Improve Prostate Cancer Diagnosis Research Articles

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively.

Point-of-Care Prostate Specific Antigen Testing: Examining Translational Progress toward Clinical Implementation.

Prostate cancer (PCa) is the second most common male cancer and is attributable to over 375,000 deaths annually. Prostate specific antigen (PSA) is a key biomarker for PCa and therefore measuring patient PSA levels is an important aspect of the diagnostic pathway. Automated immunoassays are currently utilized for PSA analysis, but they require a laboratory setting with specialized equipment and trained personnel. This results in high diagnostic costs, extended therapeutic turnaround times, and restrictions on testing capabilities in resource-limited settings. Consequently, there is a strong drive to develop point-of-care (PoC) PSA tests that can offer accurate, low-cost, and rapid results at the time and place of the patient. However, many emerging PoC tests experience a trade-off between accuracy, affordability, and accessibility which distinctly limits their translational potential. This review comprehensively assesses the translational advantages and limitations of emerging laboratory-level and commercial PoC tests for PSA determination. Electrochemical and optical PSA sensors from 2013 to 2023 are systematically examined. Furthermore, we suggest how the translational potential of emerging tests can be optimized to achieve clinical implementation and thus improve PCa diagnosis globally.

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning.

To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images. Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis. The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010). A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

Four-quadrant vector mapping of hybrid multidimensional MRI data for the diagnosis of prostate cancer.

The interpretation of prostate multiparametric magnetic resonance imaging (MRI) is subjective in nature, and there is large inter-observer variability among radiologists and up to 30% of clinically significant cancers are missed. This has motivated the development of new MRI techniques and sequences, especially quantitative approaches to improve prostate cancer diagnosis. Using hybrid multidimensional MRI, apparent diffusion coefficient (ADC) and T2 have been shown to change as a function of echo time (TE) and b-values, and that this dependence is different for cancer and benign tissue, which can be exploited for prostate cancer diagnosis. The purpose of this study is to investigate whether four-quadrant vector mapping of hybrid multidimensional MRI (HM-MRI) data can be used to diagnose prostate cancer (PCa) and determine cancer aggressiveness. Twenty-one patients with confirmed PCa underwent preoperative MRI prior to radical prostatectomy. Axial HM-MRI were acquired with all combinations of TE=47, 75, 100ms and b-values of 0, 750, 1500s/mm2 , resulting in a 3×3 data matrix associated with each voxel. Prostate Quadrant (PQ) mapping analysis represents HM-MRI data for each voxel as a color-coded vector in the four-quadrant space of HM-MRI parameters (a 2D matrix of signal values for each combination of b-value and TE) with associated amplitude and angle information representing the change in T2 and ADC as a function of b-value and TE, respectively. Cancers have a higher PQ4 (22.50%±21.27%) and lower PQ2 (69.86%±28.24%) compared to benign tissue: peripheral, transition, and central zone (PQ4=0.13%±0.56%, 5.73%±15.07%, 2.66%±4.05%, and PQ2=98.51%±3.05%, 86.18%±21.75%, 93.38%±9.88%, respectively). Cancers have a higher vector angle (206.5±41.8°) and amplitude (0.017±0.013) compared to benign tissue. PQ metrics showed moderate correlation with Gleason score (|ρ|=0.388-0.609), with more aggressive cancers being associated with increased PQ4 and angle and reduced PQ2 and amplitude. A combination of four-quadrant analysis metrics provided an area under the curve of 0.904 (p<0.001) for the differentiation of prostate cancer from benign prostatic tissue. Four-quadrant vector mapping of HM-MRI data provides effective cancer markers, with cancers associated with high PQ4 and high vector angle and lower PQ2 and vector amplitude.

