Classification Of Prostate Cancer Research Articles

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.

Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method's effectiveness is verified on a fully annotated dataset.

Value of T2 Mapping MRI for Prostate Cancer Detection and Classification

ROI Measurements of benign tissue in the peripheral zone (PZ; purple ROI) and transition zone (TZ; orange ROI), and PCA (red ROI). Quantitative T2 map (A), T2 TSE (B), and calculatedadc map of RS-DWI (C). On Radical prostatectomy (RPE), an ISUP grade 5 PCA was confirmed. by Maximilian Klingebiel et al. (413–422)

Multi-parametric Magnetic Resonance Imaging Fusion for Automatic Classification of Prostate Cancer.

Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mp-MRI) has recently gained great research interest. In this work, a fully automatic CAD pipeline of PCa using mp-MRI data is presented. In order to fully explore the mp-MRI data, we systematically investigate three multi-modal medical image fusion strategies in convolutional neural networks, namely input-level fusion, feature-level fusion, and decision-level fusion. Extensive experiments are conducted on two datasets with different PCa-related diagnostic tasks. We identify a pipeline that works relatively the best for both diagnostic tasks, two important components of which are stacking three adjacent slices as the input and performing decision-level fusion with specific loss weights. Clinical relevance- This work provides a practical method for automated diagnosis of PCa based on multi-parametric MRI.

Multiclass Classification of Prostate Tumors Following an MR Image Analysis-Based Radiomics Approach.

Prostate cancer is one of the most common cancers in men, with symptoms that may be confused with those caused by benign prostatic hyperplasia. One of the key aspects of treating prostate cancer is its early detection, increasing life expectancy and improving the quality of life of those patients. However, the tests performed are often invasive, resulting in a biopsy. A non-invasive alternative is the magnetic resonance imaging (MRI)-based PI-RADS v2 classification. The aim of this work was to find objective biomarkers that allow the PI-RADS classification of prostate lesions using a radiomics approach on Multiparametric MRI. A total of 90 subjects were analyzed. From each segmented lesion, 609 different texture features were extracted using five different statistical methods. Two feature selection methods and eight multiclass predictive models were evaluated. This was a multiclass study in which the best AUC result was 0.7442 ± 0.0880, achieved with the Naïve Bayes model using a subset of 120 features. Valuable results were also obtained using the Random Forests model, obtaining an AUC of 0.7394 ± 0.0965 with a lower number of features (52). Clinical Relevance- The current study establishes a methodology for classifying prostate cancer and supporting clinical decision-making in a fast and efficient manner and avoiding additional invasive procedures using MRI.

Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer.

Background Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers. Aims To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes. Results Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups. Conclusions Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

Prostate Cancer Classification Using Artificial Neural Networks

Prostate cancer is one of the major causes of cancer death in men in the Western world. One in 6 men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs [Computer-aided detection of prostate cancer in MRI. IEEE, May 2014]. The artificial neural network (ANN) is used to classify and extract the feature of the magnetic resonance imaging (MRI) scan. This classification is computer-based and has the advantage that it enriches the patient from the doctor and undergoes the different diagnostic techniques that may appear to be stressful and painful. A computer-aided classification set of MRI scans of the prostate subjects these images to a set of preprocessing and image enhancement giving an accurate classification to improve the properties of the image such as noise filtering to improve the contrast of the image and then using segmentation to separate the tumor region and feature extraction; this step can be done by using a set of rolls (explained below). This feature is used to input data to ANN. It classifies the given data set into benign or malignant (prostate cancer). The system succeeded in classifying malignant tumors with 92.4%, increasing decision-making and providing particular treatment. The computerized method detects the tumor in its early stage; thus, good classification is an essential part. The first stage is to extract features from MRI using principal component analysis (computer aided diagnosis), and the second stage is to train the probabilistic neural network (ANN) for classification. The computer aided diagnosis method gives existing attributes a new set of calculated parameters.

Dynamic plasticity of prostate cancer intermediate cells during androgen receptor-targeted therapy.

