Role In Diagnosis Of Prostate Cancer Research Articles

Prostate MRI for Improving Personalized Risk Prediction of Incontinence and Surgical Planning: The Role of Membranous Urethral Length Measurements and the Use of 3D Models.

Prostate MRI has an important role in prostate cancer diagnosis and treatment, including detection, the targeting of prostate biopsies, staging and guiding radiotherapy and active surveillance. However, there are other ''less well-known'' applications which are being studied and frequently used in our highly specialized medical center. In this review, we focus on two research topics that lie within the expertise of this study group: (1) anatomical parameters predicting the risk of urinary incontinence after radical prostatectomy, allowing more personalized shared decision-making, with special emphasis on the membranous urethral length (MUL); (2) the use of three-dimensional models to help the surgical planning. These models may be used for training, patient counselling, personalized estimation of nerve sparing and extracapsular extension and may help to achieve negative surgical margins and undetectable postoperative PSA values.

Micro-Ultrasound: Current Role in Prostate Cancer Diagnosis and Future Possibilities.

Prostate Cancer (PCa) is the second most common cancer in men. Population screening using prostate specific antigen (PSA) blood test and digital rectal exam (DRE) is recommended by the NCCN, EAU and other prominent clinical guidelines. While MRI is the recommended initial test in men at risk for PCa, micro-Ultrasound (MicroUS) is a novel high resolution ultrasound technology that has shown promise in PCa detection. This article provides a narrative review of the studies to date which have been conducted to evaluate the functionality and efficacy of MicroUS within the patient care pathway for prostate cancer. A total of 13 relevant publications comparing detection of csPCa between MicroUS and mpMRI were selected. An amount of 4 publications referring to use of MicroUS for other indications were found. Each publication was evaluated for risk of bias and applicability using the Quality Assessment of Diagnostic Accuracy (QUADAS-2) tool. The studies reviewed conclude that MicroUS detection rates for clinically significant prostate cancer diagnosis are comparable to the detection rates of mpMRI guided biopsy procedures. While the existing literature indicates that MicroUS should replace conventional TRUS for prostate imaging and biopsy, it is not yet clear whether MicroUS should be used on its own or in conjunction with mpMRI for augmenting prostate cancer detection. The ongoing OPTIMUM trial will provide evidence on how best to utilize this new technology. Early data also suggest this flexible new imaging modality has a place in local staging and active surveillance of prostate cancer as well as in bladder cancer staging.

Preclinical Evaluation of a Fibroblast Activation Protein and a Prostate-Specific Membrane Antigen Dual-Targeted Probe for Noninvasive Prostate Cancer Imaging.

Prostate-specific membrane antigen (PSMA) is a prostate cancer target that plays a crucial role in prostate cancer diagnosis and therapy. Herein, a novel dual-targeted imaging probe, [68Ga]Ga-FAPI-PSMA, was prepared by radiolabeling conjugated DOTA-FAPI-PSMA with the short half-life radionuclide gallium-68 (68Ga), which is dedicated to prostate cancer diagnostic imaging. In vitro, [68Ga]Ga-FAPI-PSMA had higher affinity for the PSMA and FAP high-expressing cell lines 22Rv1 and U87 MG with IC50 values of 4.73 and 2.10 nM, respectively, than in the corresponding negative expression cell lines PC3 and A549, and significant differences in cell uptake were also observed. In vivo, [68Ga]Ga-FAPI-PSMA was rapidly cleared from the body, and the estimated radiation dose was relatively low compared with several other FAPI probes. In 22Rv1 and U87 MG tumor xenografts, [68Ga]Ga-FAPI-PSMA rapidly accumulated in tumors after administration, and the best images can be obtained at 1 h postinjection. In conclusion, the dual-targeted probe [68Ga]Ga-FAPI-PSMA was successfully prepared for in vivo prostate cancer PET/CT imaging.

Diagnostic accuracy and clinical utility of micro-ultrasound guided biopsies in patients with suspected prostate cancer.