Systematic or targeted fusion-guided biopsy

Early detection of prostate cancer (PCa) is associated with ahigh risk for detecting low-risk disease. In the primary biopsy indication, systematic biopsy leads to an increased detection of clinically insignificant PCa, and significant prostate cancers are not detected with sufficient sensitivity, especially without prior magnetic resonance imaging (MRI). Similar data have recently become available for PCa screening. In light of the current literature, this article aims to discuss the data on systematic and combined targeted and systematic multiparametric MRI (mpMRI)-guided fusion biopsy to improve PCa diagnosis in clinically suspected cancer even in screening using multivariable risk stratification. Literature review on mpMRI and MRI/TRUS fusion biopsy (TRUS: transrectal ultrasonography) for tumor detection in suspected prostate cancer and PCa screening was performed. Multiparametric MRI as areflex test after prostate-specific antigen (PSA) determination (PSA cut-off 4 ng/ml) in combination with targeted biopsy alone reduces the detection of clinically nonsignificant tumors in early detection by half. On the other hand, in the form of atarget saturation or in combination with asystematic biopsy, the sensitivity for the detection of cancers of International Society of Urogenital Pathology (ISUP) grade groups2 or higher can be improved. Similar results are also shown in PCa screening with aPSA cut-off of 3 ng/ml. The evidence for performing atargeted fusion biopsy alone is currently insufficient. Therefore, the combination of mpMRI-guided targeted and systematic biopsy continues to be the recommended standard for prostate cancer diagnosis.

Prospective study of prostate cancer detection using multiparametric magnetic resonance imaging ultrasound-guided fusion, standard, and saturation biopsy

Background. Currently, about 80 % of men with low-grade prostate cancer (per ISUP 1 (International Society of Urological Pathology)) have indications for radical treatment. Overdiagnosis of low-grade cancer is associated with the use of systematic biopsy methods (standard transrectal, saturation) under ultrasound control for diagnosis verification. To improve prostate cancer diagnosis, the European Association of Urology (2019) recommended multiparametric magnetic resonance imaging before biopsy, and in case of detection of a suspicious lesion magnetic resonance imaging (MRI)-targeted biopsy. In clinical practice, the most common method of MRI-targeted biopsy is multiparametric MRI ultrasound-guided (mpMRI/US) fusion biopsy. However, some studies show contradictory results in detection of prostate cancer using systematic and MRI-targeted biopsy techniques.Aim. To compare detection of clinically significant prostate cancer (ISUP ≥2) using mpMRI/US fusion, standard, and saturation biopsy.Materials and methods. The study included 96 patients. The following inclusion criteria were applied: prostate-specific antigen &gt;2 ng/mL and/or detection of a suspicious lesion during digital rectal and/or transrectal ultrasound examination, and PI-RADS (Prostate Imaging Reporting and Data System) v.2.1 score ≥3. At the first stage, “unblinded” urologist performed a transperineal mpMRI/US fusion and saturation biopsies. At the second stage, “blinded” urologist performed standard transrectal biopsy. Clinically significant cancer was defined as ISUP ≥2.Results. Median age was 63 years, prostate volume – 47 cm3, prostate-specific antigen – 6.82 ng/mL. MpMRI/US fusion, standard, and saturation biopsies were comparable in regard to the rate of detection of clinically significant (29, 24, 28 %; p = 0.81) and clinically insignificant (25, 26, 35 %; p = 0.43) cancer. Overall prostate cancer detection rates were also similar: 54, 50, 63 %, respectively (p = 0.59). The percentages of positive cores in mpMRI/US fusion, standard, and saturation biopsies were 33, 10 and 13 %, respectively (p &lt;0.01). The maximal core length in mpMRI/US was 6.4 mm, in standard biopsy – 6.35 mm, in saturation biopsy – 5.1 mm (p = 0.7).Conclusion. Detection rates of clinically significant, clinically insignificant prostate cancer and overall detection rate are comparable between systematic biopsy techniques and mpMRI/US fusion biopsy.