Treatment-emergent small cell neuroendocrine prostate cancer (t-SCNC) is associated with an epithelial lineage switch from an androgen receptor (AR)-positive to neuroendocrine (NE)-marker-positive status. Understanding the potential for reversibility of this aggressive disease state has been hampered by the paucity of models suitable for studying rate-limiting, transitional, or intermediate tumor cell subpopulations. We define a dual reporter model that measures acute transcriptional changes in response to castration or AR targeting agents. We identify steady-state transcriptional heterogeneity in AR and NE biomarkers, including intermediate subpopulations that are coordinately high for prostate-specific antigen (PSA) and neuron-specific enoclase (NSE) promoter activity. In the presence of castration or AR inhibitors, intermediate cells were necessary and sufficient for therapy-induced conversion of human PC cells to an NSE-high transcriptional status. Using hormone add-back studies, treatment-induced PSA-NSE transcriptional plasticity was reversible in PTEN-deficient PC cells but not in the presence of secondary genetic driver genes, including MYCN.

Large Cell Neuroendocrine Carcinoma of the Prostate Gland: A Review and Update

It has been documented that the 2016 World Health Organization’s histological classification of prostate cancer has included well-differentiated carcinoid tumours and small- or large-cell poorly differentiated tumours within the category of neuroendocrine tumours. Up to May 2021, only 20 cases of large-cell neuroendocrine tumours of the prostate gland (LCNTPs) had been reported within the literature, among which are nine cases of primary tumours. LCNTPs are a rare histological entity whose evolutive profile and therapeutic potential do differ from those of conventional adenocarcinoma of the prostate gland. Primary neuroendocrine tumours of the prostate gland could be pure or associated with an adenocarcinoma component. Mixed forms of large cell neuroendocrine cancers of the prostate gland (LCNECPs) tend to associated with better prognosis when diagnosed early at a localized stage. Nevertheless, most cases of (LCNTPs) at the time of initial diagnosis could tend to be advanced, locally advanced or metastatic. Even though LCNECPs that are diagnosed initially tend to primary prostate cancers that may be pure primary prostate cancers, few large LCNECPs have been found to be metastatic large cell neuroendocrine carcinomas that had metastasised from other sites of the body for example the lung. Primary LCNECP does tend to manifest similarly to primary adenocarcinoma of prostate gland with lower urinary tract symptoms, haematuria, or signs of obstruction of the upper urinary tract or inability to empty the urinary bladder. Cases of primary LCNECP that manifest tend also to be associated with symptoms related to the sites of the metastases. Serum prostate specific antigen levels tend to be slightly elevated with cases of primary LCNECP, but the levels generally tend not to be as high as most cases of advanced, locally advanced or metastatic primary adenocarcinomas of the prostate gland. Diagnosis of LCNECPs tend to be made pursuant to the undertaking of histopathology and immunohistochemistry staining studies of specimens of the prostate that had been obtained from prostate biopsies, trans-urethral reception of prostate specimens or prostatectomy specimens. The microscopy histopathology examination features of LCNECP the prostate gland have been summarized as follows: (a) Microscopy histopathology examination of LCNECP gland does tend to demonstrate large islands or sheets of tumour cells. (b) Large tumour cells with prominent neuroendocrine features such as salt and pepper chromatin and small nucleoli tend to be visualised upon microscopy examination of specimens of the prostate tumour. (c) High grade features such as lack of glandular formation, frequent mitoses and apoptotic bodies and tumour necrosis tend to be frequent findings upon microscopy examination of specimens of LCNECP. Immunohistochemistry staining features of LCNECP include: (a) At least one of the ensuing neuroendocrine tumour markers should be demonstrated upon immunohistochemistry staining including exhibition of positive immunohistochemistry staining for: chromogranin A, synaptophysin, neuron specific enolase, and CD56 may be positive: It has been stated that immunohistochemistry staining studies may exhibit positive staining in cases of LCNECP with utilization of the following: o TTFI tends to be positive in less than 50% of cases of LCNECP. o Positive AMACR immunohistochemistry staining, but this may be focal or weaker than for adenocarcinoma of the prostate gland. o Positive markers including: PSA, PSMA, NKX3.1 prostein (P501S) tend to be negative in majority of cases of LCNECP, but they could be focally positive in a small subset of the tumours. It has been iterated that in cases of LCNECP, immunohistochemistry staining studies tend to demonstrate negative staining for the ensuing markers: • Urothelial markers such as GATA3, p63, and high molecular weight cytokeratins such as CK5 / 6. • CD99. • Carcinoid tumour of the prostate gland. • PNET/Ewing sarcoma of the prostate gland. • Adenocarcinoma of the prostate gland with focal neuroendocrine differentiation. • Urothelial carcinoma with neuroendocrine differentiation. A high index of suspicion is required in order to diagnose early cases of primary large cell neuroendocrine carcinomas by undertaking early biopsies of prostate glands in all patients who have significant lower urinary tract symptoms if their serum prostate specific antigen (PSA) levels are slightly high or high even if they are commenced on Tamsulosin in an attempt to help improve the symptoms of voiding. There is no consensus opinion on the best management of LCNECP, therefore, there is an urgent need for the establishment of a global multi-centre trial related to various management options for the disease in order to ascertain the best options of management for LCNECP. gland.

Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions.

The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of values from 1 to 5. The value is assigned according to the probability that a finding is a malignant tumor (prostate carcinoma) and is calculated by evaluating the signal behavior in morphological, diffusion, and post-contrastographic sequences. A PI-RADS score of 3 is recognized as the equivocal likelihood of clinically significant prostate cancer, making its diagnosis very challenging. While PI-RADS values of 4 and 5 make biopsy necessary, it is very hard to establish whether to perform a biopsy or not in patients with a PI-RADS score 3. In recent years, machine learning algorithms have been proposed for a wide range of applications in medical fields, thanks to their ability to extract hidden information and to learn from a set of data without previous specific programming. In this paper, we evaluate machine learning approaches in detecting prostate cancer in patients with PI-RADS score 3 lesions via considering clinical-radiological characteristics. A total of 109 patients were included in this study. We collected data on body mass index (BMI), location of suspicious PI-RADS 3 lesions, serum prostate-specific antigen (PSA) level, prostate volume, PSA density, and histopathology results. The implemented classifiers exploit a patient’s clinical and radiological information to generate a probability of malignancy that could help the physicians in diagnostic decisions, including the need for a biopsy.

Abstract 473: GeneMasking: A new approach for inferring the functional role of cancer driver genes and its application in prostate cancer

Abstract Introduction: Genomic events in cancer driver genes such as mutations and copy number alterations play critical roles in cancer onset and progression. Here we apply a novel biologically inspired deep learning classification method called GeneMasking to perform prostate cancer (PC) classification high accuracy. The important key novelty of GeneMasking is that it marks the genomic events that determine the classification of a given sample in a sample-specific manner. This provides us with an exciting opportunity to investigate two fundamental cancer research questions: 1.For a specific tumor sample, which cancer driver genes are functionally important, playing a detrimental role in its classification? 2.For a specific tumor sample, does an established cancer driver gene act as oncogene or tumor suppressor gene (TSG) in that sample? Methods: We applied GeneMasking to study these questions by analyzing the somatic mutation and copy number alteration (CNA) data of 1011 PC patient samples recently published by Elmarakeby et al. [Nature 598, 348-352 (2021)] and classify as primary or metastatic tumors. GeneMasking achieves an overall AUC (area under the receiver operating characteristic curve) of 0.958 in this classification task. Additionally and importantly, for each sample, GeneMasking provides a list of genes with mutation and/or CNA events that are important in classifying it. We focus on 61 known COSMIC cancer driver genes marked as important in ≥ 20 samples. A driver gene that is marked as important is inferred as oncogenic-acting in that given sample if it is amplified or mutated by an activating mutation, and vice versa for inferring it as a TSG-acting in that sample. For each gene, we compute the oncogene/TSG ratio of its inferred function across all samples where it was marked ‘important’. We then investigate whether this ratio is higher than 1 as expected for drivers traditionally labeled oncogenes, or lower, as expected for those known as TSGs. Results: Our key findings are: 1) For 44 drivers (including 5 PC-specific genes), the overall inferred oncogene/TSG ratios reassuringly match their conventional annotations as oncogenes or TSGs in COSMIC. 2) For 8 drivers, however, their inferred oncogene/TSG ratios are opposite from those listed in COSMIC. Those include MUC4, XPO1, FANCD2 etc. 3) 5 drivers are annotated as both oncogenes and TSGs in COSMIC and here we infer their specific roles in PC. Conclusions: We provide a first-of-its-kind computational approach to annotate cancer driver genes as oncogenes or TSGs in a patient cohort. This lays the basis for two future exciting applications: (a) Identifying the contexts in which a driver acts as oncogene or TSG, and (b) Help advance precision oncology, targeting actionable mutations of drivers only when they are truly oncogenic. Citation Format: Saugato Rahman Dhruba, Niv Amitay, Lior Wolf, Eytan Ruppin. GeneMasking: A new approach for inferring the functional role of cancer driver genes and its application in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 473.

