Classification Of Prostate Cancer Research Articles

Augmented Radiology: Feature Space Transfer Model for Prostate Cancer Stage Prediction

Recent improvements in medical image analysis using deep learning-based neural networks can potentially be exploited to enhance the performance of computer-aided detection/diagnosis systems. In this study, we propose a feature space transfer mode (FSTM) for learning the phenotype relationships between radiological images and pathological images. We hypothesize that high-level features from the same patient can be linked between different modality images with different resolutions. We refer to our method as “augmented radiology” because the inference model only requires radiological images as input while the prediction result can be linked to specific pathological phenotypes. We applied the proposed method to the pathological tumor classification (T0 vs. T2c/T3a and T0 vs. T2c vs. T3a) of prostate cancer and found that it achieved a high classification accuracy (0.880 for T0 vs. T2c/T3a and 0.825 for T0 vs. T2c vs. T3a) given only the radiological images as input. We also analyzed the validity of the proposed method by visualizing the transferred features and found that it can extract useful information for diagnosis embedded in radiological images. We conclude that the proposed method will significantly help improve the diagnostic prediction performance of radiological images.

Semi-Automatic Classification of Prostate Cancer from Multi-Parametric MR Imaging Using Novel Image Sequences and 3D Convolutional Neural Networks

Semi-Automatic Classification of Prostate Cancer from Multi-Parametric MR Imaging Using Novel Image Sequences and 3D Convolutional Neural Networks

Classification of Prostate Cancer and Determination of Related Factors with Different Artificial Neural Network

Objective: In this study, it is aimed to classify prostate cancer, compare the predictions of these two models and determine the factors associated with the disease by applying Multilayer Perceptron Neural Network (MLPNN) and Radial-Based Function Neural Network (RBFNN) methods on the open access Prostate cancer dataset.Methods: In this study, the dataset named "Prostate Cancer Data Set" was used by obtaining from https://www.kaggle.com/sajidsaifi/prostate-cancer address. To classify prostate cancer, MLPNN and RBFNN methods, which are artificial neural network models, is used. The classification performance of the models was evaluated with the sensitivity, specificity, accuracy, negative predictive value and positive predictive value, which are among the classification performance metrics. Prostate cancer related factors were estimated by using MLPNN and RBFNN models.Results: With the applied MLPNN model, performance metric values were obtained as AUC 0.937, Sensitivity 100%, accuracy 92.5%, Selectivity 84.6%, Positive predictive value 87.5% and Negative predictive value 100%. With the RBFNN model, the performance metric values were obtained as AUC 0.921, Sensitivity 83.3%, accuracy 86.6%, Selectivity 91.6%, Positive predictive value 93.7% and Negative predictive value 78.5%. When the effects of variables in the dataset in this study on prostate cancer are examined; The three most important variables for the MLPNN model were obtained as perimeter, area and compactness, respectively. For the RBFNN model, the three most important variables were obtained as perimeter, area and compactness, respectively.Conclusion: It was seen that MLPNN and RBFNN models used in this study gave successful predictions in the classification of prostate cancer. In addition, estimating the significance values of factors associated with the disease with these classification models made it different from similar studies with the same dataset.

Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

PurposeRisk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning.MethodsFifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses.ResultsThe area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively.ConclusionOur results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

Selecting proper combination of mpMRI sequences for prostate cancer classification using multi-input convolutional neuronal network.

This study aims to develop a deep-learning-basedmethod to classify clinically significant (CS) and clinically insignificant (CiS) prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI) automatically, and to select suitable mpMRI sequences for PCa classificationin different anatomic zones. A multi-input selection network (MISN) is proposed for both PCa classification and theselection of the optimal combination of sequences for PCa classification in a specific zone.MISN is a multi-input/-output classification network consisting of nine branches to process nine input images from the mpMRI data. To improve classification accuracy and reduce model parameters, a pruning strategy is proposed to select a subset of the nine branches of MIST to form two more effective networks for the peripheral zone (PZ) PCa and transition zone (TZ) PCa, which are named as PZN and TZN, respectively. Besides, a new penalized cross-entropy loss function is adopted to train the networks tobalance the classification sensitivity and specificity. The proposed methods were evaluated on the PROSTATEx challenge dataset and achieved an area under the receiver operator characteristics curve of 0.95, which was much higher than currently published results and ranked first out of more than 1500 entries submitted to the challenge at the time of submission of this paper. For PZ-PCa and TZ-PCa classification, PZN and TZN achieved better performance than MISN. Higher performance can be achieved by selecting a suitable subset of the mpMRI sequences in PCa classification.