New technologies to improve quality of prostate biopsies are appearing in clinical practice.We evaluate the performance of a micro-ultrasound device and the Prostate Risk Identification using MicroUltraSound (PRI-MUS) score in detecting clinically significant prostate cancer (csPCa). We retrospectively reviewed data of 139 biopsy- naïve patients with suspicion of prostate cancer, who underwent diagnostic MRI and micro-ultrasonography (microUS), followed by transrectal prostatic biopsy (systematic ±targeted) under local anesthetic. The main objective was to evaluate the performance of the Prostate Risk Identification using MicroUltraSound (PRI-MUS) score in detecting csPCa, defined as International Society of Urological Pathology (ISUP) ≥2. Of all patients, 97 (70%) were found to have PCa, and 62 (45%) having csPCa.Among 100 patients with positive microUS (PRI-MUS score ≥3), 23 (23%) had ncsPCa and 57 (57%) were diagnosed with csPCa (ISUP ≥2); and in 39 patients with negative microUS, 12 (31%) were diagnosed with ncsPCa and 5 (13%) with csPCa.A PRI-MUS score ≥3 presented a sensitivity, specificity, positive predictive value and negative predictive value of 92%, 44%, 57% and 95%, respectively, for the detection of csPCa.The PRI-MUS score had higher areas under the curve than Prostate Imaging Reporting & Data System (PI-RADS) both for targeted (AUC 0.801 vs 0.733) and systematic + targeted (AUC 0.776 vs 0.694) biopsies for csPCa detection. In our cohort, microUS performed well as a diagnostic tool through an easily implementable scale. MicroUS presented similar sensitivity and higher specificity than MRI in detecting csPCa. Further multicenter prospective studies may clarify its role in prostate cancer diagnosis.

Current applications of ex-vivo fluorescent confocal microscope in urological practice: a systematic review of literature.

Histopathological examination, a cornerstone in diagnosing cancer, faces challenges due to its time-consuming nature. This review explores the potential of ex-vivo fluorescent confocal microscopy (FCM) in urology, addressing the need for real-time pathological assessment, particularly in prostate cancer. This systematic review aims to assess the applications of FCM in urology, including its role in prostate cancer diagnosis, surgical margin assessment, and other urological fields. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, a systematic search of PubMed and SCOPUS was conducted, focusing on English written original articles published after January 1, 2018, discussing the use of FCM in urological practice. The search included keywords related to FCM and urological terms. The risk of bias assessment was performed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A total of 17 relevant studies were included in the review that focuses on three main urological issues: prostate cancer (15 articles), bladder cancer (1 article), and renal biopsy (1 article). FCM exhibited significant promise in diagnosing prostate cancer. These studies reported an accuracy range of 85.33% to 95.1% in distinguishing between cancerous and non-cancerous prostate tissues. Moreover, FCM proved valuable for assessing surgical margins in real-time during radical prostatectomy, reducing the need for frozen section analysis. In some investigations, researchers explored the integration of artificial intelligence (AI) with FCM to automate diagnostic processes. Concerning bladder cancer, FCM played a beneficial role in evaluating urethral and ureteral margins during radical cystectomy. Notably, it showed substantial agreement with conventional histopathology and frozen section examination. In the context of renal biopsy, FCM demonstrated the potential to differentiate normal renal parenchyma from cancerous tissue, although the available evidence is limited in this area. The main limitation of the current study is the scarcity of data regarding the topic of interest. Ex-vivo FCM holds promise in urology, particularly in prostate cancer diagnosis and surgical margin assessment. Its real-time capabilities may reduce diagnostic delays and patient stress. However, most studies remain experimental, requiring further research to validate clinical utility.

An inception-based deep multiparametric net to classify clinical significance MRI regions of prostate cancer