Adversarial training for prostate cancer classification using magnetic resonance imaging.

To use adversarial training to increase the generalizability and diagnostic accuracy of deep learning models for prostate cancer diagnosis. This multicenter study retrospectively included 396 prostate cancer patients who underwent magnetic resonance imaging (development set, 297 patients from Shanghai Jiao Tong University Affiliated Sixth People's Hospital and Eighth People's Hospital; test set, 99 patients from Renmin Hospital of Wuhan University). Two binary classification deep learning models for clinically significant prostate cancer classification [PM1, pretraining Visual Geometry Group network (VGGNet)-16-based model 1; PM2, pretraining residual network (ResNet)-50-based model 2] and two multiclass classification deep learning models for prostate cancer grading (PM3, pretraining VGGNet-16-based model 3; PM4: pretraining ResNet-50-based model 4) were built using apparent diffusion coefficient and T2-weighted images. These models were then retrained with adversarial examples starting from the initial random model parameters (AM1, adversarial training VGGNet-16 model 1; AM2, adversarial training ResNet-50 model 2; AM3, adversarial training VGGNet-16 model 3; AM4, adversarial training ResNet-50 model 4, respectively). To verify whether adversarial training can improve the diagnostic model's effectiveness, we compared the diagnostic performance of the deep learning methods before and after adversarial training. Receiver operating characteristic curve analysis was performed to evaluate significant prostate cancer classification models. Differences in areas under the curve (AUCs) were compared using Delong's tests. The quadratic weighted kappa score was used to verify the PCa grading models. AM1 and AM2 had significantly higher AUCs than PM1 and PM2 in the internal validation dataset (0.84 vs. 0.89 and 0.83 vs. 0.87) and test dataset (0.73 vs. 0.86 and 0.72 vs. 0.82). AM3 and AM4 showed higher κ values than PM3 and PM4 in the internal validation dataset {0.266 [95% confidence interval (CI): 0.152-0.379] vs. 0.292 (95% CI: 0.178-0.405) and 0.254 (95% CI: 0.159-0.390) vs. 0.279 (95% CI: 0.163-0.396)} and test set [0.196 (95% CI: 0.029-0.362) vs. 0.268 (95% CI: 0.109-0.427) and 0.183 (95% CI: 0.015-0.351) vs. 0.228 (95% CI: 0.068-0.389)]. Using adversarial examples to train prostate cancer classification deep learning models can improve their generalizability and classification abilities.

Prostate cancer classification with MRI using Taylor-Bird Squirrel Optimization based Deep Recurrent Neural Network

ABSTRACT Prostate cancer is a type of cancer that develops in the prostate. The prostate is a little gland in men that resembles a walnut and secretes seminal fluid, which nourishes and transports sperm. A Taylor-Bird Squirrel Optimization based Deep Recurrent Neural Network (Taylor-BSO based Deep RNN) is created to determine the severity degree of prostate cancer classificationstart the diagnosis process . The Taylor series is combined with the Bird Swarm Algorithm (BSA) and Squirrel Search Algorithm (SSA), respectively, to create the Taylor-BSO. By filtering the MR image using the Hybrid Local and Non-Local Means (HLNLM) filtering model, the noise that was present in the input image is successfully eliminated. The procedure of cancer classification is designed for determining the existence or absence of a tumour using the features that were collected in the segmentation findings. The threshold number, however, categorizes the tumour severity level as either a high-grade or low-grade tumour. .

Therapeutic Implications for Intrinsic Phenotype Classification of Metastatic Castration-Resistant Prostate Cancer.