Improving prostate cancer classification in H&E tissue micro arrays using Ki67 and P63 histopathology

Histopathology of Hematoxylin and Eosin (H&E)-stained tissue obtained from biopsy is commonly used in prostate cancer (PCa) diagnosis. Automatic PCa classification of digitized H&E slides has been developed before, but no attempts have been made to classify PCa using additional tissue stains registered to H&E. In this paper, we demonstrate that using H&E, Ki67 and p63-stained (3-stain) tissue improves PCa classification relative to H&E alone. We also show that we can infer PCa-relevant Ki67 and p63 information from the H&E slides alone, and use it to achieve H&E-based PCa classification that is comparable to the 3-stain classification. Reported improvements apply to classifying benign vs. malignant tissue, and low grade (Gleason group 2) vs. high grade (Gleason groups 3,4,5) cancer. Specifically, we conducted four classification tasks using 333 tissue samples extracted from 231 radical prostatectomy patients: regression tree-based classification using either (i) 3-stain features, with a benign vs malignant area under the curve (AUC = 92.9%), or (ii) real H&E features and H&E features learned from Ki67 and p63 stains (AUC = 92.4%), as well as deep learning classification using either (iii) real 3-stain tissue patches (AUC = 94.3%) and (iv) real H&E patches and generated Ki67 and p63 patches (AUC = 93.0%) using a deep convolutional generative adversarial network. Classification performance was assessed with Monte Carlo cross validation and quantified in terms of the Area Under the Curve, Brier score, sensitivity, and specificity. Our results are interpretable and indicate that the standard H&E classification could be improved by mimicking other stain types.

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer

Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists.

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.

Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding the application of machine learning with emphasis on modern efficient convolutional neural networks (CNNs) algorithms, for the diagnosis of prostate cancer metastasis from bone scintigraphy images. The advantageous and outstanding capabilities of deep learning, machine learning's groundbreaking technological advancement, have not yet been fully investigated regarding their application in computer-aided diagnosis systems in the field of medical image analysis, such as the problem of bone metastasis classification in whole-body scans. In particular, CNNs are gaining great attention due to their ability to recognize complex visual patterns, in the same way as human perception operates. Considering all these new enhancements in the field of deep learning, a set of simpler, faster and more accurate CNN architectures, designed for classification of metastatic prostate cancer in bones, is explored. This research study has a two-fold goal: to create and also demonstrate a set of simple but robust CNN models for automatic classification of whole-body scans in two categories, malignant (bone metastasis) or healthy, using solely the scans at the input level. Through a meticulous exploration of CNN hyper-parameter selection and fine-tuning, the best architecture is selected with respect to classification accuracy. Thus a CNN model with improved classification capabilities for bone metastasis diagnosis is produced, using bone scans from prostate cancer patients. The achieved classification testing accuracy is 97.38%, whereas the average sensitivity is approximately 95.8%. Finally, the best-performing CNN method is compared to other popular and well-known CNN architectures used for medical imaging, like VGG16, ResNet50, GoogleNet and MobileNet. The classification results show that the proposed CNN-based approach outperforms the popular CNN methods in nuclear medicine for metastatic prostate cancer diagnosis in bones.

Immunogenomic Profiling and Classification of Prostate Cancer Based on HIF-1 Signaling Pathway.