Objective. Multi-parametric magnetic resonance imaging (MP-MRI) has played an important role in prostate cancer diagnosis. Nevertheless, in the clinical routine, these sequences are principally analyzed from expert observations, which introduces an intrinsic variability in the diagnosis. Even worse, the isolated study of these MRI sequences trends to false positive detection due to other diseases that share similar radiological findings. Hence, the main objective of this study was to design, propose and validate a deep multimodal learning framework to support MRI-based prostate cancer diagnosis using cross-correlation modules that fuse MRI regions, coded from independent MRI parameter branches. Approach. This work introduces a multimodal scheme that integrates MP-MRI sequences and allows to characterize prostate lesions related to cancer disease. For doing so, potential 3D regions were extracted around expert annotations over different prostate zones. Then, a convolutional representation was obtained from each evaluated sequence, allowing a rich and hierarchical deep representation. Each convolutional branch representation was integrated following a special inception-like module. This module allows a redundant non-linear integration that preserves textural spatial lesion features and could obtain higher levels of representation. Main results. This strategy enhances micro-circulation, morphological, and cellular density features, which thereafter are integrated according to an inception late fusion strategy, leading to a better differentiation of prostate cancer lesions. The proposed strategy achieved a ROC-AUC of 0.82 over the PROSTATEx dataset by fusing regions of K trans and apparent diffusion coefficient (ADC) maps coded from DWI-MRI. Significance. This study conducted an evaluation about how MP-MRI parameters can be fused, through a deep learning representation, exploiting spatial correlations among multiple lesion observations. The strategy, from a multimodal representation, learns branches representations to exploit radio-logical findings from ADC and K trans. Besides, the proposed strategy is very compact (151 630 trainable parameters). Hence, the methodology is very fast in training (3 s for an epoch of 320 samples), being potentially applicable in clinical scenarios.

Multiparametric ultrasound of prostate: role in prostate cancer diagnosis.

Recent advances in ultrasonography (US) technology established modalities, such as Doppler-US, HistoScanning, contrast-enhanced ultrasonography (CEUS), elastography, and micro-ultrasound. The early results of these US modalities have been promising, although there are limitations including the need for specialized equipment, inconsistent results, lack of standardizations, and external validation. In this review, we identified studies evaluating multiparametric ultrasonography (mpUS), the combination of multiple US modalities, for prostate cancer (PCa) diagnosis. In the past 5 years, a growing number of studies have shown that use of mpUS resulted in high PCa and clinically significant prostate cancer (CSPCa) detection performance using radical prostatectomy histology as the reference standard. Recent studies have demonstrated the role mpUS in improving detection of CSPCa and guidance for prostate biopsy and therapy. Furthermore, some aspects including lower costs, real-time imaging, applicability for some patients who have contraindication for magnetic resonance imaging (MRI) and availability in the office setting are clear advantages of mpUS. Interobserver agreement of mpUS was overall low; however, this limitation can be improved using standardized and objective evaluation systems such as the machine learning model. Whether mpUS outperforms MRI is unclear. Multicenter randomized controlled trials directly comparing mpUS and multiparametric MRI are warranted.

Automatic prostate segmentation of magnetic resonance imaging using Res-Net.

Segmenting the prostate from magnetic resonance images plays an important role in prostate cancer diagnosis and in evaluating the treatment response. However, the lack of a clear prostate boundary, heterogeneity of prostate tissue, large variety of prostate shape and scarcity of annotated training data makes automatic segmentation a very challenging task. In this work, we proposed a novel two stage segmentation method to automatically segment prostate to support accurate and reproducible results with multisite and multivendor dataset. In the proposed method, we use the combination U-Net with residual blocks. The proposed method comprises two stage neural network, first is 2D U-Net, used find the approximate location of prostate, the second is the combination of U-Net and Res-Net used for accurate segmentation of prostate. The network was trained on 116 patient datasets from three publicly available data sources. 80% of data is used for training, 10% for validation, and 10% for testing. The commonly used segmentation evaluation metrics Dice similarity coefficient (DSC), Sensitivity, and Specificity are used for quantitative evaluation of the network. With the proposed method average DSC value of 93.8%, Sensitivity value of 94.6% and Specificity of 99.3% was achieved on test datasets. Our experimental results show that the segmentation accuracy can be improved significantly using two stage neural networks.

Deep learning-based artificial intelligence applications in prostate MRI: brief summary.