New model of PIRADS and adjusted prostatespecific antigen density of peripheral zone improves the detection rate of initial prostate biopsy: a diagnostic study.

This study explored a new model of Prostate Imaging Reporting and Data System (PIRADS) and adjusted prostate-specific antigen density of peripheral zone (aPSADPZ) for predicting the occurrence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The demographic and clinical characteristics of 853 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), PSAD of peripheral zone (PSADPZ), aPSADPZ, and peripheral zone volume ratio (PZ-ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with the calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves. The AUCs of PSA, PSAD, PSADPZ, aPSADPZ, and PZ-ratio were 0.669, 0.762, 0.659, 0.812, and 0.748 for PCa diagnosis, while 0.713, 0.788, 0.694, 0.828, and 0.735 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of PCa or csPCa. The new model significantly improved the diagnostic accuracy of PCa (0.945 vs 0.830, P < 0.01) and csPCa (0.937 vs 0.845, P < 0.01) compared with the base model. In addition, the number of patients with PCa and csPCa predicted by the new model was in good agreement with the actual number of patients with PCa and csPCa in high-risk threshold. This study demonstrates that aPSADPZ has a higher predictive accuracy for PCa diagnosis than the conventional indicators. Combining aPSADPZ with PIRADS can improve PCa diagnosis and avoid unnecessary biopsies.

High Throughput Isolation and Data Independent Acquisition Mass Spectrometry (DIA-MS) of Urinary Extracellular Vesicles to Improve Prostate Cancer Diagnosis

Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.

The evolving role of contrast-enhanced ultrasound in urology: a review.

Ultrasound's versatility and ease of use has expanded its application in many clinical settings. Technological advancements with contrast-enhanced ultrasound (CEUS) have allowed high quality imaging similar to CT or MRI with lower risk of contrast toxicity and radiation exposure. In this review article we examine the development of CEUS and its vast applications in the field of urology. A PubMed literature search was performed using keywords: contrast enhanced ultrasound, prostate cancer, renal cancer, and multiparametric ultrasound. The development of CEUS has improved transrectal ultrasound imaging with increased detection of prostate cancer (PCa). Further enhancements of CEUS such as subharmonic imaging (SHI), flash replenishment imaging (FRI) and contrast ultrasound dispersion imaging (CUDI) allow improved PCa diagnosis. CEUS has also emerged as an important tool in characterizing suspicious renal mass without compromising renal function with contrast imaging. CEUS has modernized imaging and diagnosis of prostate and renal cancer. Future advancements and utilization of CEUS will allow its expansion into other urological subspecialties.

Medical, social, and economic perspectives of health care development. Prostate Cancer Early Diagnosis and Screening Program

Aim. To improve diagnostics and screening of prostate cancer to ensure early detection of malignant tumors.Materials and methods. We analyzed the population size and structure in the region in 2005–2010. The primary diagnostics of prostate cancer in regional healthcare institutions allowed the detection of locally advanced tumors and generalized cancer. Such diagnostic approaches can produce only accidental diagnosis since they lack proper systematization, organization, and control.Results. We have developed and implemented into clinical practice a long-term Program for Prostate Cancer Early Diagnosis and Screening. It includes a diagnostic algorithm for prostate cancer that was modified for clinical use. Screening starts with the PSA blood test performed annually. Patients with PSA level &gt;10 ng/mL should undergo additional diagnostic procedures. The Program implies active use of currently available information technologies to implement a unified routing scheme for cancer patients, execute measures for early cancer diagnosis, and warrant promptness and reliability of the cancer registry.Conclusion. Implementation of the long-term Program for Prostate Cancer Early Diagnosis and Screening will help to improve prostate cancer diagnosis, and, therefore, treatment outcomes.

The Impact of Lifestyle on Prostate Cancer: A Road to the Discovery of New Biomarkers.