The HIF-1 signaling pathway plays an important role in the pathogenesis of cancer. Many studies have explored the progression of prostate cancer (PCa) under hypoxic conditions based on transcriptome data; few have uncovered the immunogenomic profiling and prostate cancer classification based on the HIF-1 signaling pathway. This pathway may help to identify the optimal subset of PCa patients responsive to immunotherapy/chemotherapy. The immunogenomic PCa subsets were classified based on profiling of the HIF-1 signaling pathway, using four publicly available PCa datasets. Three PCa subtypes that named as HIF-1 High (HIF-1_H), HIF-1 Medium (HIF-1_M), and HIF-1 Low (HIF-1_L) were identified. Functional enrichment was analyzed in each subtype. Several cancer-associated and immune-related pathways were hyperactivated in the HIF-1_H subtypes. In contrast, HIF-1_L subtypes were enriched in cell cycle and cell repair. Compared with other subtypes, HIF-1_H subtypes have greater immune cell infiltration, anti-tumor immune activity, and better survival prognosis. The submap and TIDE algorithm were used to predict the clinical response to immune checkpoint blockade, and GDSC was employed to screen potential chemotherapeutic targets for the treatment of PCa. Several chemotherapy drugs were identified in the GDSC dataset, including ABT 888, Temsirolimus, and EHT 1864. Meanwhile, HIF-1_H was defined as an early PCa marker, which is more likely to respond to immunotherapy. The identification of immunogenomic PCa subtypes based on the HIF-1 signaling pathway has potential clinical implications for PCa treatment. Immunopositive PCa subtypes will help to explore the reasons for the poor response of PCa to immunotherapy, and it is expected that immunotherapy will guide the personalized treatment of PCa patients.

Distinct Metabolic Signatures of Hormone-Sensitive and Castration-Resistant Prostate Cancer Revealed by a 1H NMR-Based Metabolomics of Biopsy Tissue.

Tumor metabolic characteristics have been associated with the progression of prostate cancer (PCa), but little information is available regarding the metabolic alterations from hormone-sensitive (HSPC) to castration-resistant PCa (CRPC). In this study, therefore, we investigated the metabolic profiles in prostate tissues from patients with benign prostatic hyperplasia (BPH), HSPC, and CRPC using a 1H NMR-based metabolomics approach. The results show that clear separations in metabolic patterns were obtained in prostate tissues among BPH, HSPC, and CRPC; however, CRPC may induce a metabolic shift toward BPH, mainly involving amino acid metabolism, choline metabolism, and the Warburg effect. Based on these metabolic changes, we identified potential biomarker panels for the discrimination between BPH vs HSPC, BPH vs CRPC, and HSPC vs CRPC with the AUC values of 0.995, 0.972, and 0.937, respectively. Collectively, tissue-based metabolomics analysis not only identifies the altered metabolic pathways during PCa progression but also has the potential to help the classification and diagnosis of PCa in clinical practice.

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters.

Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

48 - Autonomous detection and classification of prostate cancer in a MRI screening population - incorporating multicenter-labeled deep learning and biparametric imaging

48 - Autonomous detection and classification of prostate cancer in a MRI screening population - incorporating multicenter-labeled deep learning and biparametric imaging

Review of the Role of Radiomics in Tumour Risk Classification and Prognosis of Cancer.

Radiomics, an emerging field in radiation therapy, is hypothesized to improve classification of tumour risk and prognosis. Despite encouraging results, there are issues of practicality and interpretation of radiomic data. This study investigates the emerging role of radiomics in tumour risk classification and prognosis of breast and prostate cancer. A literature search was conducted using predefined terms to retrieve studies related to radiomics. Studies were evaluated and selected upon meeting the criteria defined. A total of 19 relevant publications were selected from 63 publications identified. Data from studies revealed significant area under the curve (AUC) values and high discriminative power. Significant AUC values for biochemical recurrence of disease and disease-free survival were reported for prognosis. Radiomics show promising potential in discriminating tumour risk and predicting prognosis of cancer using specified features. It is an alternative to conventional predictive tools and has the ability to improve with the use of existing tools.

High mortality risk of prostate cancer patients in Asia and West Africa: A systematic review.

ABSTRACTGlobally, prostate cancer (PCa) is the second most preponderant cancer in men. It contributes to the high mortality-to-incidence ratio reported in West Africa and Asia largely due to low screening. The mortality risk is determined or predicted based on the prevalence of high-risk or aggressive PCa using a scoring or grading system such as Gleason score (GS), Gleason grade (GG), and prostate-specific antigen (PSA) level. In this review, peer-reviewed articles found on databases such as Google Scholar, Scopus, Web of Science, PubMed Central and, EMBASE were selected based on adherence to clinical guidelines for the classification of PCa. In West Africa and Asia, the result revealed that the frequency of high-risk PCa was 42% and 51.2% based on GS, 48.8% and 25.3% based on GG pattern, and 87.5% and 44.3% based on PSA level >10 ng/mL, respectively. Data revealed a high prevalence of high-risk PCa both in West Africa and Asia when compared with developed countries. However, the prevalence of high-risk PCa is higher in West Africa than in Asia. Studies have shown that high-risk PCas are associated with germline mutations and such mutations are prevalent in blacks and Asians than in whites. Thus, testing for germline mutations in patients with GS of ≥ 7, GG ≥ 3, high prostate density, low prostate volume, and PSA levels of >4.0 ng/mL may identify those at risk of developing lethal PCa and could reduce the mortality rates in Asia and West Africa.