To determine whether metastatic castration-resistant prostate cancers (mCRPC) partition into molecular phenotypes corresponding to intrinsic differentiation states and ascertain whether these subtypes exhibit specific druggable features and associate with treatment outcomes. We used RNAseq, digital spatial profiling, and histological assessments from metastatic biopsies and patient-derived xenografts to segregate mCRPCs into subtypes defined by the PAM50 breast cancer classification algorithm. Subtype associations with treatment responses in preclinical models and patients were determined. Using the PAM50 algorithm, we partitioned 270 mCRPC tumors into LumA (42%), LumB (24%), and Basal (34%) subtypes with classification largely driven by proliferation rates and androgen receptor (AR) activity. Most neuroendocrine tumors classified as Basal. Pathways enriched in the LumA subtype include TGFß and NOTCH signaling. LumB subtype tumors were notable for elevated MYC activity. Basal subtype tumors exhibited elevated IL6-STAT3 signaling and features of adult stem cell states. In patients where multiple tumors were evaluated, the majority had concordant PAM50 subtype determination, though a subset exhibited marked inter- and intratumor heterogeneity, including divergent classifications between primary and metastatic sites. In preclinical models, LumA subtype tumors were highly responsive to androgen deprivation and docetaxel chemotherapy whereas Basal tumors were largely resistant. In clinical cohorts patients with Basal subtype tumors demonstrated a shorter time on treatment with AR signaling inhibitors and docetaxel relative to patients with luminal subtypes. Subtyping of mCRPC based on cell differentiation states has potential clinical utility for identifying patients with divergent expression of treatment targets and responses to systemic therapy.

Oncologic Outcomes of Total Length Gleason Pattern 4 on Biopsy in Men with Grade Group 2 Prostate Cancer.

Gleason Score 7 prostate cancer comprises a wide spectrum of disease risk, and precise substratification is paramount. Our group previously demonstrated that the total length of Gleason pattern (GP) 4 is a better predictor than %GP4 for adverse pathological outcomes at radical prostatectomy. We aimed to determine the association of GP4 length on prostate biopsy with post-prostatectomy oncologic outcomes. We compared 4 GP4 quantification methods-including maximum %GP4 in any single core, overall %GP4, total length GP4 (mm) across all cores and length GP4 (mm) in the highest volume core-for prediction of biochemical recurrence-free survival after radical prostatectomy using multivariable Cox proportional hazards regression. A total of 457 men with grade group 2 prostate cancer on biopsy subsequently underwent radical prostatectomy. The 3-year biochemical recurrence-free survival probability was 85% (95% CI 81-88). On multivariable analysis, all 4 GP4 quantification methods were associated with biochemical recurrence-maximum %GP4 (HR=1.30; 95% CI 1.07-1.59; p=0.009), overall %GP4 (HR=1.61; 95% CI 1.21-2.15; p=0.001), total length GP4 (HR=2.48; 95% CI 1.36-4.52; p=0.003) and length GP4 in highest core (HR=1.32; 95% CI 1.11-1.57; p=0.001). However, we were unable to identify differences between methods of quantification with a relatively low event rate. These findings support further studies on GP4 quantification in addition to the ratio of GP3 and GP4 to classify prostate cancer risk. Research should also be conducted on whether GP4 quantification could provide a surrogate endpoint for disease progression for trials in active surveillance.

Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network

Background and objectiveProstate 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. MethodsThis 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. ResultsAccording 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. ConclusionExperiments 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.

Identification of novel prostate cancer genes in patients stratified by Gleason classification: Role of antitumoral genes.

Prostate cancer (PCa) is a tumor with a great heterogeneity, both at a molecular and clinical level. Despite its global good prognosis, cases can vary from indolent to lethal metastatic and scientific efforts are aimed to discern those with worse outcomes. Current prognostic markers, as Gleason score, fall short when it comes to distinguishing these cases. Identification of new early biomarkers to enable a better PCa distinction and classification remains a challenge. In order to identify new genes implicated in PCa progression we conducted several differential gene expression analyses over paired samples comparing primary PCa tissue against healthy prostatic tissue of PCa patients. The results obtained show that this approach is a serious alternative to overcome patient heterogeneity. We were able to identify 250 genes whose expression varies along with tissue differentiation—healthy to tumor tissue, 161 of these genes are described here for the first time to be related to PCa. The further manual curation of these genes allowed to annotate 39 genes with antitumoral activity, 22 of them described for the first time to be related to PCa proliferation and metastasis. These findings could be replicated in different cohorts for most genes. Results obtained considering paired differential expression, functional annotation and replication results point to: CGREF1, UNC5A, C16orf74, LGR6, IGSF1, QPRT and CA14 as possible new early markers in PCa. These genes may prevent the progression of the disease and their expression should be studied in patients with different outcomes.

