClinicalTrials.gov as NCT02936258.

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PURPOSE: This study aims to compare the ability of the PHI versus tPSA test to predict the presence of PCa in our population. METHODS: A prospective observational study was performed. We included patients with tPSA ≥ 2.5 ng/ml, biopsy naïve or previous negative biopsy, undergoing a blood test, which includes tPSA, fPSA, and p2PSA, and a prostate biopsy between March 2019 and March 2022. Patients with PCa found in the biopsy-Group A-were compared with patients with a negative biopsy result-Group B. Diagnostic accuracy of tPSA and PHI was assessed by receiver operating characteristic [ROC] curves and logistic regression. RESULTS: 140 men were included. Fifty-seven (40.7%) had a positive prostate biopsy result (Group A), and 83 (59.3%) had a negative biopsy result (Group B). The mean age was similar in both groups (mean ± standard deviation), 66.86 ± 6.61 years. No difference was found in the tPSA value between the groups (Group A PSA: 6.11 ng/ml (3.56-17.01); Group B: 6.42 ng/ml (2.46-19.45), p = 0.41). The mean value of PHI was statistically different between groups (Group A 65.50 (29-146) vs. Group B 48 (16-233), p = 0.0001). The area under the curve 0.44 for tPSA and 0.77 for PHI. The multivariate logistic regression model applied to PHI showed a significant increase in its predictive accuracy: 72.14% in the model without PHI, 76.09% with PHI. CONCLUSION: The PHI test improves PCa detection compared to tPSA in our population.

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A calibrated palpation sensor has been developed for making instrumented Digital Rectal Examinations (iDREs) with a view to assessing patients for prostate cancer. The instrument measures the dynamic stiffness of the palpable surface of the prostate, and has been trialled on 12 patients in vivo. The patients had been diagnosed with prostate cancer and were scheduled for radical prostatectomy. As far as possible, patients with asymmetric disease were chosen so as to give a variation in gland condition over the palpable surface. The device works by applying an oscillating pressure (force) to a flexible probe whose displacement into the tissue is also measured in order to yield a dynamic stiffness, the static stiffness being incidentally measured at the mean oscillatory force. The device was deployed mounted on the index finger of a urologist and measurements taken at 12-16 positions on each patient using light and firm pressure and palpation frequencies of 1 or 5 Hz. In parallel, conventional DRE assessments were made by a consultant urologist for cancer. After in vivo measurement, the glands were removed and examined histologically with each palpation point being classified as cancerous (C) or not (NC). The work has established the first measurements of static modulus of living prostate tissue to be: 26.8 (13.3) kPa for tissue affected by prostate cancer (C classification), and 24.8 kPa (11.9) for tissue unaffected by cancer (NC classification), values quoted as median (interquartile range). The dynamic properties were characterised by: dynamic modulus, 5.15 kPa (4.86) for the C classification and 4.61 kPa (3.08) for the NC classification and the time lag between force and displacement at 5 Hz palpation frequency, 0.0175 s (0.0078) for the C classification and 0.0186 s (0.0397) for the NC classification, values again quoted as median (interquartile range). With the limited set of features that could be generated, an Artificial Neural Network (ANN) classification yielded a sensitivity of 97%, negative predictive value of 86%, positive predictive value of 67% and accuracy of 70% but with relatively poor specificity (30%). Besides extending the feature set, there are a number of changes in probe design, probing strategy and in mechanics analysis, which are expected to improve the diagnostic capabilities of the method.