Prostate cancer (PCa) is one of the most common cancers among men, and its incidence has been rising through the years. Several risk factors have been associated with this disease and unhealthy lifestyles and inflammation were appointed as major contributors for PCa development, progression, and severity. Despite the advantages associated with the currently used diagnostic tools [prostate-specific antigen(PSA) serum levels and digital rectal examination (DRE)], the development of effective approaches for PCa diagnosis is still necessary. Finding lifestyle-associated proteins that may predict the development of PCa seems to be a promising strategy to improve PCa diagnosis. In this context, several biomarkers have been identified, including circulating biomarkers (CRP, insulin, C-peptide, TNFα-R2, adiponectin, IL-6, total PSA, free PSA, and p2PSA), urine biomarkers (PCA3, guanidine, phenylacetylglycine, and glycine), proteins expressed in exosomes (afamin, vitamin D-binding protein, and filamin A), and miRNAs expressed in prostate tissue (miRNA-21, miRNA-101, and miRNA-182). In conclusion, exploring the impact of lifestyle and inflammation on PCa development and progression may open doors to the identification of new biomarkers. The discovery of new PCa diagnostic biomarkers should contribute to reduce overdiagnosis and overtreatment.

H-ProSeg: Hybrid ultrasound prostate segmentation based on explainability-guided mathematical model

Background and ObjectiveAccurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for image-guided prostate interventions and prostate cancer diagnosis. However, it remains a challenging task for various reasons, including a missing or ambiguous boundary between the prostate and surrounding tissues, the presence of shadow artifacts, intra-prostate intensity heterogeneity, and anatomical variations. MethodsHere, we present a hybrid method for prostate segmentation (H-ProSeg) in TRUS images, using a small number of radiologist-defined seed points as the prior points. This method consists of three subnetworks. The first subnetwork uses an improved principal curve-based model to obtain data sequences consisting of seed points and their corresponding projection index. The second subnetwork uses an improved differential evolution-based artificial neural network for training to decrease the model error. The third subnetwork uses the parameters of the artificial neural network to explain the smooth mathematical description of the prostate contour. The performance of the H-ProSeg method was assessed in 55 brachytherapy patients using Dice similarity coefficient (DSC), Jaccard similarity coefficient (Ω), and accuracy (ACC) values. ResultsThe H-ProSeg method achieved excellent segmentation accuracy, with DSC, Ω, and ACC values of 95.8%, 94.3%, and 95.4%, respectively. Meanwhile, the DSC, Ω, and ACC values of the proposed method were as high as 93.3%, 91.9%, and 93%, respectively, due to the influence of Gaussian noise (standard deviation of Gaussian function, σ = 50). Although the σ increased from 10 to 50, the DSC, Ω, and ACC values fluctuated by a maximum of approximately 2.5%, demonstrating the excellent robustness of our method. ConclusionsHere, we present a hybrid method for accurate and robust prostate ultrasound image segmentation. The H-ProSeg method achieved superior performance compared with current state-of-the-art techniques. The knowledge of precise boundaries of the prostate is crucial for the conservation of risk structures. The proposed models have the potential to improve prostate cancer diagnosis and therapeutic outcomes.

Clinical Efficacy of SelectMDx for the Detection of Clinically Significant Prostate Cancer

Prostate Cancer (PCa) is the second most frequent cancer diagnosed in men. Elevated serum PSA levels (&gt;4.0 ng/mL) detection is commonly used as a threshold for recommending the tissue biopsy. However, PSA serum levels are not adequate to correctly localize and stage PCa, and to determine its volume and aggressiveness. Therefore, there is a need for healthcare specialists to base the therapeutic decisions on new biomarkers and advanced imaging findings. SelectMDx is a novel non-invasive urine-based assay that measures the expression of two mRNA cancer-related biomarkers (HOXC6 and DLX1) combined with traditional clinical risk factors. Validation studies evidenced the higher precision of SelectMDx urine test to predict PCa. Combining the assay with multi-parametric magnetic resonance imaging (mpMRI) can improve PCa diagnosis and therefore the treatment. Compared to traditional diagnosis strategy, the use of the SelectMDx test in men with elevated PSA levels may lead to a reduction in overdiagnosis and overtreatment. SelectMDx has to be tested in larger and heterogeneous populations in diverse clinical settings to further assess its clinical validity and utility.