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

Prostate-specific membrane antigen and fibroblast activation protein distribution in prostate cancer: preliminary data on immunohistochemistry and PET imaging

IntroductionFibroblast activation protein (FAP) has been recently presented as new imaging target for malignant diseases and offers high contrast to surrounding normal tissue. FAP tracer uptake has been reported in various tumor entities. The aim of this study was to compare FAP and Prostate-specific membrane antigen (PSMA) expression in primary prostate cancer employing histological analyses and PET imaging in two small patient collectives.MethodsTwo independent small patient collectives were included in this study. For cohort A, data of 5 prostate cancer patients and 3 patients with benign prostate hyperplasia were included. Patients with prostate cancer were initially referred for PSMA PET staging. Radical prostatectomy was performed in all patients and prostate specimen of patients and biopsies of healthy controls were available for further evaluation. Histological workup included HE and immunohistochemistry using PSMA Ab, FAP Ab. Cohort B consists of 6 Patients with diagnosed mCRPC and available PSMA as well as FAP PET.ResultsPatients with proven prostate cancer infiltration exhibited strong positivity for PSMA in both primary tumors and lymph node metastases while stainings for FAP were found positive in some cases, but not all (2/5). Controls with BPH presented moderate PSMA staining and in one case also with a positive FAP staining (1/3). PET imaging with FAP seemed to result in more precise results in case of low PSMA expression than PSMA-PET.ConclusionsWhile PSMA staining intensity is a valid indicator of prostate cancer in both primary tumor and lymph node metastases, the expression of FAP seems to be heterogeneous but not necessarily linked to cancer-associated fibroblasts. It is also present in inflammation-associated myofibroblasts. Therefore, its ultimate role in prostate cancer diagnosis remains a subject of discussion.

A Systematic Review of Artificial Intelligence in Prostate Cancer.

The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.

Transrectal ultrasound biopsy of the prostate: does it still have a role in prostate cancer diagnosis?

Transrectal ultrasound (TRUS) guided biopsy of the prostate has been a standard diagnostic approach for prostate cancer over the past thirty years. Today, the role of TRUS biopsy is being challenged by transperineal (TP) prostate biopsy due to concerns over the safety and diagnostic yield of TRUS biopsy. TRUS biopsy still offers a convenient, reliable and accessible tool for diagnosing prostate cancer in the majority of patients. It continues to play a role in prostate cancer diagnosis, especially where hospital resource allocation is limited, including the public sector. TRUS biopsy has low rates of severe complications, although there remains room for improvement in current practice to improve the tolerability and reduce the incidence of post-biopsy infection.

MRI in the Management of Prostate Cancer

Multiparametric MRI has a changing role in prostate cancer diagnosis. Internationally recognized consensus documents such as prostate imaging reporting and data system version have been developed and adapted to standardize the acquisition and reporting of prostate MRI. The improvement in scanning techniques and development of highly sensitive functional sequences have improved the detection of clinically significant prostate cancer as well as treatment planning and follow up. This has led to a recent NICE recommendation to use prostate MRI as the initial investigation in men with clinically suspected localized disease. The results of several recent international MRI prostate trials are influencing the way imaging is used to stratify which patients require a prostate biopsy as well as how MRI guidance is used to target biopsies.

Positron emission tomography (PET) radiotracers for prostate cancer imaging.

Imaging plays an increasing role in prostate cancer diagnosis and staging. Accurate staging of prostate cancer is required for optimal treatment planning. In detecting extraprostatic cancer and sites of early recurrence, traditional imaging methods (computed tomography, magnetic resonance imaging, radionuclide bone scan) have suboptimal performance. This leaves a gap between known disease recurrence as indicated by rising prostate-specific antigen and the ability to localize the recurrence on imaging. Novel positron emission tomography (PET) agents including radiolabeled choline, fluciclovine (18F-FACBC), and agents targeting prostate-specific membrane antigen are being developed and tested to increase diagnostic performance of non-invasive prostate cancer localization. When combined with CT or MRI, these tracers offer a combination of functional information and anatomic localization that is superior to conventional imaging methods. These PET radiotracers have varying mechanisms and excretion patterns affecting their pharmacokinetics and diagnostic performance, which will be reviewed in this article.

Laboratory role in prostate cancer diagnosis and management: a case report

This case report describes the clinical course of a prostate cancer case, from the first diagnosis until the exitus in four years and it wants to highlight the relevance of the Laboratory in managing this disease. This case report suggests that data provided by the laboratory can help the clinicians during the diagnostic and therapeutic course, allowing a robust disease monitoring and follow-up, compared to radiological or nuclear medicine diagnostic reports.