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PURPOSE: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men in Western and Asian countries. Serum prostate-specific antigen (PSA) test has been the routine diagnostic method despite the tremendous research in diagnostic markers for early detection of PCa. A shift towards a promising and potential biomarker for PCa detection is through metabolomic profiling of biofluids, particularly the blood and urine samples. Finding reliable, routinely usable circulating metabolite biomarkers may not be a distant reality. METHODS: We performed a PubMed-based literature search of metabolite biomarkers in blood and urine for the early detection of prostate cancer. The timeline of these searches was limited between 2007 and 2022 and the following keywords were used: 'metabolomics', 'liquid biopsy', 'circulating metabolites', 'serum metabolite', 'plasma metabolite', and 'urine metabolite' with respect to 'prostate cancer'. We focussed only on diagnosis-based studies with only the subject-relevant articles published in the English language and excluded all of the other irrelevant publications that included prostate tissue biomarkers and cell line biomarkers. RESULTS: We have consolidated all the blood and urine-based potential metabolite candidates in individual as well as panels, including lipid classes, fatty acids, amino acids, and volatile organic compounds which may become useful for PCa diagnosis. CONCLUSION: All these metabolome findings unveil the impact of different dimensions of PCa development, giving a promising strategy to diagnose the disease since suspected individuals can be subjected to repeated and largescale blood and urine testing.

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PURPOSE: Markers are needed to increase the diagnostic accuracy of prostate-specific antigen (PSA) in prostate cancer (PCa) screening. Mounting evidence has shown that plasma proteins can be hopeful biomarkers for cancer diagnosis. METHODS: Tandem mass tag (TMT)-based proteomics and parallel reaction monitoring (PRM) analysis were used to screen the differential proteins and further validated in other independent studies (n = 539). Receiver-operating characteristic (ROC), decision curves and nomograms were applied to assess the diagnostic accuracy of biomarkers. RESULTS: Three candidate proteins (DBP, LCAT and ORM2) were preliminarily screened. Subsequent validation studies revealed significant upregulation of ORM2 in PCa patients across other independent cohorts. ORM2 yielded excellent discriminative power for PCa from benign prostatic hyperplasia (BPH) patients (AUC = 0.861 and 0.814 in validation phases 2a and 2b, respectively). Importantly, the combination of ORM2 and PSA gave better predictive accuracy than PSA alone. We incorporated age, PSA and ORM2 into a nomogram, which yielded C-index of 0.883 in validation phase 2a. A similar C-index of 0.879 was obtained in external validation phase 2b. CONCLUSIONS: In summary, our study suggests that ORM2 could be treated as a complementary biomarker for PSA in distinguishing PCa from BPH.

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BACKGROUND: The Rotterdam Prostate Cancer Risk Calculator (RPCRC) and Stockholm3 can be used to aid urologists in their decision to refer men to magnetic resonance imaging (MRI) or biopsy for early detection of prostate cancer. OBJECTIVE: To assess the external validity of the RPCRC and compare it with using PSA and Stockholm3 to detect clinically significant prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: Using data from the prospective, population-based, randomised STHLM3-MRI screening trial, we included participants with prostate-specific antigen (PSA) ≥3 ng/ml or Stockholm3 risk threshold ≥11% in the standard group who underwent systematic prostate biopsies. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Probabilities for clinically significant prostate cancer (csPC, International Society of Urological Pathology grade ≥2) were calculated for each participant using the RPCRC and Stockholm3 with and without prostate volume. Performance of the risk calculators was assessed by discrimination, calibration, and clinical benefits. RESULTS AND LIMITATIONS: In total, 666 men with a median age of 67 yr (interquartile range [IQR]: 61-71) and PSA of 3.4 ng/ml (2.5-5.0) were included, of whom 154 (23%) had csPC. Risk distribution of the RPCRC was narrow: median risks of 2% (IQR 1-4%) compared with 14% (IQR: 9.5-23%) for Stockholm3. Using RPCRC's recommended risk threshold of ≥4% for finding csPC, 54% of all csPC cases would be detected versus 94% using Stockholm3 with a threshold of ≥11%. Calibration of Stockholm3 was adequate while RPCRC underestimated the risk of csPC. The Stockholm3 test showed positive net benefits at clinically relevant thresholds, while the RPCRC showed negative net benefits. Compared with PSA, the RPCRC was associated with lower detection of csPC (84 vs 103; 0.82 [0.71-0.93]), while Stockholm3 was associated with higher detection of csPC (143 vs 103; 1.40 [1.23-1.57]). The main limitation was that Stockholm3 was evaluated in a similar population to where it was developed. CONCLUSIONS: The performance of the RPCRC in a Swedish population-based cohort is suboptimal with a considerable underestimation of prostate cancer risk, while the Stockholm3 test showed superior performance and a positive clinical benefit. PATIENT SUMMARY: The use of the Rotterdam Prostate Cancer Risk Calculator available online to predict the risk of prostate cancer in a Swedish cohort was found to be clinically harmful as it underpredicted the risk of clinically significant prostate cancer, while the Stockholm3 test performed well showing clinical benefits.