Circulating RNAs in prostate cancer patients.

Growing bodies of evidence have demonstrated that the identification of prostate cancer (PCa) biomarkers in the patients' blood and urine may remarkably improve PCa diagnosis and progression monitoring. Among diverse cancer-derived circulating materials, extracellular RNA molecules (exRNAs) represent a compelling component to investigate cancer-related alterations. Once outside the intracellular environment, exRNAs circulate in biofluids either in association with protein complexes or encapsulated inside extracellular vesicles (EVs). Notably, EV-associated RNAs (EV-RNAs) were used for the development of several assays (such as the FDA-approved Progensa Prostate Cancer Antigen 3 (PCA3 test) aiming at improving early PCa detection. EV-RNAs encompass a mixture of species, including small non-coding RNAs (e.g. miRNA and circRNA), lncRNAs and mRNAs. Several methods have been proposed to isolate EVs and relevant RNAs, and to perform RNA-Seq studies to identify potential cancer biomarkers. However, EVs in the circulation of a cancer patient include a multitude of diverse populations that are released by both cancer and normal cells from different tissues, thereby leading to a heterogeneous EV-RNA-associated transcriptional signal. Decrypting the complexity of such a composite signal is nowadays the major challenge faced in the identification of specific tumor-associated RNAs. Multiple deconvolution algorithms have been proposed so far to infer the enrichment of cancer-specific signals from gene expression data. However, novel strategies for EVs sorting and sequencing of RNA associated to single EVs populations will remarkably facilitate the identification of cancer-related molecules. Altogether, the studies summarized here demonstrate the high potential of using EV-RNA biomarkers in PCa and highlight the urgent need of improving technologies and computational approaches to characterize specific EVs populations and their relevant RNA cargo.

The ReIMAGINE Multimodal Warehouse: Using Artificial Intelligence for Accurate Risk Stratification of Prostate Cancer.

Introduction. Prostate cancer (PCa) is the most frequent cancer diagnosis in men worldwide. Our ability to identify those men whose cancer will decrease their lifespan and/or quality of life remains poor. The ReIMAGINE Consortium has been established to improve PCa diagnosis. Materials and methods. MRI will likely become the future cornerstone of the risk-stratification process for men at risk of early prostate cancer. We will, for the first time, be able to combine the underlying molecular changes in PCa with the state-of-the-art imaging. ReIMAGINE Screening invites men for MRI and PSA evaluation. ReIMAGINE Risk includes men at risk of prostate cancer based on MRI, and includes biomarker testing. Results. Baseline clinical information, genomics, blood, urine, fresh prostate tissue samples, digital pathology and radiomics data will be analysed. Data will be de-identified, stored with correlated mpMRI disease endotypes and linked with long term follow-up outcomes in an instance of the Philips Clinical Data Lake, consisting of cloud-based software. The ReIMAGINE platform includes application programming interfaces and a user interface that allows users to browse data, select cohorts, manage users and access rights, query data, and more. Connection to analytics tools such as Python allows statistical and stratification method pipelines to run profiling regression analyses. Discussion. The ReIMAGINE Multimodal Warehouse comprises a unique data source for PCa research, to improve risk stratification for PCa and inform clinical practice. The de-identified dataset characterized by clinical, imaging, genomics and digital pathology PCa patient phenotypes will be a valuable resource for the scientific and medical community.

Validation of Prostate Tissue Composition by Using Hybrid Multidimensional MRI: Correlation with Histologic Findings.