Looking for a Simplified Diagnostic Model to Identify Potentially Lethal Cases of Prostate Cancer at Initial Diagnosis: An ImGO Pilot Study.

Simple SummarySeveral genetic anomalies are recurrent in prostate cancer (PC) and allow this disease to be classified into distinct molecular subtypes; however, these anomalies have no predictive role and have limited relevance for clinical practice. Within this pilot study, metastatic castration-resistant PC (mCRPC) and metastatic castration-sensitive PC (mCSPC) were used for identifying and understanding lethal prostate tumor clones, from a genomic point of view. Some elements, such as a high Gleason Score and the presence of a cribriform pattern or intraductal carcinoma were evaluated as phenotypic markers of potentially lethal PC. Our results provide hypothesis-generating data, as the idea of evaluating mCSPC and mCRPC as a phenotypic/biologic model able to be translated in clinical practice. The confirmation of a high incidence of TP53 and BRCA2 mutations in larger trials may find a therapeutic implication through the choice of whether or not to use “more” therapy in respect to “selective” treatments approaches.The recurrent genetic anomalies used to classify prostate cancer (PC) into distinct molecular subtypes have limited relevance for clinical practice. In consideration of WHO 2016 histological classification, which includes the introduction of Gleason Score 4 for patients with cribriform component and the definition of intraductal carcinoma as a new entity, a retrospective pilot study was conducted to investigate, by histological review, if there were any variations of Gleason Score and the incidence of intraductal carcinoma and cribriform pattern, intended as “phenotypic” markers of potentially lethal PC, among metastatic castration-sensitive PC (mCSPC) and metastatic castration-resistant PC (mCRPC) samples. Potentially predictive factors were also assessed. Among 125 cases, a variation in the Gleason Score was reported in 26% of cases. A cribriform (36%) or intraductal (2%) pattern was reported in a higher percentage. Of them, a primary Gleason pattern 4 was reported in 80% of cases. All patients with intraductal carcinoma present a BRCA2 mutation, also found in 80% of cases with a cribriform pattern. This pilot study documented some hypothesis-generating data, as the evaluation of de novo mCSPC and mCRPC as phenotypic/biologic model to be translated in clinical practice. A cribriform pattern/intraductal carcinoma might be a marker of potentially lethal PC. The high incidence of TP53 and BRCA2 mutations in de novo mCSPC may also have a therapeutic implication.

Red Blood Cell Distribution Width Is Associated with All-cause Mortality but Not Adverse Cancer-specific Outcomes in Men with Clinically Localized Prostate Cancer Treated with Radical Prostatectomy: Findings Based on a Multicenter Shared Equal Access Regional Cancer Hospital Registry

BackgroundRecent reports with a small number of patients showed an association of red blood cell distribution width (RDW) with prostate cancer (PCa) progression. ObjectiveTo investigate whether preoperative RDW can serve as a prognostic marker in patients with PCa undergoing radical prostatectomy (RP) in a large, equal access, and diverse patient cohort. Design, setting, and participantsData were retrospectively collected on 4756 men treated with RP at eight Veteran Affairs medical centers within the Shared Equal Access Regional Cancer Hospital (SEARCH) database from 1999 through 2017. Outcome measurements and statistical analysisBiochemical recurrence (BCR) was the primary outcome, while metastasis, all-cause mortality (ACM), and prostate cancer–specific mortality (PCSM) were secondary outcomes. Results and limitationsThe mean (standard deviation) age was 62 yr (6.1), and 1589 (33%) men were black. The median (interquartile range) follow-up was 82 mo (46–127). Preoperative RDW either as a continuous variable or when stratified by quartiles was not associated with BCR. Likewise, preoperative RDW was not associated with metastases or PCSM. However, higher RDW was significantly associated with higher ACM, both as a continuous variable (p < 0.001) and when stratified by quartiles in univariable and multivariable models (p < 0.001). RDW was found to be correlated with D’Amico risk classification of PCa. Study limitations include its retrospective nature and lack of data regarding advanced PCa. ConclusionsPreoperative RDW was not associated with PCa outcomes in men treated with RP but was associated with ACM. While RDW may be a biomarker of overall health, it is not a biomarker for PCa outcomes. These results emphasize the importance of diverse, larger sized studies in genitourinary cancer research. Patient summaryProstate cancer includes a wide spectrum of diseases with different genetic, pathological, and oncological behaviors. Red blood cell distribution width is helpful in predicting the overall survival for a localized prostate cancer patient, and hence, it can help inform personalized treatment decisions and operative care.

Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation

Magnetic resonance elastography (MRE) for measuring viscoelasticity heavily depends on proper tissue segmentation, especially in heterogeneous organs such as the prostate. Using trained network-based image segmentation, we investigated if MRE data suffice to extract anatomical and viscoelastic information for automatic tabulation of zonal mechanical properties of the prostate. Overall, 40 patients with benign prostatic hyperplasia (BPH) or prostate cancer (PCa) were examined with three magnetic resonance imaging (MRI) sequences: T2-weighted MRI (T2w), diffusion-weighted imaging (DWI), and MRE-based tomoelastography, yielding six independent sets of imaging data per patient (T2w, DWI, apparent diffusion coefficient, MRE magnitude, shear wave speed, and loss angle maps). Combinations of these data were used to train Dense U-nets with manually segmented masks of the entire prostate gland (PG), central zone (CZ), and peripheral zone (PZ) in 30 patients and to validate them in 10 patients. Dice score (DS), sensitivity, specificity, and Hausdorff distance were determined. We found that segmentation based on MRE magnitude maps alone (DS, PG: 0.93 ± 0.04, CZ: 0.95 ± 0.03, PZ: 0.77 ± 0.05) was more accurate than magnitude maps combined with T2w and DWI_b (DS, PG: 0.91 ± 0.04, CZ: 0.91 ± 0.06, PZ: 0.63 ± 0.16) or T2w alone (DS, PG: 0.92 ± 0.03, CZ: 0.91 ± 0.04, PZ: 0.65 ± 0.08). Automatically tabulated MRE values were not different from ground-truth values (P>0.05). In conclusion, MRE combined with Dense U-net segmentation allows tabulation of quantitative imaging markers without manual analysis and independent of other MRI sequences and can thus contribute to PCa detection and classification.

18F-DCFPyL PET/CT in Newly Diagnosed Prostate Cancer: Diagnostic Value of Intraprostatic PSMA Uptake in Risk Classification of Prostate Cancer.

Purpose18F-DCFPyL prostate-specific membrane antigen (PSMA) PET/CT is commonly applied to locate lesions of prostate cancer (PCa), but its diagnostic function of quantitative parameters is ignored. Our study evaluates the parameters of intraprostatic PSMA uptake in patients newly diagnosed with PCa and explores their predictive value in risk classification, which is similar to D’Amico criteria.Materials and MethodsWe quantified the maximal standardized uptake value (SUVmax), mean SUV (SUVmean), total lesion (TL)-PSMA, prostate/muscle (P/M) ratio of the primary tumor, and PSMA-derived tumor volume (PSMA-TV) from 62 patients with histologically proven PCa. Patients newly diagnosed with PCa were allocated into risk groups (at low, intermediate, and high risk, respectively) in accordance with D’Amico criteria. Afterwards, the five parameters mentioned above among three different risk groups were compared, and their predictive values in the risk classification of PCa were explored.ResultsSignificantly decreased levels of SUVmax, SUVmean, TL-PSMA, and P/M ratio were observed in the risk groups of low or intermediate or both, compared with the high-risk group. However, only the P/M ratio significantly elevated in patients with intermediate risk [mean ± SD (median): 46.58 ± 9.74 (45.27), P = 0.042] or high risk [98.95 ± 38.83 (97.52), P < 0.001], compared with low-risk patients [12.33 ± 5.93 (9.81)]. When P/M ratio was used to distinguish between low-risk and intermediate-risk patients, its c-statistics was 0.660. On the other hand, when distinguishing between intermediate-risk and high-risk groups, the c-statistics of P/M ratio was 0.667. Finally, when P/M ratio was used to distinguish between low-risk and high-risk patients, the c-statistics was 0.969. P/M ratio had a positive correlation with prostate-specific antigen in all enrolled PCa patients.ConclusionThe quantitative parameters of 18F-DCFPyL PET/CT, including SUVmax, SUVmean, and P/M ratio, might assist in distinguishing low-risk or intermediate-risk groups from the high-risk group. Of these parameters, P/M ratio appears to be the better promising parameter for risk classification of prostate cancer than SUVmax.