New trends in Prostate Cancer Imaging

Successful imaging of prostate cancer remains a major goal in everyday clinical practice. The widely adapted imaging techniques are limited by their low specificity and sensitivity especially for identification of lymph node or bone metastasis. A novel imaging technique with better outcomes would provide a significant aid and possibly increase the overall survival of patients suffering from prostate cancer. We review the literature about this interesting topic in an effort to clarify the value of these methods (if any) and their role in prostate cancer diagnosis.

Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect

Segmentation of the prostate from Magnetic Resonance Imaging (MRI) plays an important role in prostate cancer diagnosis. However, the lack of clear boundary and significant variation of prostate shapes and appearances make the automatic segmentation very challenging. In the past several years, approaches based on deep learning technology have made significant progress on prostate segmentation. However, those approaches mainly paid attention to features and contexts within each single slice of a 3D volume. As a result, this kind of approaches faces many difficulties when segmenting the base and apex of the prostate due to the limited slice boundary information. To tackle this problem, in this paper, we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation. In addition to utilizing the intraslice contexts and features, the proposed model also treats prostate slices as a data sequence and utilizes the interslice contexts to assist segmentation. The experimental results show that the proposed approach achieved significant segmentation improvement compared to other reported methods.

Stellenwert der MRT/TRUS-Fusionsbiopsie im Rahmen der Primärbiopsie beim Prostatakarzinom

Multiparametric magnetic resonance imaging (mpMRI) plays an emerging role in prostate cancer diagnosis. We compared the cancer detection rates of targeted biopsy (tB) of suspicious lesions in mpMRI versus systematic transperineal saturation biopsy (sB) in men with primary suspicion of prostate cancer (PCa). A total of 437 consecutive primary biopsy patients, who underwent transperineal systematic and fusion-guided biopsy between 2012 and 2014, were enrolled. mpMRI was evaluated based on PI-RADS. Analysis of biopsy specimen was performed following START criteria. Of the 437 men, 334 harbored 426 MR lesions. Overall, 274 PCa and 203 significant PCa (Gleason score (GS) ≥ 3 + 4, GS = 3 + 3 and PSA values ≥ 10 ng/ml) were detected. There were 52 (26 %) significant PCa exclusively found by sB, whereas only 18 (9 %) were identified by tB (p < 0.001). Of 80 high-grade PCa with GS ≥ 4 + 3, 70 were diagnosed by sB, and 60 by tB (p = 0.007). In addition, 54 % of all insignificant PCa (GS < 7, PSA < 10 ng/ml) were detected by sB alone (p < 0.001). AUC of mpMRI was 0.76-0.78. The combination of tB + sB detects PCa most accurately. Ongoing prospective (multicenter) studies are evaluating the status of the 12 core TRUS-guided random biopsy.

CANCER PROSTATE

Prostate specific antigen was identified and characterized first time in 1977. Thereis no doubt that the PSA has significant role in prostate cancer diagnosis with help of DigitalRectal Examination and biopsy. Study design: This study was designed to assess the level ofPSA in prostate cancer patients before and after the treatment given to patients. To determinethe specific and precise finding regarding the tumors stages, the cancer biomarkers are handyand helpful. For this purpose researchers monitor changes in the cell on chromosomal level bycomparing primary tumor with secondary tumor. The prostate-specific gene kallikrein 3 (KLK3),encodes PSA located on chromosome 19q13.4. KLK2 and KLK4 genes are also present herewhich are present in a family of fifteen closely related serine proteases. Setting: Allied Hospital,Faisalabad. Period: Jan 2014 to Dec 2014. Material and methods: We use the PSA (Human)CLIA Kit which is a solid phase two site immunoassay. One antibody was coated on the surfaceof the microtiter wells and another antibody (used as a tracer) was labeled with horseradishperoxidase. The PSA molecules present in the standard solution or serum were “sandwiched”between the two antibodies. Results: All prostate cancer patients had highly variable serumPSA values ranging from 12.90 ng/mL to 193.5 ng/mL. The post treatment PSA analysisrevealed that the increase level of PSA (15%) which showed that the PSA level increased afterthe treatment. Whereas in all other 17 patients the PSA level was found to decreased. Our studyalso shows that sometime PSA level is increased by non-cancer associated benign prostatichyperplasia (BPH), prostatitis, medications and environment and diet alterations. Conclusions:This study indicated that PSA serum level has a useful role in diagnosis of prostate cancer.

Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization.

Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.