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications.

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. Results: From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77–0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. Conclusions: To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.

Abstract B045: Exosomal microRNAs are associated with prostate cancer aggressiveness in African American patients

Abstract Background: The morbidity and mortality rates of prostate cancer (PCa) in African American (AA) are 2-3 times higher than European American (EuA) men. The molecular mechanisms underlying the aggressiveness of PCa have not fully identified. Thus, our aim was to evaluate the diagnostic/prognostic utility of exosomal microRNAs (miRs) to classify PCa patients according their race and aggressive phenotype in AA patients. Their functional role in tumor aggressiveness was also determined. Methods: Exosomes were isolated from the conditioned media of AA and EuA PCa cell lines. The expression of miRs was validated in exosomes, free-circulating plasma, and FFPE tissue specimens of forty AA and EuA patients using quantitative real-time PCR analysis. The sensitivity and specificity of exosomal miRs to classify prostate cancer patients according their race and aggressiveness were assessed using receiver operating characteristic (ROC) curve analysis. To study the functional significance of exosomal miRs, cell proliferation, clonogenic, cell cycle and migration assays were performed in PCa cells transfected with miR-3128. Results: Differential expression of exosomal miR-3613-3p, miR-3218, miR-3679, and miR-3680 was demonstrated in the plasma of AA versus EuA of PCa patients. While exosomal miR-3613 and miR-3679 (p<0.05) were upregulated, free-circulating miRs downregulated (p<0.05) in the plasma of AA versus EuA patients. The accuracy of miR-3679 to discriminate AA from EuA was improved when combined with the other three miRs (AUC jumped from 0.717 to 0.897). Intriguingly, miR-3128 showed a dual role in AA versus EuA cells. Overexpression of miR-3128 increased the cell growth in AA cells while it did the opposite in EuA cells. These data were recapitulated by migration, cell cycle and clonogenic assays. Conclusion: Our findings underline the role of exosomal miRs in health disparity of PCa. The differential expression of miRs in AA men demonstrates their reliability as biomarkers and their potential role in promoting tumor aggressiveness in AA men. Citation Format: Hamdy E.A. Ali, Rofaida Gaballa, Andrew S. Sholl, Mohamed Gaballah, Juan J. Bustamante, Preeti Zanwar, Hamed I. Ali, Zakaria Y. Abd Elmageed. Exosomal microRNAs are associated with prostate cancer aggressiveness in African American patients [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B045.

High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach.

To assess if adding perfusion information from dynamic contrast-enhanced (DCE MRI) acquisition schemes with high spatiotemporal resolution to T2w/DWI sequences as input features for a gradient boosting machine (GBM) machine learning (ML) classifier could better classify prostate cancer (PCa) risk groups than T2w/DWI sequences alone. One hundred ninety patients (68 ± 9years) were retrospectively evaluated at 3T MRI for clinical suspicion of PCa. Included were 201 peripheral zone (PZ) PCa lesions. Histopathological confirmation on fusion biopsy was matched with normal prostate parenchyma contralaterally. Biopsy results were grouped into benign tissue and low-, intermediate-, and high-risk groups (Gleason sum score 6, 7, and > 7, respectively). DCE MRI was performed using golden-angle radial sparse MRI. Perfusion maps (Ktrans, Kep, Ve), apparent diffusion coefficient (ADC), and absolute T2w signal intensity were determined and used as input features for building two ML models: GBM with/without perfusion maps. Areas under the receiver operating characteristic curve (AUC) values for correlated models were compared. For the classification of benign vs. malignant and intermediate- vs. high-grade PCa, perfusion information added relevant information (AUC values 1 vs. 0.953 and 0.909 vs. 0.700, p < 0.001 and p = 0.038), while no statistically significant effect was found for low- vs. intermediate- and high-grade PCa. Perfusion information from DCE MRI acquisition schemes with high spatiotemporal resolution to ML classifiers enables a superior risk stratification between benign and malignant and intermediate- and high-risk PCa in the PZ compared with classifiers based on T2w/DWI information alone. • In the recent guidelines, the role of DCE MRI has changed from a mandatory to recommended sequence. • DCE MRI acquisition schemes with high spatiotemporal resolution (e.g., GRASP) have been shown to improve the diagnostic performance compared with conventional DCE MRI sequences. • Using perfusion information acquired with GRASP in combination with ML classifiers significantly improved the prediction of benign vs. malignant and intermediate- vs. high-grade peripheral zone prostate cancer compared with non-contrast sequences.