Background Tissue estimates obtained by using microstructure imaging techniques, such as hybrid multidimensional (HM) MRI, may improve prostate cancer diagnosis but require histologic validation. Purpose To validate prostate tissue composition measured by using HM MRI, with quantitative histologic evaluation from whole-mount prostatectomy as the reference standard. Materials and Methods In this HIPAA-compliant study, from December 2016 to July 2018, prospective participants with biopsy-confirmed prostate cancer underwent 3-T MRI before radical prostatectomy. Axial HM MRI was performed with all combinations of echo times (57, 70, 150, and 200 msec) and b values (0, 150, 750, and 1500 sec/mm2). Data were fitted by using a three-compartment signal model to generate volumes for each tissue component (stroma, epithelium, lumen). Quantitative histologic evaluation was performed to calculate volume fractions for each tissue component for regions of interest corresponding to MRI. Tissue composition measured by using HM MRI and quantitative histologic evaluation were compared (paired t test) and correlated (Pearson correlation coefficient), and agreement (concordance correlation) was assessed. Receiver operating characteristic curve analysis for cancer diagnosis was performed. Results Twenty-five participants (mean age, 60 years ± 7 [standard deviation]; 30 cancers and 45 benign regions of interest) were included. Prostate tissue composition measured with HM MRI and quantitative histologic evaluation did not differ (stroma, 45% ± 11 vs 44% ± 11 [P = .23]; epithelium, 31% ± 15 vs 34% ± 15 [P = .08]; and lumen, 24% ± 13 vs 22% ± 11 [P = .80]). Between HM MRI and histologic evaluation, there was excellent correlation (Pearson r: overall, 0.91; stroma, 0.82; epithelium, 0.93; lumen, 0.90 [all P < .05]) and agreement (concordance correlation coefficient: overall, 0.91; stroma, 0.81; epithelium, 0.90; and lumen, 0.87). High areas under the receiver operating characteristic curve obtained with HM MRI (0.96 for epithelium and 0.94 for lumen, P < .001) and histologic evaluation (0.94 for epithelium and 0.88 for lumen, P < .001) were found for differentiation between benign tissue and prostate cancer. Conclusion Tissue composition measured by using hybrid multidimensional MRI had excellent correlation with quantitative histologic evaluation as the reference standard. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Muglia in this issue.

An origami paper-based peptide nucleic acid device coupled with label-free DNAzyme probe hybridization chain reaction for prostate cancer molecular screening test

Prostate cancer associated 3 (PCA3) assay has been used to improve prostate cancer diagnosis and reduce unnecessary biopsies. In this work, we successfully developed a new PCA3 assay on an origami paper-based peptide nucleic acid device (oPAD). The PCA3 oPAD comprises an acrylic cassette and shutter slides to facilitate the molecular reaction and liquid control occurring on the paper surface. To quantify PCA3, a pyrrolidinyl peptide nucleic acid (acpcPNA) was immobilized onto the aldehyde-modified oPAD surface as a selective capture probe. A G-quadruplex (GQD) DNAzyme reporter probe was designed so that the PCA3 gene target binding triggered the hybridization chain reaction of the reporter probe, resulting in the accumulation of the GQD on the oPAD. The peroxidase activity of the GQD-hemin generated a deep green color of the oxidized ABTS substrate. Image analyses were performed in Adobe Photoshop CS6. The proposed oPAD was successfully applied in PCA3 detection ranges of 1–5 μM (r2 = 0.982) with a limit of detection of 0.5 μM. Our proposed oPAD was demonstrated to measure PCA3 samples in both urine matrix and human cancer cell lines. The results reveal the great potential of our origami paper-based platform to be an alternative approach for facile, rapid, and low-cost detection of PCA3 in real samples.