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Prostate cancer（PCa）is the most common malignant tumor in male genitourinary system. In China, the incidence of PCa is increasing significantly, which seriously endangers the physical and mental health of Chinese men. Programmed cell death（PCD）is a kind of active and orderly cell death mode, which exists widely in the process of life activities. With the deepening understanding of PCD, more and more studies have found that different types of PCD are closely related to PCa.This article mainly reviews therole of programmed cell death in the diagnosis and treatment of prostate cancer.

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The rapid evolution of image processing equipment and techniques ensures the development of novel picture analysis methodologies. One of the most powerful yet computationally possible algebraic techniques for measuring the topological characteristics of functions is persistent homology. It's an algebraic invariant that can capture topological details at different spatial resolutions. Persistent homology investigates the topological features of a space using a set of sampled points, such as pixels. It can track the appearance and disappearance of topological features caused by changes in the nested space created by an operation known as filtration, in which a parameter scale, in our case the intensity of pixels, is increased to detect changes in the studied space over a range of varying scales. In addition, at the level of machine learning there were many studies and articles witnessing recently the combination between homological persistence and machine learning algorithms. On another level, prostate cancer is diagnosed referring to a scoring criterion describing the severity of the cancer called Gleason score. The classical Gleason system defines five histological growth patterns (grades). In our study we propose to study the Gleason score on some glands issued from a new optical microscopy technique called SLIM. This new optical microscopy technique that combines two classic ideas in light imaging: Zernike's phase contrast microscopy and Gabor's holography. Persistent homology features are computed on these images. We suggested machine learning methods to classify these images into the corresponding Gleason score. Machine learning techniques applied on homological persistence features was very effective in the detection of the right Gleason score of the prostate cancer in these kinds of images and showed an accuracy of above 95%.

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A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

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Prostate cancer is one of the malignancies that affects men and significantly contributes to increased mortality rates in men globally. Patients affected with prostate cancer present with either a localized or advanced disease. In this review, we aim to provide a holistic overview of prostate cancer, including the diagnosis of the disease, mutations leading to the onset and progression of the disease, and treatment options. Prostate cancer diagnoses include a digital rectal examination, prostate-specific antigen analysis, and prostate biopsies. Mutations in certain genes are linked to the onset, progression, and metastasis of the cancer. Treatment for localized prostate cancer encompasses active surveillance, ablative radiotherapy, and radical prostatectomy. Men who relapse or present metastatic prostate cancer receive androgen deprivation therapy (ADT), salvage radiotherapy, and chemotherapy. Currently, available treatment options are more effective when used as combination therapy; however, despite available treatment options, prostate cancer remains to be incurable. There has been ongoing research on finding and identifying other treatment approaches such as the use of traditional medicine, the application of nanotechnologies, and gene therapy to combat prostate cancer, drug resistance, as well as to reduce the adverse effects that come with current treatment options. In this article, we summarize the genes involved in prostate cancer, available treatment options, and current research on alternative treatment options.