Improving Prostate Cancer (PCa) Classification Performance by Using Three-Player Minimax Game to Reduce Data Source Heterogeneity.

PCa is a disease with a wide range of tissue patterns and this adds to its classification difficulty. Moreover, the data source heterogeneity, i.e. inconsistent data collected using different machines, under different conditions, by different operators, from patients of different ethnic groups, etc., further hinders the effectiveness of training a generalized PCa classifier. In this paper, for the first time, a Generative Adversarial Network (GAN)-based three-player minimax game framework is used to tackle data source heterogeneity and to improve PCa classification performance, where a proposed modified U-Net is used as the encoder. Our dataset consists of novel high-frequency ExactVu ultrasound (US) data collected from 693 patients at five data centers. Gleason Scores (GSs) are assigned to the 12 prostatic regions of each patient. Two classification tasks: benign vs. malignant and low- vs. high-grade, are conducted and the classification results of different prostatic regions are compared. For benign vs. malignant classification, the three-player minimax game framework achieves an Area Under the Receiver Operating Characteristic (AUC) of 93.4%, a sensitivity of 95.1% and a specificity of 87.7%, respectively, representing significant improvements of 5.0%, 3.9%, and 6.0% compared to those of using heterogeneous data, which confirms its effectiveness in terms of PCa classification.

A TV-based image processing framework for blind color deconvolution and classification of histological images

In digital histopathological image analysis, two conflicting objectives are often pursued: closeness to the original tissue and high classification performance. The former objective tries to recover images (stains) that are as close as possible to the ones obtained by staining the tissue with a single dye. The latter objective requires images that allow the extraction of better features for an improved classification, even if their appearance is not close to single stained tissues. In this paper we propose a framework that achieves both objectives depending on the number of stains used to mathematically decompose the scanned image. The proposed framework uses a total variation prior for each stain together with the similarity to a given reference color-vector matrix. Variational inference and an evidence lower bound are utilized to automatically estimate all the latent variables and model parameters. The proposed methodology is tested on real images and compared to classical and state-of-the-art methods for histopathological blind image color deconvolution and prostate cancer classification.

Expression of Small Noncoding RNAs in Urinary Exosomes Classifies Prostate Cancer into Indolent and Aggressive Disease.

This is the first report of the development and performance of a platform that interrogates small noncoding RNAs (sncRNA) isolated from urinary exosomes. The Sentinel™ PCa Test classifies patients with prostate cancer from subjects with no evidence of prostate cancer, the miR Sentinel CS Test stratifies patients with prostate cancer between those with low risk prostate cancer (Grade Group 1) from those with intermediate and high risk disease (Grade Group 2-5), and the miR Sentinel HG Test stratifies patients with prostate cancer between those with low and favorable intermediate risk prostate cancer (Grade Group 1 or 2) and those with high risk (Grade Group 3-5) disease. sncRNAs were extracted from urinary exosomes of 235 participants and interrogated on miR 4.0 microarrays. Using proprietary selection and classification algorithms, informative sncRNAs were selected to customize an interrogation OpenArray™ platform that forms the basis of the tests. The tests were validated using a case-control sample of 1,436 subjects. The performance of the miR Sentinel PCa Test demonstrated a sensitivity of 94% and specificity of 92%. The Sentinel CS Test demonstrated a sensitivity of 93% and specificity of 90% for prediction of the presence of Grade Group 2 or greater cancer, and the Sentinel HG Test demonstrated a sensitivity of 94% and specificity of 96% for the prediction of the presence of Grade Group 3 or greater cancer. The Sentinel PCa, CS and HG Tests demonstrated high levels of sensitivity and specificity, highlighting the utility of interrogation of urinary exosomal sncRNAs for noninvasively diagnosing and classifying prostate cancer with high precision.