Abstract 862: Transcriptomics analysis reveals unique immunoinflammatory signatures of nigerian prostate cancer

Abstract Background: Prostate cancer (PCa) is the leading cause of cancer death among Nigerian men, with most men presenting late and with high grade disease. Furthermore, improved PCa diagnosis and treatment could rely on genomics-driven precision approaches. Mounting evidence supports potential population enriched molecular features of cancer, however the extent of which is hampered due to a dearth of genomic data in diverse cohorts. This study, therefore, aims at utilizing transcriptomics analysis to deduce transcriptional states in PCa from Nigerian men. Methodology: RNA was extracted from macro-dissected prostate tumor and adjacent normal tissue from 35 archival formalin fixed paraffin-embedded (FFPE) blocks from Gleason 8-9 cancers, of which 25 had cribriform morphologic architecture. High resolution hybrid-capture mRNA-seq libraries were sequenced using Illumina NovaSeq 6000 system and generated an average of 124 million read pairs per sample. Raw reads were processed using a custom pipeline based upon industry standard algorithms along with the deduction of genetic ancestry. Molecular annotation was also performed to investigate tumor and immune cell heterogeneity. Result: The Nigerian data were compared with high grade PCa data of Caucasian obtained from TCGA. The Nigerian PCa tumors where characterized with uniquely enriched immune-inflammatory microenvironment which include crosstalk between dendritic cells and natural killer cells, and natural killer cell signaling. While the Nigerian tumors share more features of distal metastatic tumor, their enrichment in NKT Cells is a unique feature. Conclusion: This represents the first report of RNA-seq analysis of PCa in Sub-Saharan Africa and the unique transcriptomics features of Nigerian PCa presents opportunities for precision intervention in addressing its burden in Nigeria. Citation Format: Solomon Oladapo Rotimi, Abidemi E. Omonisi, Faruk Mohammed, Paul Jibrin, Babatunde Duduyemi, Daniel B. Olusina, Yili Xu, Yuxin Jin, Xiyu Liu, Guang Q. Xiao, Folakemi Odedina, John Carpten, Renee Reams, Zarko Manojlovic. Transcriptomics analysis reveals unique immunoinflammatory signatures of nigerian prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 862.

An update of aberrant methylation detection on circulating cell-free DNA as a tool to improve prostate cancer diagnosis and prognosis

Prostate cancer (PCa) remains a disease of high incidence, but orphan of a specific screening program. For this reason, non-invasive techniques capable to predict PCa in patients with high specificity and sensitivity are still an urgent need. One of the major goals is to improve the PCa diagnosis and the identification of patients who benefit from tissue biopsies. Another need is the necessity to have novel biomarkers to better stratify the risk of patients with PCa to predict the aggressiveness of the tumor and the overall survival. Liquid biopsy can be an important non-invasive tool to stratify PCa at the molecular level to improve diagnosis and prognosis, and, possibly, to develop screening programs and follow-up. With this review, we are reporting the lastest update of aberrant methylation detection on circulating tumor DNA as a tool to improve prostate cancer diagnosis and prognosis.

Incorporating Prognostic Biomarkers into Risk Assessment Models and TNM Staging for Prostate Cancer.

In current practice, prostate cancer staging alone is not sufficient to adequately assess the patient’s prognosis and plan the management strategies. Multiple clinicopathological parameters and risk tools for prostate cancer have been developed over the past decades to better characterize the disease and provide an enhanced assessment of prognosis. Herein, we review novel prognostic biomarkers and their integration into risk assessment models for prostate cancer focusing on their capability to help avoid unnecessary imaging studies, biopsies and diagnosis of low risk prostate cancers, to help in the decision-making process between active surveillance and treatment intervention, and to predict recurrence after radical prostatectomy. There is an imperative need of reliable biomarkers to stratify prostate cancer patients that may benefit from different management approaches. The integration of biomarkers panel with risk assessment models appears to improve prostate cancer diagnosis and management. However, integration of novel genomic biomarkers in future prognostic models requires further validation in their clinical efficacy, standardization, and cost-effectiveness in routine application.