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The detection of cancer biomarkers is of great significance for the early screening of cancer. Detecting the content of sarcosine in blood or urine has been considered to provide a basis for the diagnosis of prostate cancer. However, it still lacks simple, high-precision and wide-ranging sarcosine detection methods. In this work, a Ti3 C2 TX /Pt-Pd nanocomposite with high stability and excellent electrochemical performance has been synthesized by a facile one-step alcohol reduction and then used on a glassy carbon electrode (GCE) with sarcosine oxidase (SOx ) to form a sarcosine biosensor (GCE/Ti3 C2 TX /Pt-Pd/SOx ). The prominent electrocatalytic activity and biocompatibility of Ti3 C2 TX /Pt-Pd enable the SOx to be highly active and sensitive to sarcosine. Under the optimized conditions, the prepared biosensor has a wide linear detection range to sarcosine from 1 to 1000 µM with a low limit of detection of 0.16 µM (S/N = 3) and a sensitivity of 84.1 µA/mM cm2 . Besides, the reliable response in serum samples shows its potential in the early diagnosis of prostate cancer. More importantly, the successful construction and application of the amperometric biosensor based on Ti3 C2 TX /Pt-Pd will provide a meaningful reference for detecting other cancer biomarkers.

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BACKGROUND AND OBJECTIVE: Prostate cancer is the most common cancer of the male reproductive system. With the development of medical imaging technology, magnetic resonance images (MRI) have been used in the diagnosis and treatment of prostate cancer because of its clarity and non-invasiveness. Prostate MRI segmentation and diagnosis experience problems such as low tissue boundary contrast. The traditional segmentation method of manually drawing the contour boundary of the tissue cannot meet the clinical real-time requirements. How to quickly and accurately segment the prostate tumor has become an important research topic. METHODS: This paper proposes a prostate tumor diagnosis based on the deep learning network PSP-Net+VGG16. The deep convolutional neural network segmentation method based on the PSP-Net constructs a atrous convolution residual structure model extraction network. First, the three-dimensional prostate MRI is converted to two-dimensional image slices, and then the slice input of the two-dimensional image is trained based on the PSP-Net neural network; and the VGG16 network is used to analyze the region of interest and classify prostate cancer and normal prostate. RESULTS: According to the experimental results, the segmentation method based on the deep learning network PSP-Net is used to identify the data set samples. The segmentation accuracy is close to the Dice similarity coefficient and Hausdorff distance, and even exceeds the traditional prostate image segmentation method. The Dice index reached 91.3%, and the technique is superior in speed of processing. The predicted tumor markers are very close to the actual markers manually by clinicians; the classification accuracy and recognition rates of prostate MRI based on VGG16 are as high as 87.95% and 87.33%, and the accuracy rate and recall rate of the network model are relatively balanced. The area under curve index is also higher than other models, with good generalization ability. CONCLUSION: Experiments show that prostate cancer diagnosis based on the deep learning network PSP-Net+VGG16 is superior in accuracy and processing time compared to other algorithms, and can be well applied to clinical prostate tumor diagnosis.

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OBJECTIVE: To determine the effects if cycling and rowing on serum prostate-specific antigen (PSA) levels. METHODS: Male volunteers (n = 101), aged 20-80 (mean, 49.9) years were randomized to exercise at the first or second study visit. They performed 1 h of either cycling or rowing on a stationary machine. To determine exercise-induced effects on the PSA level, serum total PSA (tPSA) and free PSA (fPSA) concentrations were evaluated before and after exercise and another sampling was performed at the second study visit. Pre-exercise and postexercise tPSA and fPSA concentrations were compared using the Wilcoxon matched-pairs test. The results were analyzed using the Mann-Whitney U-test. RESULTS: A significant (p < 0.001) average increase in tPSA after exercise (1.14 ± 1.11 ng/ml to 1.24 ± 1.26 ng/ml [mean, +8.8%]) was observed after both cycling and rowing, without significant differences between the sports (p = 0.54). The exercise-induced increase in PSA concentration affected participants aged ≥50 years (difference, 0.16 ± 0.37; p < 0.001), but not those aged <50 years (difference, 0.01 ± 0.06; p = 0.23). The effect size was clinically irrelevant in all except two outliers, in whom a distinct increase of PSA level by averages of 1.80 ng/ml (+55%) for tPSA and 1.25 ng/ml (+227%) for fPSA following cycling was observed. CONCLUSION: Rowing and cycling generally do not have a clinically relevant effect on PSA levels. However, outliers exist. Our findings do not support abstaining from exercise during the days approaching PSA sampling.

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